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Jin X, Dong S, Yang Y, Bao G, Ma H. Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study. J Affect Disord 2024; 358:129-137. [PMID: 38697224 DOI: 10.1016/j.jad.2024.04.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/04/2024] [Accepted: 04/21/2024] [Indexed: 05/04/2024]
Abstract
BACKGROUND The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment. METHODS We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies. RESULTS We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes. LIMITATIONS The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects. CONCLUSION The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.
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Affiliation(s)
- Xing Jin
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
| | - Shuangshuang Dong
- Department of Neurology, General Hospital of Southern Theatre Command, Guangzhou, Guangdong, China
| | - Yang Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
| | - Guangyu Bao
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
| | - Haochuan Ma
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China; Guangdong Provincial Hospital of Chinese Medicine Postdoctoral Research Workstation, Guangzhou, Guangdong, China.
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2
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Pang T, Ding N, Zhao Y, Zhao J, Yang L, Chang S. Novel genetic loci of inhibitory control in ADHD and healthy children and genetic correlations with ADHD. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110988. [PMID: 38430954 DOI: 10.1016/j.pnpbp.2024.110988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/26/2023] [Accepted: 02/28/2024] [Indexed: 03/05/2024]
Abstract
Cumulative evidence has showed the deficits of inhibitory control in patients with attention deficit hyperactivity disorder (ADHD), which is considered as an endophenotype of ADHD. Genetic study of inhibitory control could advance gene discovery and further facilitate the understanding of ADHD genetic basis, but the studies were limited in both the general population and ADHD patients. To reveal genetic risk variants of inhibitory control and its potential genetic relationship with ADHD, we conducted genome-wide association studies (GWAS) on inhibitory control using three datasets, which included 783 and 957 ADHD patients and 1350 healthy children. Subsequently, we employed polygenic risk scores (PRS) to explore the association of inhibitory control with ADHD and related psychiatric disorders. Firstly, we identified three significant loci for inhibitory control in the healthy dataset, two loci in the case dataset, and one locus in the meta-analysis of three datasets. Besides, we found more risk genes and variants by applying transcriptome-wide association study (TWAS) and conditional FDR method. Then, we constructed a network by connecting the genes identified in our study, leading to the identification of several vital genes. Lastly, we identified a potential relationship between inhibitory control and ADHD and autism by PRS analysis and found the direct and mediated contribution of the identified genetic loci on ADHD symptoms by mediation analysis. In conclusion, we revealed some genetic risk variants associated with inhibitory control and elucidated the benefit of inhibitory control as an endophenotype, providing valuable insights into the mechanisms underlying ADHD.
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Affiliation(s)
- Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Ning Ding
- School of Psychology, Shaanxi Normal University and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Yilu Zhao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jingjing Zhao
- School of Psychology, Shaanxi Normal University and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China.
| | - Li Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China.
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Peking University, Beijing 100191, China.
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3
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Piras IS, DiStefano JK. Comprehensive meta-analysis reveals distinct gene expression signatures of MASLD progression. Life Sci Alliance 2024; 7:e202302517. [PMID: 38565287 PMCID: PMC10987979 DOI: 10.26508/lsa.202302517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, metabolic dysfunction-associated steatohepatitis (MASH), pose significant risks of severe fibrosis, cirrhosis, and hepatocellular carcinoma. Despite their widespread prevalence, the molecular mechanisms underlying the development and progression of these common chronic hepatic conditions are not fully understood. Here, we conducted the most extensive meta-analysis of hepatic gene expression datasets from liver biopsy samples to date, integrating 10 RNA-sequencing and microarray datasets (1,058 samples). Using a random-effects meta-analysis model, we compared over 12,000 shared genes across datasets. We identified 685 genes differentially expressed in MASLD versus normal liver, 1,870 in MASH versus normal liver, and 3,284 in MASLD versus MASH. Integrating these results with genome-wide association studies and coexpression networks, we identified two functionally relevant, validated coexpression modules mainly driven by SMOC2, ITGBL1, LOXL1, MGP, SOD3, and TAT, HGD, SLC25A15, respectively, the latter not previously associated with MASLD and MASH. Our findings provide a comprehensive and robust analysis of hepatic gene expression alterations associated with MASLD and MASH and identify novel key drivers of MASLD progression.
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Affiliation(s)
- Ignazio S Piras
- https://ror.org/02hfpnk21 Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Johanna K DiStefano
- https://ror.org/02hfpnk21 Diabetes and Metabolic Disease Research Unit, Translational Genomics Research Institute, Phoenix, AZ, USA
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4
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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5
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Yuan X, Jiang X, Zhang M, Wang L, Jiao W, Chen H, Mao J, Ye W, Song Q. Integrative omics analysis elucidates the genetic basis underlying seed weight and oil content in soybean. THE PLANT CELL 2024; 36:2160-2175. [PMID: 38412459 DOI: 10.1093/plcell/koae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 02/29/2024]
Abstract
Synergistic optimization of key agronomic traits by traditional breeding has dramatically enhanced crop productivity in the past decades. However, the genetic basis underlying coordinated regulation of yield- and quality-related traits remains poorly understood. Here, we dissected the genetic architectures of seed weight and oil content by combining genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) using 421 soybean (Glycine max) accessions. We identified 26 and 33 genetic loci significantly associated with seed weight and oil content by GWAS, respectively, and detected 5,276 expression quantitative trait loci (eQTLs) regulating expression of 3,347 genes based on population transcriptomes. Interestingly, a gene module (IC79), regulated by two eQTL hotspots, exhibited significant correlation with both seed weigh and oil content. Twenty-two candidate causal genes for seed traits were further prioritized by TWAS, including Regulator of Weight and Oil of Seed 1 (GmRWOS1), which encodes a sodium pump protein. GmRWOS1 was verified to pleiotropically regulate seed weight and oil content by gene knockout and overexpression. Notably, allelic variations of GmRWOS1 were strongly selected during domestication of soybean. This study uncovers the genetic basis and network underlying regulation of seed weight and oil content in soybean and provides a valuable resource for improving soybean yield and quality by molecular breeding.
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Affiliation(s)
- Xiaobo Yuan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Xinyu Jiang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Mengzhu Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Longfei Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Wu Jiao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Huatao Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling, Nanjing, Jiangsu 210014, China
| | - Junrong Mao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Wenxue Ye
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Qingxin Song
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
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6
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Yan D, Hu B, Darst BF, Mukherjee S, Kunkle BW, Deming Y, Dumitrescu L, Wang Y, Naj A, Kuzma A, Zhao Y, Kang H, Johnson SC, Carlos C, Hohman TJ, Crane PK, Engelman CD, Lu Q. Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife 2024; 12:RP91360. [PMID: 38787369 PMCID: PMC11126309 DOI: 10.7554/elife.91360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
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Affiliation(s)
- Donghui Yan
- University of Wisconsin-MadisonMadisonUnited States
| | - Bowen Hu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Burcu F Darst
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Shubhabrata Mukherjee
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Brian W Kunkle
- University of Miami Miller School of MedicineMiamiUnited States
| | - Yuetiva Deming
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Yunling Wang
- University of Wisconsin-MadisonMadisonUnited States
| | - Adam Naj
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Amanda Kuzma
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Yi Zhao
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA HospitalMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | - Cruchaga Carlos
- Department of Psychiatry, Washington University in St. LouisSt. LouisUnited States
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Paul K Crane
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | | | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-MadisonMadisonUnited States
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7
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Keener R, Chhetri SB, Connelly CJ, Taub MA, Conomos MP, Weinstock J, Ni B, Strober B, Aslibekyan S, Auer PL, Barwick L, Becker LC, Blangero J, Bleecker ER, Brody JA, Cade BE, Celedon JC, Chang YC, Cupples LA, Custer B, Freedman BI, Gladwin MT, Heckbert SR, Hou L, Irvin MR, Isasi CR, Johnsen JM, Kenny EE, Kooperberg C, Minster RL, Naseri T, Viali S, Nekhai S, Pankratz N, Peyser PA, Taylor KD, Telen MJ, Wu B, Yanek LR, Yang IV, Albert C, Arnett DK, Ashley-Koch AE, Barnes KC, Bis JC, Blackwell TW, Boerwinkle E, Burchard EG, Carson AP, Chen Z, Chen YDI, Darbar D, de Andrade M, Ellinor PT, Fornage M, Gelb BD, Gilliland FD, He J, Islam T, Kaab S, Kardia SLR, Kelly S, Konkle BA, Kumar R, Loos RJF, Martinez FD, McGarvey ST, Meyers DA, Mitchell BD, Montgomery CG, North KE, Palmer ND, Peralta JM, Raby BA, Redline S, Rich SS, Roden D, Rotter JI, Ruczinski I, Schwartz D, Sciurba F, Shoemaker MB, Silverman EK, Sinner MF, Smith NL, Smith AV, Tiwari HK, Vasan RS, Weiss ST, Williams LK, Zhang Y, Ziv E, Raffield LM, Reiner AP, Arvanitis M, Greider CW, Mathias RA, Battle A. Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes. Nat Commun 2024; 15:4417. [PMID: 38789417 PMCID: PMC11126610 DOI: 10.1038/s41467-024-48394-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.
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Grants
- 5K12GM123914 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01AG069120 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R35GM139580 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01 DK071891 NIDDK NIH HHS
- R01HL153805 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01AG081244 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R35CA209974 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01HL105756 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL68959 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL079915 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL87681 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01 HL153805 NHLBI NIH HHS
- R01HL-120393 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
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Affiliation(s)
- Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Carla J Connelly
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA
| | - Margaret A Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matthew P Conomos
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Joshua Weinstock
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Benjamin Strober
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | | | - Paul L Auer
- Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Lucas Barwick
- LTRC Data Coordinating Center, The Emmes Company, LLC, Rockville, MD, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eugene R Bleecker
- Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Juan C Celedon
- Division of Pediatric Pulmonary Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yi-Cheng Chang
- Department of Internal Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Barry I Freedman
- Internal Medicine - Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Mark T Gladwin
- School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama Birmingham, Birmingham, AL, USA
| | - Carmen R Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jill M Johnsen
- Department of Medicine and Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Eimear E Kenny
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ryan L Minster
- Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Take Naseri
- Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa
- International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | - Satupa'itea Viali
- Oceania University of Medicine, Apia, Samoa
- School of Medicine, National University of Samoa, Apia, Samoa
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
| | - Sergei Nekhai
- Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington DC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marilyn J Telen
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ivana V Yang
- Departments of Biomedical Informatics, Medicine, and Epidemiology, University of Colorado, Boulder, CO, USA
| | - Christine Albert
- Harvard Medical School, Boston, MA, USA
- Division of Cardiovascular, Brigham and Women's Hospital, Boston, MA, USA
| | - Donna K Arnett
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | | | - Kathleen C Barnes
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MI, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Dawood Darbar
- Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bruce D Gelb
- Mindich Child Health and Development Institute and Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Jiang He
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Talat Islam
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Stefan Kaab
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Shannon Kelly
- Vitalant Research Institute, San Francisco, CA, USA
- University of California San Francisco Benioff Children's Hospital, Oakland, CA, USA
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Rajesh Kumar
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- The Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fernando D Martinez
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Stephen T McGarvey
- Department of Epidemiology & International Health Institute, Brown University School of Public Health, Providence, RI, USA
| | - Deborah A Meyers
- Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney G Montgomery
- Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Benjamin A Raby
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Susan Redline
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dan Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David Schwartz
- Departments of Medicine and Immunology, University of Colorado, Boulder, CO, USA
| | - Frank Sciurba
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Benjamin Shoemaker
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Moritz F Sinner
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
| | - Nicholas L Smith
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama Birmingham, Birmingham, AL, USA
| | | | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Yingze Zhang
- Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elad Ziv
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Marios Arvanitis
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA
| | - Carol W Greider
- Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA
- University Professor Johns Hopkins University, Baltimore, MD, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
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8
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Wen C, Margolis M, Dai R, Zhang P, Przytycki PF, Vo DD, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H, Peters MA, Gerstein M, Daskalakis NP, Weng Z, Jaffe AE, Kleinman JE, Hyde TM, Weinberger DR, Bray NJ, Sestan N, Geschwind DH, Roeder K, Gusev A, Pasaniuc B, Stein JL, Love MI, Pollard KS, Liu C, Gandal MJ. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024; 384:eadh0829. [PMID: 38781368 DOI: 10.1126/science.adh0829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/07/2024] [Indexed: 05/25/2024]
Abstract
Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.
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Affiliation(s)
- Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Margolis
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Pan Zhang
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pawel F Przytycki
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
| | - Daniel D Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Miao Tang
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Chuan Jiao
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Krebs, 75014 Paris, France
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ellen Tsai
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Celine Hoh
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca L Walker
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christos Chatzinakos
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Declan Clarke
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Henry Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Mette A Peters
- CNS Data Coordination Group, Sage Bionetworks, Seattle, WA 98109, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nikolaos P Daskalakis
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- McLean Hospital, Belmont, MA 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Neumora Therapeutics, Watertown, MA 02472, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK
| | - Nenad Sestan
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Alexander Gusev
- Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Medical School, Boston, MA 02215, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
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9
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Duarte RRR, Pain O, Bendall ML, de Mulder Rougvie M, Marston JL, Selvackadunco S, Troakes C, Leung SK, Bamford RA, Mill J, O'Reilly PF, Srivastava DP, Nixon DF, Powell TR. Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions. Nat Commun 2024; 15:3803. [PMID: 38778015 PMCID: PMC11111684 DOI: 10.1038/s41467-024-48153-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.
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Affiliation(s)
- Rodrigo R R Duarte
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Division of Infectious Diseases, Weill Cornell Medicine, Cornell University, New York, NY, USA.
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matthew L Bendall
- Division of Infectious Diseases, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | | | - Jez L Marston
- Division of Infectious Diseases, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Sashika Selvackadunco
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC London Neurodegenerative Diseases Brain Bank, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Claire Troakes
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC London Neurodegenerative Diseases Brain Bank, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Szi Kay Leung
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Rosemary A Bamford
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, USA
| | - Deepak P Srivastava
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Douglas F Nixon
- Division of Infectious Diseases, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Timothy R Powell
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Division of Infectious Diseases, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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10
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Cao Z, Li Q, Li Y, Wu J. Identification of plasma protein markers of allergic disease risk: a mendelian randomization approach to proteomic analysis. BMC Genomics 2024; 25:503. [PMID: 38773393 PMCID: PMC11110418 DOI: 10.1186/s12864-024-10412-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/15/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND While numerous allergy-related biomarkers and targeted treatment strategies have been developed and employed, there are still signifcant limitations and challenges in the early diagnosis and targeted treatment for allegic diseases. Our study aims to identify circulating proteins causally associated with allergic disease-related traits through Mendelian randomization (MR)-based analytical framework. METHODS Large-scale cis-MR was employed to estimate the effects of thousands of plasma proteins on five main allergic diseases. Additional analyses including MR Steiger analyzing and Bayesian colocalisation, were performed to test the robustness of the associations; These findings were further validated utilizing meta-analytical methods in the replication analysis. Both proteome- and transcriptome-wide association studies approach was applied, and then, a protein-protein interaction was conducted to examine the interplay between the identified proteins and the targets of existing medications. RESULTS Eleven plasma proteins were identified with links to atopic asthma (AA), atopic dermatitis (AD), and allergic rhinitis (AR). Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. MR analysis and extra validation revealed STAT6 and TNFRSF6B to be Tier 1 and IL1RL2 and IL6R to be Tier 2 proteins with the potential for AA treatment. Two Tier 1 proteins, CRAT and TNFRSF6B, and five Tier 2 proteins, ERBB3, IL6R, MMP12, ICAM1, and IL1RL2, were linked to AD, and three Tier 2 proteins, MANF, STAT6, and TNFSF8, to AR. CONCLUSION Eleven Tier 1 and 2 protein targets that are promising drug target candidates were identified for AA, AD, and AR, which influence the development of allergic diseases and expose new diagnostic and therapeutic targets.
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Affiliation(s)
- Ziqin Cao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha, 410000, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
| | - Qiangxiang Li
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
| | - Yajia Li
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
| | - Jianhuang Wu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha, 410000, China.
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.
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11
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Knol MJ, Poot RA, Evans TE, Satizabal CL, Mishra A, Sargurupremraj M, van der Auwera S, Duperron MG, Jian X, Hostettler IC, van Dam-Nolen DHK, Lamballais S, Pawlak MA, Lewis CE, Carrion-Castillo A, van Erp TGM, Reinbold CS, Shin J, Scholz M, Håberg AK, Kämpe A, Li GHY, Avinun R, Atkins JR, Hsu FC, Amod AR, Lam M, Tsuchida A, Teunissen MWA, Aygün N, Patel Y, Liang D, Beiser AS, Beyer F, Bis JC, Bos D, Bryan RN, Bülow R, Caspers S, Catheline G, Cecil CAM, Dalvie S, Dartigues JF, DeCarli C, Enlund-Cerullo M, Ford JM, Franke B, Freedman BI, Friedrich N, Green MJ, Haworth S, Helmer C, Hoffmann P, Homuth G, Ikram MK, Jack CR, Jahanshad N, Jockwitz C, Kamatani Y, Knodt AR, Li S, Lim K, Longstreth WT, Macciardi F, Mäkitie O, Mazoyer B, Medland SE, Miyamoto S, Moebus S, Mosley TH, Muetzel R, Mühleisen TW, Nagata M, Nakahara S, Palmer ND, Pausova Z, Preda A, Quidé Y, Reay WR, Roshchupkin GV, Schmidt R, Schreiner PJ, Setoh K, Shapland CY, Sidney S, St Pourcain B, Stein JL, Tabara Y, Teumer A, Uhlmann A, van der Lugt A, Vernooij MW, Werring DJ, Windham BG, Witte AV, Wittfeld K, Yang Q, Yoshida K, Brunner HG, Le Grand Q, Sim K, Stein DJ, Bowden DW, Cairns MJ, Hariri AR, Cheung CL, Andersson S, Villringer A, Paus T, Cichon S, Calhoun VD, Crivello F, Launer LJ, White T, Koudstaal PJ, Houlden H, Fornage M, Matsuda F, Grabe HJ, Ikram MA, Debette S, Thompson PM, Seshadri S, Adams HHH. Genetic variants for head size share genes and pathways with cancer. Cell Rep Med 2024; 5:101529. [PMID: 38703765 DOI: 10.1016/j.xcrm.2024.101529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 09/18/2023] [Accepted: 04/04/2024] [Indexed: 05/06/2024]
Abstract
The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.
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Affiliation(s)
- Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Raymond A Poot
- Department of Cell Biology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA; The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, Bordeaux, France
| | - Muralidharan Sargurupremraj
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Sandra van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; German Centre of Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Marie-Gabrielle Duperron
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, Bordeaux, France
| | - Xueqiu Jian
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Isabel C Hostettler
- Stroke Research Centre, University College London, Institute of Neurology, London, UK; Department of Neurosurgery, Klinikum rechts der Isar, University of Munich, Munich, Germany; Neurosurgical Department, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Dianne H K van Dam-Nolen
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Sander Lamballais
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mikolaj A Pawlak
- Department of Neurology, Poznań University of Medical Sciences, Poznań, Poland; Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Cora E Lewis
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - Amaia Carrion-Castillo
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, USA
| | - Céline S Reinbold
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland; Institute of Computational Life Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Canada
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany; LIFE Research Center for Civilization Disease, Leipzig, Germany
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Anders Kämpe
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Gloria H Y Li
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Reut Avinun
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia; Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Alyssa R Amod
- Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - Max Lam
- North Region, Institute of Mental Health, Singapore, Singapore; Population and Global Health, LKC Medicine, Nanyang Technological University, Singapore, Singapore
| | - Ami Tsuchida
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, Bordeaux, France; Groupe d'imagerie neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Mariël W A Teunissen
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Neurology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Nil Aygün
- Department of Genetics UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yash Patel
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Dan Liang
- Department of Genetics UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexa S Beiser
- The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany; Collaborative Research Center 1052 Obesity Mechanisms, Faculty of Medicine, University of Leipzig, Leipzig, Germany; Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gwenaëlle Catheline
- University of Bordeaux, CNRS, INCIA, UMR 5287, team NeuroImagerie et Cognition Humaine, Bordeaux, France; EPHE-PSL University, Bordeaux, France
| | - Charlotte A M Cecil
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Shareefa Dalvie
- Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - Jean-François Dartigues
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team SEPIA, UMR 1219, Bordeaux, France
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Maria Enlund-Cerullo
- Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | - Judith M Ford
- San Francisco Veterans Administration Medical Center, San Francisco, CA, USA; University of California, San Francisco, San Francisco, CA, USA
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Melissa J Green
- School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia; Neuroscience Research Australia, Sydney, NSW, Australia
| | - Simon Haworth
- Bristol Dental School, University of Bristol, Bristol, UK
| | - Catherine Helmer
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team LEHA, UMR 1219, Bordeaux, France
| | - Per Hoffmann
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, University of Bonn Medical School, Bonn, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | | | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck USC School of Medicine, Los Angeles, CA, USA
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Medical Faculty, Aachen, Germany
| | - Yoichiro Kamatani
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Annchen R Knodt
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Keane Lim
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - W T Longstreth
- Department of Neurology, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Fabio Macciardi
- Laboratory of Molecular Psychiatry, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Outi Mäkitie
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden; Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | - Bernard Mazoyer
- Groupe d'imagerie neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France; Centre Hospitalo-Universitaire de Bordeaux, Bordeaux, France
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Psychology, University of Queensland, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Susanne Moebus
- Institute for Urban Public Health, University of Duisburg-Essen, Essen, Germany
| | - Thomas H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA; Memory Impairment and Neurodegenerative Dementia (MIND) Center, Jackson, MS, USA
| | - Ryan Muetzel
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Thomas W Mühleisen
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; C. and O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Manabu Nagata
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Soichiro Nakahara
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA; Unit 2, Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc, 21 Miyukigaoka, Tsukuba, Ibaraki 305-8585, Japan
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Canada
| | - Adrian Preda
- Department of Psychiatry, University of California, Irvine, Irvine, CA, USA
| | - Yann Quidé
- School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia; Neuroscience Research Australia, Sydney, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia; Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Gennady V Roshchupkin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | | | - Kazuya Setoh
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chin Yang Shapland
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, University of Bristol, Bristol, UK
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA, USA
| | - Beate St Pourcain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Jason L Stein
- Department of Genetics UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Alexander Teumer
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anne Uhlmann
- Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - David J Werring
- Stroke Research Centre, University College London, Institute of Neurology, London, UK
| | - B Gwen Windham
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA; Memory Impairment and Neurodegenerative Dementia (MIND) Center, Jackson, MS, USA
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany; Collaborative Research Center 1052 Obesity Mechanisms, Faculty of Medicine, University of Leipzig, Leipzig, Germany; Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; German Centre of Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Han G Brunner
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Clinical Genetics MUMC+, GROW School of Oncology and Developmental Biology, and MHeNs School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Quentin Le Grand
- Bordeaux Population Health, University of Bordeaux, INSERM U1219, Bordeaux, France
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Dan J Stein
- Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany; SAMRC Unit on Risk and Resilience, University of Cape Town, Cape Town, South Africa
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia; Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sture Andersson
- Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany; Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Tomas Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada; Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Sven Cichon
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) {Georgia State, Georgia Tech, Emory}, Atlanta, GA, USA
| | - Fabrice Crivello
- Groupe d'imagerie neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Lenore J Launer
- Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute of Aging, The National Institutes of Health, Bethesda, MD, USA
| | - Tonya White
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Peter J Koudstaal
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Henry Houlden
- Stroke Research Centre, University College London, Institute of Neurology, London, UK
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA; Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health, University of Bordeaux, INSERM U1219, Bordeaux, France; Department of Neurology, Bordeaux University Hospital, Bordeaux, France
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck USC School of Medicine, Los Angeles, CA, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA; The Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Hieab H H Adams
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
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12
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Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, Zhan X, Carrel L, Liu DJ, Jiang B. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 2024; 15:4260. [PMID: 38769300 DOI: 10.1038/s41467-024-48143-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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Affiliation(s)
- Lida Wang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Chachrit Khunsriraksakul
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Havell Markus
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Dieyi Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fan Zhang
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fang Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xiaowei Zhan
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, US
- Center for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, US
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US.
| | - Bibo Jiang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
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13
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Niu L, Du Z, Xie Z, Liu X, Wang Q, Zhao Y, Wang H, Hao C, Xue D, Wang L. Total testosterone plays a crucial role in the pathway from hypothyroidism to broad depression in women. J Affect Disord 2024; 359:164-170. [PMID: 38768827 DOI: 10.1016/j.jad.2024.05.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Depression tends to develop in correlation with hypothyroidism, however it's unclear how testosterone traits contribute to this association. We examined the causal association between depression, testosterone traits, and hypothyroidism using Mendelian randomization (MR). METHOD We conducted univariable and multivariable MR studies using summary-level statistics from genome-wide association studies (GWAS) of Hypothyroidism (n = 213,990), broad depression (n = 322,580), probable major depressive disorder (probable MDD) (n = 174,519), and International Classification of Diseases (ICD)-9 or ICD-10-coded MDD (n = 217,584) from European ancestry. The inverse variance weighted (IVW) method was used as the main MR analysis. RESULTS In univariate MR analysis, there is a positive causal relationship between hypothyroidism and broad depression (P = 0.0074; OR = 1.0066; 95%CI: 1.0018-1.0114) and probable MDD (P = 0.0242; OR = 1.0056; 95%CI: 1.0007-1.0105). In females, there is a causal relationship between hypothyroidism and decreased total testosterone (P < 0.001; OR = 0.9747; 95%CI: 0.9612-0.9885) and sex hormone binding globulin (SHBG) levels (P = 0.0418; OR = 0.9858; 95%CI: 0.9723-0.9995). In females, there is an inverse causal relationship between total testosterone and broad depression (P = 0.0349; OR = 0.9898; 95%CI: 0.9804-0.9993). Furthermore, in multivariate MR analysis, after adjusting for total testosterone in females, hypothyroidism only has a positive causal relationship with probable MDD, and the relationship with broad depression is no longer significant. Most notably, after adjusting for hypothyroidism, the inverse causal effect of female total testosterone levels on broad depression becomes more significant (P = 0.0154; OR = 0.9878; 95%CI: 0.9780-0.9977). CONCLUSION Hypothyroidism increases the risk of broad depression and probable MDD development. Total Testosterone appears to play an important role in the relationship between hypothyroidism and broad depression in female.
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Affiliation(s)
- Le Niu
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhiwei Du
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhihong Xie
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuxu Liu
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qiang Wang
- Department of General Surgery, Qilu Hospital of Shandong University, Shandong, China
| | - Yong Zhao
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hao Wang
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chenjun Hao
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dongbo Xue
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Liyi Wang
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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14
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Chen K, Nan J, Xiong X. Genetic regulation of m 6A RNA methylation and its contribution in human complex diseases. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-024-2609-8. [PMID: 38764000 DOI: 10.1007/s11427-024-2609-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/02/2024] [Indexed: 05/21/2024]
Abstract
N6-methyladenosine (m6A) has been established as the most prevalent chemical modification in message RNA (mRNA), playing an essential role in determining the fate of RNA molecules. Dysregulation of m6A has been revealed to lead to abnormal physiological conditions and cause various types of human diseases. Recent studies have delineated the genetic regulatory maps for m6A methylation by mapping the quantitative trait loci of m6A (m6A-QTLs), thereby building up the regulatory circuits linking genetic variants, m6A, and human complex traits. Here, we review the recent discoveries concerning the genetic regulatory maps of m6A, describing the methodological and technical details of m6A-QTL identification, and introducing the key findings of the cis- and trans-acting drivers of m6A. We further delve into the tissue- and ethnicity-specificity of m6A-QTL, the association with other molecular phenotypes in light of genetic regulation, the regulators underlying m6A genetics, and importantly, the functional roles of m6A in mediating human complex diseases. Lastly, we discuss potential research avenues that can accelerate the translation of m6A genetics studies toward the development of therapies for human genetic diseases.
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Affiliation(s)
- Kexuan Chen
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China
| | - Jiuhong Nan
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China
| | - Xushen Xiong
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China.
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China.
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15
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Munro D, Ehsan N, Esmaeili-Fard SM, Gusev A, Palmer AA, Mohammadi P. Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594051. [PMID: 38798366 PMCID: PMC11118360 DOI: 10.1101/2024.05.14.594051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Transcriptome data is commonly used to understand genome function via quantitative trait loci (QTL) mapping and to identify the molecular mechanisms driving genome wide association study (GWAS) signals through colocalization analysis and transcriptome-wide association studies (TWAS). While RNA sequencing (RNA-seq) has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, such studies are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry (Pan-transcriptomic phenotyping), a framework to efficiently generate diverse RNA phenotypes from RNA-seq data and perform downstream integrative analyses with genetic data. Pantry currently generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We applied Pantry to Geuvadis and GTEx data, and found that 4,768 of the genes with no identified expression QTL in Geuvadis had QTLs in at least one other transcriptional modality, resulting in a 66% increase in genes over expression QTL mapping. We further found that QTLs exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities (xTWAS) approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs. We provide the Pantry code, RNA phenotypes from all Geuvadis and GTEx samples, and xQTL and xTWAS results on the web.
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16
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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.20.23291605. [PMID: 37425698 PMCID: PMC10327185 DOI: 10.1101/2023.06.20.23291605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.
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17
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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18
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Hemerich D, Svenstrup V, Obrero VD, Preuss M, Moscati A, Hirschhorn JN, Loos RJF. An integrative framework to prioritize genes in more than 500 loci associated with body mass index. Am J Hum Genet 2024:S0002-9297(24)00131-9. [PMID: 38754426 DOI: 10.1016/j.ajhg.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.
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Affiliation(s)
- Daiane Hemerich
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Bristol Myers Squibb, Summit, NJ, USA
| | - Victor Svenstrup
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Virginia Diez Obrero
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Regeneron Genetics Center, Tarrytown, NY, USA
| | - Joel N Hirschhorn
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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19
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Johnston KJ, Signer R, Huckins LM. Chronic Overlapping Pain Conditions and Nociplastic Pain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.27.23291959. [PMID: 38766033 PMCID: PMC11100847 DOI: 10.1101/2023.06.27.23291959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Chronic Overlapping Pain Conditions (COPCs) are a subset of chronic pain conditions commonly comorbid with one another and more prevalent in women and assigned female at birth (AFAB) individuals. Pain experience in these conditions may better fit with a new mechanistic pain descriptor, nociplastic pain, and nociplastic type pain may represent a shared underlying factor among COPCs. We applied GenomicSEM common-factor genome wide association study (GWAS) and multivariate transcriptome-wide association (TWAS) analyses to existing GWAS output for six COPCs in order to find genetic variation associated with nociplastic type pain, followed by genetic correlation (linkage-disequilibrium score regression), gene-set and tissue enrichment analyses. We found 24 independent single nucleotide polymorphisms (SNPs), and 127 unique genes significantly associated with nociplastic type pain, and showed nociplastic type pain to be a polygenic trait with significant SNP-heritability. We found significant genetic overlap between multisite chronic pain and nociplastic type pain, and to a smaller extent with rheumatoid arthritis and a neuropathic pain phenotype. Tissue enrichment analyses highlighted cardiac and thyroid tissue, and gene set enrichment analyses emphasized potential shared mechanisms in cognitive, personality, and metabolic traits and nociplastic type pain along with distinct pathology in migraine and headache. We use a well-powered network approach to investigate nociplastic type pain using existing COPC GWAS output, and show nociplastic type pain to be a complex, heritable trait, in addition to contributing to understanding of potential mechanisms in development of nociplastic pain.
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Affiliation(s)
- Keira J.A. Johnston
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06511, USA
| | - Rebecca Signer
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT 06511, USA
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20
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Khan RR, Guerrero RF, Wapner RJ, Hahn MW, Raja A, Salleb-Aouissi A, Grobman WA, Simhan H, Silver RM, Chung JH, Reddy UM, Radivojac P, Pe'er I, Haas DM. Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas. Sci Rep 2024; 14:10514. [PMID: 38714721 PMCID: PMC11076516 DOI: 10.1038/s41598-024-61218-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/02/2024] [Indexed: 05/10/2024] Open
Abstract
Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.
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Affiliation(s)
- Raiyan R Khan
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Rafael F Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
- Department of Computer Science, Indiana University, Bloomington, IN, USA
| | - Ronald J Wapner
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Matthew W Hahn
- Department of Computer Science, Indiana University, Bloomington, IN, USA
- Department of Biology, Indiana University, Bloomington, IN, USA
| | - Anita Raja
- Department of Computer Science, CUNY Hunter College, New York, NY, USA
| | | | - William A Grobman
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Hyagriv Simhan
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Robert M Silver
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA
| | - Judith H Chung
- Department of Obstetrics and Gynecology, University of California, Irvine, Orange, CA, USA
| | - Uma M Reddy
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, NY, USA
| | - David M Haas
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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21
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Guo Y, Liu Q, Zheng Z, Qing M, Yao T, Wang B, Zhou M, Wang D, Ke Q, Ma J, Shan Z, Chen W. Genetic association of inflammatory marker GlycA with lung function and respiratory diseases. Nat Commun 2024; 15:3751. [PMID: 38704398 PMCID: PMC11069551 DOI: 10.1038/s41467-024-47845-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Association of circulating glycoprotein acetyls (GlycA), a systemic inflammation biomarker, with lung function and respiratory diseases remain to be investigated. We examined the genetic correlation, shared genetics, and potential causality of GlycA (N = 115,078) with lung function and respiratory diseases (N = 497,000). GlycA showed significant genetic correlation with FEV1 (rg = -0.14), FVC (rg = -0.18), asthma (rg = 0.21) and COPD (rg = 0.31). We consistently identified ten shared loci (including chr3p21.31 and chr8p23.1) at both SNP and gene level revealing potential shared biological mechanisms involving ubiquitination, immune response, Wnt/β-catenin signaling, cell growth and differentiation in tissues or cells including blood, epithelium, fibroblast, fetal thymus, and fetal intestine. Genetically elevated GlycA was significantly correlated with lung function and asthma susceptibility (354.13 ml decrement of FEV1, 442.28 ml decrement of FVC, and 144% increased risk of asthma per SD increment of GlycA) from MR analyses. Our findings provide insights into biological mechanisms of GlycA in relating to lung function, asthma, and COPD.
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Affiliation(s)
- Yanjun Guo
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
- Department of Epidemiology, Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
| | - Quanhong Liu
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Zhilin Zheng
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Mengxia Qing
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Tianci Yao
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bin Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Min Zhou
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Dongming Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Qinmei Ke
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jixuan Ma
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Zhilei Shan
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weihong Chen
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
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22
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Ping J, Jia G, Cai Q, Guo X, Tao R, Ambrosone C, Huo D, Ambs S, Barnard ME, Chen Y, Garcia-Closas M, Gu J, Hu JJ, John EM, Li CI, Nathanson K, Nemesure B, Olopade OI, Pal T, Press MF, Sanderson M, Sandler DP, Yoshimatsu T, Adejumo PO, Ahearn T, Brewster AM, Hennis AJM, Makumbi T, Ndom P, O'Brien KM, Olshan AF, Oluwasanu MM, Reid S, Yao S, Butler EN, Huang M, Ntekim A, Li B, Troester MA, Palmer JR, Haiman CA, Long J, Zheng W. Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes. Nat Commun 2024; 15:3718. [PMID: 38697998 PMCID: PMC11065893 DOI: 10.1038/s41467-024-47650-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 04/08/2024] [Indexed: 05/05/2024] Open
Abstract
African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P < 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations.
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Affiliation(s)
- Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christine Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center of Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Yu Chen
- Division of Epidemiology, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Jian Gu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, University of Miami School of Medicine, Miami, FL, USA
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Katherine Nathanson
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Tuya Pal
- Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael F Press
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Toshio Yoshimatsu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Prisca O Adejumo
- Department of Nursing, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Abenaa M Brewster
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anselm J M Hennis
- George Alleyne Chronic Disease Research Centre, University of the West Indies, Bridgetown, Barbados
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | | | - Paul Ndom
- Yaounde General Hospital, Yaounde, Cameroon
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Andrew F Olshan
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mojisola M Oluwasanu
- Department of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Sonya Reid
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Ebonee N Butler
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Maosheng Huang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Atara Ntekim
- Department of Radiation Oncology, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Melissa A Troester
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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23
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Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia. Am J Hum Genet 2024:S0002-9297(24)00127-7. [PMID: 38723630 DOI: 10.1016/j.ajhg.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024] Open
Abstract
Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.
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Affiliation(s)
- S Taylor Head
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Felipe Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Andrei Todor
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jasmine Plummer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Simon Gayther
- Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Siddhartha Kar
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Joellen Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
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24
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Koebbe LL, Hess T, Giel AS, Bigge J, Gehlen J, Schueller V, Geppert M, Dumoulin FL, Heller J, Schepke M, Plaßmann D, Vieth M, Venerito M, Schumacher J, Maj C. The genetic regulation of the gastric transcriptome is associated with metabolic and obesity-related traits and diseases. Physiol Genomics 2024; 56:384-396. [PMID: 38406838 DOI: 10.1152/physiolgenomics.00120.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/26/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024] Open
Abstract
Tissue-specific gene expression and gene regulation lead to a better understanding of tissue-specific physiology and pathophysiology. We analyzed the transcriptome and genetic regulatory profiles of two distinct gastric sites, corpus and antrum, to identify tissue-specific gene expression and its regulation. Gastric corpus and antrum mucosa biopsies were collected during routine gastroscopies from up to 431 healthy individuals. We obtained genotype and transcriptome data and performed transcriptome profiling and expression quantitative trait locus (eQTL) studies. We further used data from genome-wide association studies (GWAS) of various diseases and traits to partition their heritability and to perform transcriptome-wide association studies (TWAS). The transcriptome data from corpus and antral mucosa highlights the heterogeneity of gene expression in the stomach. We identified enriched pathways revealing distinct and common physiological processes in gastric corpus and antrum. Furthermore, we found an enrichment of the single nucleotide polymorphism (SNP)-based heritability of metabolic, obesity-related, and cardiovascular traits and diseases by considering corpus- and antrum-specifically expressed genes. Particularly, we could prioritize gastric-specific candidate genes for multiple metabolic traits, like NQO1 which is involved in glucose metabolism, MUC1 which contributes to purine and protein metabolism or RAB27B being a regulator of weight and body composition. Our findings show that gastric corpus and antrum vary in their transcriptome and genetic regulatory profiles indicating physiological differences which are mostly related to digestion and epithelial protection. Moreover, our findings demonstrate that the genetic regulation of the gastric transcriptome is linked to biological mechanisms associated with metabolic, obesity-related, and cardiovascular traits and diseases. NEW & NOTEWORTHY We analyzed the transcriptomes and genetic regulatory profiles of gastric corpus and for the first time also of antrum mucosa in 431 healthy individuals. Through tissue-specific gene expression and eQTL analyses, we uncovered unique and common physiological processes across both primary gastric sites. Notably, our findings reveal that stomach-specific eQTLs are enriched in loci associated with metabolic traits and diseases, highlighting the pivotal role of gene expression regulation in gastric physiology and potential pathophysiology.
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Affiliation(s)
- Laura L Koebbe
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Timo Hess
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Ann-Sophie Giel
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Jessica Bigge
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Jan Gehlen
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | | | | | | | - Joerg Heller
- Marienhaus Hospital Ahrweiler, Ahrweiler, Germany
| | - Michael Schepke
- Department of Gastroenterology, Helios Hospital Siegburg, Siegburg, Germany
| | | | - Michael Vieth
- Institute for Pathology, Klinikum Bayreuth, University of Erlangen-Nuremberg, Bayreuth, Germany
| | - Marino Venerito
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Hospital, Magdeburg, Germany
| | | | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
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25
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Wu X, Yang C, Zou Y, Jones SE, Zhao X, Zhang L, Han Z, Hao Y, Xiao J, Xiao C, Zhang W, Yan P, Cui H, Tang M, Wang Y, Chen L, Zhang L, Yao Y, Liu Z, Li J, Jiang X, Zhang B. Using human genetics to understand the phenotypic association between chronotype and breast cancer. J Sleep Res 2024; 33:e13973. [PMID: 37380357 DOI: 10.1111/jsr.13973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/23/2023] [Accepted: 06/11/2023] [Indexed: 06/30/2023]
Abstract
Little is known regarding the shared genetic influences underlying the observed phenotypic association between chronotype and breast cancer in women. Leveraging summary statistics from the hitherto largest genome-wide association study conducted in each trait, we investigated the genetic correlation, pleiotropic loci, and causal relationship of chronotype with overall breast cancer, and with its subtypes defined by the status of oestrogen receptor. We identified a negative genomic correlation between chronotype and overall breast cancer (r g = -0.06, p = 3.00 × 10-4), consistent across oestrogen receptor-positive (r g = -0.05, p = 3.30 × 10-3) and oestrogen receptor-negative subtypes (r g = -0.05, p = 1.11 × 10-2). Five specific genomic regions were further identified as contributing a significant local genetic correlation. Cross-trait meta-analysis identified 78 loci shared between chronotype and breast cancer, of which 23 were novel. Transcriptome-wide association study revealed 13 shared genes, targeting tissues of the nervous, cardiovascular, digestive, and exocrine/endocrine systems. Mendelian randomisation demonstrated a significantly reduced risk of overall breast cancer (odds ratio 0.89, 95% confidence interval 0.83-0.94; p = 1.30 × 10-4) for genetically predicted morning chronotype. No reverse causality was found. Our work demonstrates an intrinsic link underlying chronotype and breast cancer, which may provide clues to inform management of sleep habits to improve female health.
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Affiliation(s)
- Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, Luzhou, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Samuel E Jones
- Institute for Molecular Medicine, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Xunying Zhao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhitong Han
- School of Life Sciences, Sichuan University, Chengdu, China
| | - Yu Hao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jinyu Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ling Zhang
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhenmi Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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26
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Breunig S, Lawrence JM, Foote IF, Gebhardt HJ, Willcutt EG, Grotzinger AD. Examining Differences in the Genetic and Functional Architecture of Attention-Deficit/Hyperactivity Disorder Diagnosed in Childhood and Adulthood. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100307. [PMID: 38633226 PMCID: PMC11021367 DOI: 10.1016/j.bpsgos.2024.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with diagnostic criteria requiring symptoms to begin in childhood. We investigated whether individuals diagnosed as children differ from those diagnosed in adulthood with respect to shared and unique architecture at the genome-wide and gene expression level of analysis. Methods We used genomic structural equation modeling (SEM) to investigate differences in genetic correlations (rg) of childhood-diagnosed (ncases = 14,878) and adulthood-diagnosed (ncases = 6961) ADHD with 98 behavioral, psychiatric, cognitive, and health outcomes. We went on to apply transcriptome-wide SEM to identify functional annotations and patterns of gene expression associated with genetic risk sharing or divergence across the ADHD subgroups. Results Compared with the childhood subgroup, adulthood-diagnosed ADHD exhibited a significantly larger negative rg with educational attainment, the noncognitive skills of educational attainment, and age at first sexual intercourse. We observed a larger positive rg for adulthood-diagnosed ADHD with major depression, suicidal ideation, and a latent internalizing factor. At the gene expression level, transcriptome-wide SEM analyses revealed 22 genes that were significantly associated with shared genetic risk across the subtypes that reflected a mixture of coding and noncoding genes and included 15 novel genes relative to the ADHD subgroups. Conclusions This study demonstrated that ADHD diagnosed later in life shows much stronger genetic overlap with internalizing disorders and related traits. This may indicate the potential clinical relevance of distinguishing these subgroups or increased misdiagnosis for those diagnosed later in life. Top transcriptome-wide SEM results implicated genes related to neuronal function and clinical characteristics (e.g., sleep).
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Affiliation(s)
- Sophie Breunig
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Jeremy M. Lawrence
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Isabelle F. Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Hannah J. Gebhardt
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Erik G. Willcutt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
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27
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Liufu C, Luo L, Pang T, Zheng H, Yang L, Lu L, Chang S. Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders. Mol Psychiatry 2024:10.1038/s41380-024-02574-w. [PMID: 38684796 DOI: 10.1038/s41380-024-02574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.
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Affiliation(s)
- Chao Liufu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lingxue Luo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Haohao Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Li Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China.
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28
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Wen J, Zhang J, Zhang H, Zhang N, Lei R, Deng Y, Cheng Q, Li H, Luo P. Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases. J Transl Med 2024; 22:392. [PMID: 38685026 PMCID: PMC11057084 DOI: 10.1186/s12967-024-04928-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Epidemiological evidence links a close correlation between long-term exposure to air pollutants and autoimmune diseases, while the causality remained unknown. METHODS Two-sample Mendelian randomization (TSMR) was used to investigate the role of PM10, PM2.5, NO2, and NOX (N = 423,796-456,380) in 15 autoimmune diseases (N = 14,890-314,995) using data from large European GWASs including UKB, FINNGEN, IMSGC, and IPSCSG. Multivariable Mendelian randomization (MVMR) was conducted to investigate the direct effect of each air pollutant and the mediating role of common factors, including body mass index (BMI), alcohol consumption, smoking status, and household income. Transcriptome-wide association studies (TWAS), two-step MR, and colocalization analyses were performed to explore underlying mechanisms between air pollution and autoimmune diseases. RESULTS In TSMR, after correction of multiple testing, hypothyroidism was causally associated with higher exposure to NO2 [odds ratio (OR): 1.37, p = 9.08 × 10-4] and NOX [OR: 1.34, p = 2.86 × 10-3], ulcerative colitis (UC) was causally associated with higher exposure to NOX [OR: 2.24, p = 1.23 × 10-2] and PM2.5 [OR: 2.60, p = 5.96 × 10-3], rheumatoid arthritis was causally associated with higher exposure to NOX [OR: 1.72, p = 1.50 × 10-2], systemic lupus erythematosus was causally associated with higher exposure to NOX [OR: 4.92, p = 6.89 × 10-3], celiac disease was causally associated with lower exposure to NOX [OR: 0.14, p = 6.74 × 10-4] and PM2.5 [OR: 0.17, p = 3.18 × 10-3]. The risky effects of PM2.5 on UC remained significant in MVMR analyses after adjusting for other air pollutants. MVMR revealed several common mediators between air pollutants and autoimmune diseases. Transcriptional analysis identified specific gene transcripts and pathways interconnecting air pollutants and autoimmune diseases. Two-step MR revealed that POR, HSPA1B, and BRD2 might mediate from air pollutants to autoimmune diseases. POR pQTL (rs59882870, PPH4=1.00) strongly colocalized with autoimmune diseases. CONCLUSION This research underscores the necessity of rigorous air pollutant surveillance within public health studies to curb the prevalence of autoimmune diseases.
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Affiliation(s)
- Jie Wen
- The Animal Laboratory Center, Hunan Cancer Hospital, and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jingwei Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Nan Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Ruoyan Lei
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Yujia Deng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- First Clinical Department, Changsha Medical University, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
| | - He Li
- The Animal Laboratory Center, Hunan Cancer Hospital, and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
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29
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Luo L, Pang T, Zheng H, Liufu C, Chang S. xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data. J Transl Med 2024; 22:387. [PMID: 38664746 PMCID: PMC11044291 DOI: 10.1186/s12967-024-05065-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/05/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes. METHODS In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored. RESULTS Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes. CONCLUSIONS The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.
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Affiliation(s)
- Lingxue Luo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Haohao Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Chao Liufu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China.
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China.
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30
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Jin X, Mei Y, Yang P, Huang R, Zhang H, Wu Y, Wang M, He X, Jiang Z, Zhu W, Wang L. Prioritization of therapeutic targets for cancers using integrative multi-omics analysis. Hum Genomics 2024; 18:42. [PMID: 38659038 PMCID: PMC11040978 DOI: 10.1186/s40246-024-00571-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/17/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The integration of transcriptomic, proteomic, druggable genetic and metabolomic association studies facilitated a comprehensive investigation of molecular features and shared pathways for cancers' development and progression. METHODS Comprehensive approaches consisting of transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR) and MR were performed to identify genes significantly associated with cancers. The results identified in above analyzes were subsequently involved in phenotype scanning and enrichment analyzes to explore the possible health effects and shared pathways. Additionally, we also conducted MR analysis to investigate metabolic pathways related to cancers. RESULTS Totally 24 genes (18 transcriptomic, 1 proteomic and 5 druggable genetic) showed significant associations with cancers risk. All genes identified in multiple methods were mainly enriched in nuclear factor erythroid 2-related factor 2 (NRF2) pathway. Additionally, biosynthesis of ubiquinol and urate were found to play an important role in gastrointestinal tumors. CONCLUSIONS A set of putatively causal genes and pathways relevant to cancers were identified in this study, shedding light on the shared biological processes for tumorigenesis and providing compelling genetic evidence to prioritize anti-cancer drugs development.
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Affiliation(s)
- Xin Jin
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yunyun Mei
- Department of Neurosurgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Puyu Yang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People's Republic of China
| | - Runze Huang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Haifeng Zhang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People's Republic of China
| | - Yibin Wu
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Miao Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xigan He
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Ziting Jiang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Weiping Zhu
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Lu Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
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31
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Chia R, Ray A, Shah Z, Ding J, Ruffo P, Fujita M, Menon V, Saez-Atienzar S, Reho P, Kaivola K, Walton RL, Reynolds RH, Karra R, Sait S, Akcimen F, Diez-Fairen M, Alvarez I, Fanciulli A, Stefanova N, Seppi K, Duerr S, Leys F, Krismer F, Sidoroff V, Zimprich A, Pirker W, Rascol O, Foubert-Samier A, Meissner WG, Tison F, Pavy-Le Traon A, Pellecchia MT, Barone P, Russillo MC, Marín-Lahoz J, Kulisevsky J, Torres S, Mir P, Periñán MT, Proukakis C, Chelban V, Wu L, Goh YY, Parkkinen L, Hu MT, Kobylecki C, Saxon JA, Rollinson S, Garland E, Biaggioni I, Litvan I, Rubio I, Alcalay RN, Kwei KT, Lubbe SJ, Mao Q, Flanagan ME, Castellani RJ, Khurana V, Ndayisaba A, Calvo A, Mora G, Canosa A, Floris G, Bohannan RC, Moore A, Norcliffe-Kaufmann L, Palma JA, Kaufmann H, Kim C, Iba M, Masliah E, Dawson TM, Rosenthal LS, Pantelyat A, Albert MS, Pletnikova O, Troncoso JC, Infante J, Lage C, Sánchez-Juan P, Serrano GE, Beach TG, Pastor P, Morris HR, Albani D, Clarimon J, Wenning GK, Hardy JA, Ryten M, Topol E, Torkamani A, Chiò A, Bennett DA, De Jager PL, Low PA, Singer W, Cheshire WP, Wszolek ZK, Dickson DW, Traynor BJ, Gibbs JR, Dalgard CL, Ross OA, Houlden H, Scholz SW. Genome sequence analyses identify novel risk loci for multiple system atrophy. Neuron 2024:S0896-6273(24)00240-X. [PMID: 38701790 DOI: 10.1016/j.neuron.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/28/2024] [Accepted: 04/02/2024] [Indexed: 05/05/2024]
Abstract
Multiple system atrophy (MSA) is an adult-onset, sporadic synucleinopathy characterized by parkinsonism, cerebellar ataxia, and dysautonomia. The genetic architecture of MSA is poorly understood, and treatments are limited to supportive measures. Here, we performed a comprehensive analysis of whole genome sequence data from 888 European-ancestry MSA cases and 7,128 controls to systematically investigate the genetic underpinnings of this understudied neurodegenerative disease. We identified four significantly associated risk loci using a genome-wide association study approach. Transcriptome-wide association analyses prioritized USP38-DT, KCTD7, and lnc-KCTD7-2 as novel susceptibility genes for MSA within these loci, and single-nucleus RNA sequence analysis found that the associated variants acted as cis-expression quantitative trait loci for multiple genes across neuronal and glial cell types. In conclusion, this study highlights the role of genetic determinants in the pathogenesis of MSA, and the publicly available data from this study represent a valuable resource for investigating synucleinopathies.
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Affiliation(s)
- Ruth Chia
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Anindita Ray
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Zalak Shah
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Jinhui Ding
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Paola Ruffo
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA; Medical Genetics Laboratory, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Italy
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA
| | - Sara Saez-Atienzar
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Paolo Reho
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Karri Kaivola
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Ronald L Walton
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Regina H Reynolds
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK; Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK; Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ramita Karra
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Shaimaa Sait
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Fulya Akcimen
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Monica Diez-Fairen
- Memory and Movement Disorders Units, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
| | - Ignacio Alvarez
- Memory and Movement Disorders Units, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
| | | | - Nadia Stefanova
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Susanne Duerr
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Fabian Leys
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Krismer
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Victoria Sidoroff
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Walter Pirker
- Department of Neurology, Klinik Ottakring - Wilhelminenspital, Vienna, Austria
| | - Olivier Rascol
- MSA French Reference Center and CIC-1436, Department of Clinical Pharmacology and Neurosciences, University of Toulouse, Toulouse, France
| | - Alexandra Foubert-Samier
- Service de Neurologie des Maladies Neurodégénératives, French Reference Center for MSA, NS-Park/FCRIN Network, CHU Bordeaux, Bordeaux, France
| | - Wassilios G Meissner
- Service de Neurologie des Maladies Neurodégénératives, French Reference Center for MSA, NS-Park/FCRIN Network, CHU Bordeaux, Bordeaux, France; University of Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France; Department of Medicine, University of Otago, and the New Zealand Brain Research Institute, Christchurch, New Zealand
| | - François Tison
- Service de Neurologie des Maladies Neurodégénératives, French Reference Center for MSA, NS-Park/FCRIN Network, CHU Bordeaux, Bordeaux, France; University of Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
| | - Anne Pavy-Le Traon
- French Reference Center for MSA, Department of Neurosciences, Centre d'Investigation Clinique de Toulouse CIC1436, UMR 1048, Institute of Cardiovascular and Metabolic Diseases (I2MC), University Hospital of Toulouse, INSERM, Toulouse, France
| | - Maria Teresa Pellecchia
- Neuroscience Section, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Salerno, Italy
| | - Paolo Barone
- Neuroscience Section, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Salerno, Italy
| | - Maria Claudia Russillo
- Neuroscience Section, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Salerno, Italy
| | - Juan Marín-Lahoz
- Movement Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau), Centro de Investigación en Red Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain; Servicio de Neurología, Hospital Universitario Miguel Servet, Zaragoza, Spain
| | - Jaime Kulisevsky
- Movement Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau), Centro de Investigación en Red Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Soraya Torres
- Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau), Centro de Investigación en Red Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pablo Mir
- Unidad de Trastornos del Movimiento Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain; Departamento de Medicina Facultad de Medicina, Universidad de Sevilla, Seville, Spain
| | - Maria Teresa Periñán
- Unidad de Trastornos del Movimiento Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain; Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University, London, UK
| | - Christos Proukakis
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Viorica Chelban
- Department of Neuromuscular Diseases, University College London Queen Square Institute of Neurology, London, UK; The National Hospital for Neurology and Neurosurgery, London, UK
| | - Lesley Wu
- Department of Neuromuscular Diseases, University College London Queen Square Institute of Neurology, London, UK
| | - Yee Y Goh
- Department of Neuromuscular Diseases, University College London Queen Square Institute of Neurology, London, UK
| | - Laura Parkkinen
- Nuffield Department of Clinical Neurosciences, Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Christopher Kobylecki
- Department of Neurology, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK
| | - Jennifer A Saxon
- Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salfort, UK; Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Sara Rollinson
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Emily Garland
- Autonomic Dysfunction Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Italo Biaggioni
- Autonomic Dysfunction Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, San Diego, CA, USA
| | - Ileana Rubio
- Department of Neurosciences, University of California, San Diego, San Diego, CA, USA
| | - Roy N Alcalay
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA; Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Kimberly T Kwei
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Steven J Lubbe
- Ken and Ruth Davee Department of Neurology and Simpson Querrey Center for Neurogenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Qinwen Mao
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Margaret E Flanagan
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA; Department of Pathology, UT Health San Antonio, San Antonio, TX, USA
| | - Rudolph J Castellani
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Vikram Khurana
- Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Alain Ndayisaba
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria; Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrea Calvo
- "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Turin, Italy
| | - Gabriele Mora
- Istituti Clinici Scientifici Maugeri, IRCCS, Milan, Italy
| | - Antonio Canosa
- "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Turin, Italy
| | - Gianluca Floris
- Department of Neurology, University Hospital of Cagliari, Cagliari, Italy
| | - Ryan C Bohannan
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Anni Moore
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | | | - Jose-Alberto Palma
- Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Horacio Kaufmann
- Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Changyoun Kim
- Molecular Neuropathology Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Michiyo Iba
- Molecular Neuropathology Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Eliezer Masliah
- Molecular Neuropathology Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Ted M Dawson
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA; Neuroregeneration and Stem Cell Programs, Institute of Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA; Department of Pharmacology and Molecular Science, Johns Hopkins University Medical Center, Baltimore, MD, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Liana S Rosenthal
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Alexander Pantelyat
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Olga Pletnikova
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA; Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Juan C Troncoso
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Jon Infante
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Carmen Lage
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Pascual Sánchez-Juan
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain; Alzheimer's Centre Reina Sofia-CIEN Foundation-ISCIII, Madrid, Spain
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Pau Pastor
- Genomics and Transcriptomics of Synucleinopathies, Neurosciences, The Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Diego Albani
- Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Jordi Clarimon
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; The Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Gregor K Wenning
- Autonomic Unit - Division of Neurobiology, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - John A Hardy
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute of UCL, UCL Institute of Neurology, University College London, London, UK; Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK; UCL Movement Disorders Centre, University College London, London, UK; Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mina Ryten
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK; Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Eric Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Ali Torkamani
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Adriano Chiò
- "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Turin, Italy; Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy; Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Turin, Italy
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA
| | - Philip A Low
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Bryan J Traynor
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA; RNA Therapeutics Laboratory, Therapeutics Development Branch, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - J Raphael Gibbs
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Clifton L Dalgard
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL, USA
| | - Henry Houlden
- Department of Neuromuscular Diseases, University College London Queen Square Institute of Neurology, London, UK; The National Hospital for Neurology and Neurosurgery, London, UK
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA.
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32
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Shukla K, Idanwekhai K, Naradikian M, Ting S, Schoenberger SP, Brunk E. Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation. J Chem Inf Model 2024. [PMID: 38635316 DOI: 10.1021/acs.jcim.3c01967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.
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Affiliation(s)
- Kriti Shukla
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
| | - Kelvin Idanwekhai
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
- School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
| | - Martin Naradikian
- La Jolla Institute for Immunology, San Diego, California 92093, United States
| | - Stephanie Ting
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
| | | | - Elizabeth Brunk
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
- Integrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik VK, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. Nat Hum Behav 2024:10.1038/s41562-024-01851-6. [PMID: 38632388 DOI: 10.1038/s41562-024-01851-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Hyunjoon Lee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Alexander S Hatoum
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emma C Johnson
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | | | - Nancy J Cox
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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Dagostino R, Gottlieb A. Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns. BMC Genomics 2024; 25:377. [PMID: 38632500 PMCID: PMC11022497 DOI: 10.1186/s12864-024-10317-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms. RESULTS We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms. CONCLUSIONS Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.
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Affiliation(s)
- Robert Dagostino
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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He R, Liu M, Lin Z, Zhuang Z, Shen X, Pan W. DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. Biostatistics 2024; 25:468-485. [PMID: 36610078 PMCID: PMC11017120 DOI: 10.1093/biostatistics/kxac051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.
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Affiliation(s)
- Ruoyu He
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Mingyang Liu
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhong Zhuang
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Xiaotong Shen
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
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Johnston KJA, Cote AC, Hicks E, Johnson J, Huckins LM. Genetically Regulated Gene Expression in the Brain Associated With Chronic Pain: Relationships With Clinical Traits and Potential for Drug Repurposing. Biol Psychiatry 2024; 95:745-761. [PMID: 37678542 PMCID: PMC10924073 DOI: 10.1016/j.biopsych.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 07/20/2023] [Accepted: 08/28/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Chronic pain is a common, poorly understood condition. Genetic studies including genome-wide association studies have identified many relevant variants, which have yet to be translated into full understanding of chronic pain. Transcriptome-wide association studies using transcriptomic imputation methods such as S-PrediXcan can help bridge this genotype-phenotype gap. METHODS We carried out transcriptomic imputation using S-PrediXcan to identify genetically regulated gene expression associated with multisite chronic pain in 13 brain tissues and whole blood. Then, we imputed genetically regulated gene expression for over 31,000 Mount Sinai BioMe participants and performed a phenome-wide association study to investigate clinical relationships in chronic pain-associated gene expression changes. RESULTS We identified 95 experiment-wide significant gene-tissue associations (p < 7.97 × 10-7), including 36 unique genes and an additional 134 gene-tissue associations reaching within-tissue significance, including 53 additional unique genes. Of the 89 unique genes in total, 59 were novel for multisite chronic pain and 18 are established drug targets. Chronic pain genetically regulated gene expression for 10 unique genes was significantly associated with cardiac dysrhythmia, metabolic syndrome, disc disorders/dorsopathies, joint/ligament sprain, anemias, and neurologic disorder phecodes. Phenome-wide association study analyses adjusting for mean pain score showed that associations were not driven by mean pain score. CONCLUSIONS We carried out the largest transcriptomic imputation study of any chronic pain trait to date. Results highlight potential causal genes in chronic pain development and tissue and direction of effect. Several gene results were also drug targets. Phenome-wide association study results showed significant associations for phecodes including cardiac dysrhythmia and metabolic syndrome, thereby indicating potential shared mechanisms.
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Affiliation(s)
- Keira J A Johnston
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Alanna C Cote
- Pamela Sklar Division of Psychiatric Genetics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emily Hicks
- Pamela Sklar Division of Psychiatric Genetics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jessica Johnson
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Laura M Huckins
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
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Luo Q, Wang J, Ge W, Li Z, Mao Y, Wang C, Zhang L. Exploration of the potential causative genes for inflammatory bowel disease: Transcriptome-wide association analysis, Mendelian randomization analysis and Bayesian colocalisation. Heliyon 2024; 10:e28944. [PMID: 38617957 PMCID: PMC11015108 DOI: 10.1016/j.heliyon.2024.e28944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024] Open
Abstract
Background Inflammatory bowel disease (IBD) poses a complex challenge due to its intricate underlying mechanisms, and curative treatments remain elusive. Consequently, there is an urgent need to identify genes causally associated with IBD. Methods We extracted blood eQTL data from the GTExv8.ALL.Whole_Blood database, genome-wide association studies (GWAS) summary statistics of IBD from the IEU GWAS database, and performed a three-fold analysis protocol, including transcriptome-wide association analysis, Mendelian randomisation analysis, Bayesian colocalisation, and subsequent potential therapeutic agents identification. Results We identified four pathogenic genes, namely CARD9, RTEL1, STMN3 and ARFRP1, that promote the development of IBD, encompassing both ulcerative colitis (UC) and Crohn's disease (CD). Notably, ARFRP1 exhibited the ability to suppress IBD (encompassing UC and CD) development. Regarding drug prediction, cyclophosphamide emerged as a promising novel therapeutic option for IBD, encompassing UC and CD. Conclusion We identified several potential genes related to IBD (UC and CD), including CARD9, RTEL1, STMN3 and ARFRP1, warranting further investigation in functional studies to elucidate underlying disease mechanisms. Additionally, clinical studies exploring the potential of cyclophosphamide as a treatment avenue for IBD are warranted.
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Affiliation(s)
- Qinghua Luo
- Clinical Medical College, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Jiawen Wang
- Department of Proctology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Ge
- Department of Proctology, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China
| | - Zihao Li
- Office of the President, Jiangmen Wuyi Hospital of Traditional Chinese Medicine, Jiangmen, China
| | - Yuanting Mao
- Clinical Medical College, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Chen Wang
- Department of Proctology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Leichang Zhang
- Formula-Pattern Research Center, Jiangxi University of Chinese Medicine, Jiangxi, China
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Chen Y, Li W, Lv L, Yue W. Shared Genetic Determinants of Schizophrenia and Autism Spectrum Disorder Implicate Opposite Risk Patterns: A Genome-Wide Analysis of Common Variants. Schizophr Bull 2024:sbae044. [PMID: 38616054 DOI: 10.1093/schbul/sbae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS The synaptic pruning hypothesis posits that schizophrenia (SCZ) and autism spectrum disorder (ASD) may represent opposite ends of neurodevelopmental disorders: individuals with ASD exhibit an overabundance of synapses and connections while SCZ was characterized by excessive pruning of synapses and a reduction. Given the strong genetic predisposition of both disorders, we propose a shared genetic component, with certain loci having differential regulatory impacts. STUDY DESIGN Genome-Wide single nucleotide polymorphism (SNP) data of European descent from SCZ (N cases = 53 386, N controls = 77 258) and ASD (N cases = 18 381, N controls = 27 969) were analyzed. We used genetic correlation, bivariate causal mixture model, conditional false discovery rate method, colocalization, Transcriptome-Wide Association Study (TWAS), and Phenome-Wide Association Study (PheWAS) to investigate the genetic overlap and gene expression pattern. STUDY RESULTS We found a positive genetic correlation between SCZ and ASD (rg = .26, SE = 0.01, P = 7.87e-14), with 11 genomic loci jointly influencing both conditions (conjFDR <0.05). Functional analysis highlights a significant enrichment of shared genes during early to mid-fetal developmental stages. A notable genetic region on chromosome 17q21.31 (lead SNP rs2696609) showed strong evidence of colocalization (PP.H4.abf = 0.85). This SNP rs2696609 is linked to many imaging-derived brain phenotypes. TWAS indicated opposing gene expression patterns (primarily pseudogenes and long noncoding RNAs [lncRNAs]) for ASD and SCZ in the 17q21.31 region and some genes (LRRC37A4P, LINC02210, and DND1P1) exhibit considerable variation in the cerebellum across the lifespan. CONCLUSIONS Our findings support a shared genetic basis for SCZ and ASD. A common genetic variant, rs2696609, located in the Chr17q21.31 locus, may exert differential risk regulation on SCZ and ASD by altering brain structure. Future studies should focus on the role of pseudogenes, lncRNAs, and cerebellum in synaptic pruning and neurodevelopmental disorders.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Wenqiang Li
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
| | - Luxian Lv
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Province People's Hospital, Zhengzhou, Henan, China
| | - Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder (2018RU006), Chinese Academy of Medical Sciences, Beijing, China
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Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG ADVANCES 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
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Rich AL, Lin P, Gamazon ER, Zinkel SS. The broad impact of cell death genes on the human disease phenome. Cell Death Dis 2024; 15:251. [PMID: 38589365 PMCID: PMC11002008 DOI: 10.1038/s41419-024-06632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
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Affiliation(s)
- Abigail L Rich
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sandra S Zinkel
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
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Zhang W, Zhu J, Wu X, Feng T, Liao W, Li X, Chen J, Zhang L, Xiao C, Cui H, Yang C, Yan P, Wang Y, Tang M, Chen L, Liu Y, Zou Y, Wu X, Zhang L, Yang C, Yao Y, Li J, Liu Z, Jiang X, Zhang B. Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke. Hum Genet 2024:10.1007/s00439-024-02666-1. [PMID: 38578439 DOI: 10.1007/s00439-024-02666-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 04/06/2024]
Abstract
While carotid intima-media thickness (cIMT) as a noninvasive surrogate measure of atherosclerosis is widely considered a risk factor for stroke, the intrinsic link underlying cIMT and stroke has not been fully understood. We aimed to evaluate the clinical value of cIMT in stroke through the investigation of phenotypic and genetic relationships between cIMT and stroke. We evaluated phenotypic associations using observational data from UK Biobank (N = 21,526). We then investigated genetic relationships leveraging genomic data conducted in predominantly European ancestry for cIMT (N = 45,185) and any stroke (AS, Ncase/Ncontrol=40,585/406,111). Observational analyses suggested an increased hazard of stroke per one standard deviation increase in cIMT (cIMTmax-AS: hazard ratio (HR) = 1.39, 95%CI = 1.09-1.79; cIMTmean-AS: HR = 1.39, 95%CI = 1.09-1.78; cIMTmin-AS: HR = 1.32, 95%CI = 1.04-1.68). A positive global genetic correlation was observed (cIMTmax-AS: [Formula: see text]=0.23, P=9.44 × 10-5; cIMTmean-AS: [Formula: see text]=0.21, P=3.00 × 10-4; cIMTmin-AS: [Formula: see text]=0.16, P=6.30 × 10-3). This was further substantiated by five shared independent loci and 15 shared expression-trait associations. Mendelian randomization analyses suggested no causal effect of cIMT on stroke (cIMTmax-AS: odds ratio (OR)=1.12, 95%CI=0.97-1.28; cIMTmean-AS: OR=1.09, 95%CI=0.93-1.26; cIMTmin-AS: OR=1.03, 95%CI = 0.90-1.17). A putative association was observed for genetically predicted stroke on cIMT (AS-cIMTmax: beta=0.07, 95%CI = 0.01-0.13; AS-cIMTmean: beta=0.08, 95%CI = 0.01-0.15; AS-cIMTmin: beta = 0.08, 95%CI = 0.01-0.16) in the reverse direction MR, which attenuated to non-significant in sensitivity analysis. Our work does not find evidence supporting causal associations between cIMT and stroke. The pronounced cIMT-stroke association is intrinsic, and mostly attributed to shared genetic components. The clinical value of cIMT as a surrogate marker for stroke risk in the general population is likely limited.
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Affiliation(s)
- Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Jingwei Zhu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Xuan Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Tianle Feng
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Wei Liao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Xuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Jianci Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, West China- PUMC C. C. Chen Institute of Health, Sichuan University, No. 16, Section 3, South Renmin Road, Wuhou District, Chengdu, 610041, China.
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Clinical Neuroscience, Karolinskaa Institutet, Stockholm, Sweden.
| | - Ben Zhang
- Hainan General Hospital and Hainan Affiliated Hospital, Hainan Medical University, Haikou, China; West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
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Li Q, Bian J, Qian Y, Kossinna P, Gau C, Gordon PMK, Zhou X, Guo X, Yan J, Wu J, Long Q. An expression-directed linear mixed model discovering low-effect genetic variants. Genetics 2024; 226:iyae018. [PMID: 38314848 DOI: 10.1093/genetics/iyae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 11/29/2023] [Accepted: 01/05/2024] [Indexed: 02/07/2024] Open
Abstract
Detecting genetic variants with low-effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model. Our linear mixed model estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model, enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying expression-directed linear mixed model to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by expression-directed linear mixed model substantially improved estimation of missing heritability. Expression-directed linear mixed model moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.
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Affiliation(s)
- Qing Li
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Jiayi Bian
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Yanzhao Qian
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Pathum Kossinna
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Cooper Gau
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Paul M K Gordon
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
| | - Xiang Zhou
- School of Public Health, University of Michigan, Ann Arbor 48109, USA
| | - Xingyi Guo
- Department of Medicine & Biomedical Informatics, Vanderbilt University Medical Center, Nashville 37203, USA
| | - Jun Yan
- Physiology and Pharmacology, University of Calgary, Calgary T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada
| | - Jingjing Wu
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Quan Long
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada
- Department of Medical Genetics, University of Calgary, Calgary T2N 1N4, Canada
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44
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Choi E, Song J, Lee Y, Jeong Y, Jang W. Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study. Hum Genomics 2024; 18:34. [PMID: 38566255 PMCID: PMC10985920 DOI: 10.1186/s40246-024-00591-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. RESULTS In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. CONCLUSIONS Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.
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Affiliation(s)
- Eunyoung Choi
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yubin Lee
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yeonbin Jeong
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea.
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45
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell EE, Pavicic M, Sullivan KA, Xu K, Jacobson DA, Gelernter J, Rentsch CT, Stahl E, Cheatle M, Zhou H, Waxman SG, Justice AC, Kember RL, Kranzler HR. A multi-ancestry genetic study of pain intensity in 598,339 veterans. Nat Med 2024; 30:1075-1084. [PMID: 38429522 DOI: 10.1038/s41591-024-02839-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 01/27/2024] [Indexed: 03/03/2024]
Abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel Vickers-Smith
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Epidemiology and Environmental Health, University of Kentucky College of Public Health, Lexington, KY, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Divya Saini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Emily E Hartwell
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mirko Pavicic
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kyle A Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ke Xu
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Joel Gelernter
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christopher T Rentsch
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- London School of Hygiene & Tropical Medicine, London, UK
| | - Eli Stahl
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Martin Cheatle
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hang Zhou
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA
| | - Stephen G Waxman
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Xiao C, Wu X, Gallagher CS, Rasooly D, Jiang X, Morton CC. Genetic contribution of reproductive traits to risk of uterine leiomyomata: a large-scale, genome-wide, cross-trait analysis. Am J Obstet Gynecol 2024; 230:438.e1-438.e15. [PMID: 38191017 DOI: 10.1016/j.ajog.2023.12.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/03/2023] [Accepted: 12/26/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Although phenotypic associations between female reproductive characteristics and uterine leiomyomata have long been observed in epidemiologic investigations, the shared genetic architecture underlying these complex phenotypes remains unclear. OBJECTIVE We aimed to investigate the shared genetic basis, pleiotropic effects, and potential causal relationships underlying reproductive traits (age at menarche, age at natural menopause, and age at first birth) and uterine leiomyomata. STUDY DESIGN With the use of large-scale, genome-wide association studies conducted among women of European ancestry for age at menarche (n=329,345), age at natural menopause (n=201,323), age at first birth (n=418,758), and uterine leiomyomata (ncases/ncontrols=35,474/267,505), we performed a comprehensive, genome-wide, cross-trait analysis to examine systematically the common genetic influences between reproductive traits and uterine leiomyomata. RESULTS Significant global genetic correlations were identified between uterine leiomyomata and age at menarche (rg, -0.17; P=3.65×10-10), age at natural menopause (rg, 0.23; P=3.26×10-07), and age at first birth (rg, -0.16; P=1.96×10-06). Thirteen genomic regions were further revealed as contributing significant local correlations (P<.05/2353) to age at natural menopause and uterine leiomyomata. A cross-trait meta-analysis identified 23 shared loci, 3 of which were novel. A transcriptome-wide association study found 15 shared genes that target tissues of the digestive, exo- or endocrine, nervous, and cardiovascular systems. Mendelian randomization suggested causal relationships between a genetically predicted older age at menarche (odds ratio, 0.88; 95% confidence interval, 0.85-0.92; P=1.50×10-10) or older age at first birth (odds ratio, 0.95; 95% confidence interval, 0.90-0.99; P=.02) and a reduced risk for uterine leiomyomata and between a genetically predicted older age at natural menopause and an increased risk for uterine leiomyomata (odds ratio, 1.08; 95% confidence interval, 1.06-1.09; P=2.30×10-27). No causal association in the reverse direction was found. CONCLUSION Our work highlights that there are substantial shared genetic influences and putative causal links that underlie reproductive traits and uterine leiomyomata. The findings suggest that early identification of female reproductive risk factors may facilitate the initiation of strategies to modify potential uterine leiomyomata risk.
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Affiliation(s)
- Changfeng Xiao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | | | - Danielle Rasooly
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Solna, Stockholm, Sweden.
| | - Cynthia Casson Morton
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Manchester Centre for Audiology and Deafness, Manchester Academic Health Science Center, University of Manchester, Manchester, United Kingdom.
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48
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Mitra S, Malik R, Wong W, Rahman A, Hartemink AJ, Pritykin Y, Dey KK, Leslie CS. Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis. Nat Genet 2024; 56:627-636. [PMID: 38514783 PMCID: PMC11018525 DOI: 10.1038/s41588-024-01689-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024]
Abstract
We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.
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Affiliation(s)
- Sneha Mitra
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | | | - Wilfred Wong
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York City, NY, USA
| | - Afsana Rahman
- Hunter College, City University of New York, New York City, NY, USA
| | - Alexander J Hartemink
- Department of Computer Science, Duke University, Durham, NC, USA
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Yuri Pritykin
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Kushal K Dey
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
| | - Christina S Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
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49
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Lawrence JM, Breunig S, Foote IF, Tallis CB, Grotzinger AD. Genomic SEM applied to explore etiological divergences in bipolar subtypes. Psychol Med 2024; 54:1152-1159. [PMID: 37885278 DOI: 10.1017/s0033291723002957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
BACKGROUND Bipolar disorder (BD) is an overarching diagnostic class defined by the presence of at least one prior manic episode (BD I) or both a prior hypomanic episode and a prior depressive episode (BD II). Traditionally, BD II has been conceptualized as a less severe presentation of BD I, however, extant literature to investigate this claim has been mixed. METHODS We apply genomic structural equation modeling (Genomic SEM) to investigate divergent genetic pathways across BD's two major subtypes using the most recent GWAS summary statistics from the PGC. We begin by identifying divergences in genetic correlations across 98 external traits using a Bonferroni-corrected threshold. We also use a theoretically informed follow-up model to examine the extent to which the genetic variance in each subtype is explained by schizophrenia and major depression. Lastly, transcriptome-wide SEM (T-SEM) was used to identify neuronal gene expression patterns associated with BD subtypes. RESULTS BD II was characterized by significantly larger genetic overlap across non-psychiatric medical and internalizing traits (e.g. heart disease, neuroticism, insomnia), while stronger associations for BD I were absent. Consistent with these findings, follow-up modeling revealed a substantial major depression component for BD II. T-SEM results revealed 35 unique genes associated with shared risk across BD subtypes. CONCLUSIONS Divergent patterns of genetic relationships across external traits provide support for the distinction of the bipolar subtypes. However, our results also challenge the illness severity conceptualization of BD given stronger genetic overlap across BD II and a range of clinically relevant traits and disorders.
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Affiliation(s)
- Jeremy M Lawrence
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Sophie Breunig
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Isabelle F Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Connor B Tallis
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
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50
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Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, Green GS, Rozenblatt-Rosen O, Phillips D, Amir-Zilberstein L, Lee H, Pearse RV, Khan A, Vardarajan BN, Kiryluk K, Ye CJ, Klein HU, Wang G, Regev A, Habib N, Schneider JA, Wang Y, Young-Pearse T, Mostafavi S, Bennett DA, Menon V, De Jager PL. Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain. Nat Genet 2024; 56:605-614. [PMID: 38514782 DOI: 10.1038/s41588-024-01685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/08/2024] [Indexed: 03/23/2024]
Abstract
The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.
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Affiliation(s)
- Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Zongmei Gao
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gilad Sahar Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Devan Phillips
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Hyo Lee
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chun Jimmie Ye
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Gao Wang
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sara Mostafavi
- Department of Statistics, Centre for Molecular Medicine and Therapeutics, British Columbia Children's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
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