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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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2
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Benjamin KJM, Arora R, Feltrin AS, Pertea G, Giles HH, Stolz JM, D'Ignazio L, Collado-Torres L, Shin JH, Ulrich WS, Hyde TM, Kleinman JE, Weinberger DR, Paquola ACM, Erwin JA. Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus. Nat Commun 2024; 15:3980. [PMID: 38730231 PMCID: PMC11087501 DOI: 10.1038/s41467-024-48048-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: 11/21/2022] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University Krieger School of Arts & Sciences, Baltimore, MD, USA
| | | | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Hunter H Giles
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Laura D'Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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3
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Chen XF, Duan YY, Jia YY, Dong QH, Shi W, Zhang Y, Dong SS, Li M, Liu Z, Chen F, Huang XT, Hao RH, Zhu DL, Jing RH, Guo Y, Yang TL. Integrative high-throughput enhancer surveying and functional verification divulges a YY2-condensed regulatory axis conferring risk for osteoporosis. CELL GENOMICS 2024; 4:100501. [PMID: 38335956 PMCID: PMC10943593 DOI: 10.1016/j.xgen.2024.100501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/23/2023] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
The precise roles of chromatin organization at osteoporosis risk loci remain largely elusive. Here, we combined chromatin interaction conformation (Hi-C) profiling and self-transcribing active regulatory region sequencing (STARR-seq) to qualify enhancer activities of prioritized osteoporosis-associated single-nucleotide polymorphisms (SNPs). We identified 319 SNPs with biased allelic enhancer activity effect (baaSNPs) that linked to hundreds of candidate target genes through chromatin interactions across 146 loci. Functional characterizations revealed active epigenetic enrichment for baaSNPs and prevailing osteoporosis-relevant regulatory roles for their chromatin interaction genes. Further motif enrichment and network mapping prioritized several putative, key transcription factors (TFs) controlling osteoporosis binding to baaSNPs. Specifically, we selected one top-ranked TF and deciphered that an intronic baaSNP (rs11202530) could allele-preferentially bind to YY2 to augment PAPSS2 expression through chromatin interactions and promote osteoblast differentiation. Our results underline the roles of TF-mediated enhancer-promoter contacts for osteoporosis, which may help to better understand the intricate molecular regulatory mechanisms underlying osteoporosis risk loci.
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Affiliation(s)
- Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Ying-Ying Jia
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Qian-Hua Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Yan Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Meng Li
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Zhongbo Liu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, Shaanxi, China
| | - Fei Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Xiao-Ting Huang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Ruo-Han Hao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Rui-Hua Jing
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710000, Shaanxi, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China; Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
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4
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Habib M, Lalagkas PN, Melamed RD. Mapping drug biology to disease genetics to discover drug impacts on the human phenome. BIOINFORMATICS ADVANCES 2024; 4:vbae038. [PMID: 38736684 PMCID: PMC11087821 DOI: 10.1093/bioadv/vbae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/18/2024] [Accepted: 03/07/2024] [Indexed: 05/14/2024]
Abstract
Motivation Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects. Results Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine. Availability and implementation Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.
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Affiliation(s)
- Mamoon Habib
- Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
| | | | - Rachel D Melamed
- Department of Biological Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
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5
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Clay S, Alladina J, Smith NP, Visness CM, Wood RA, O'Connor GT, Cohen RT, Khurana Hershey GK, Kercsmar CM, Gruchalla RS, Gill MA, Liu AH, Kim H, Kattan M, Bacharier LB, Rastogi D, Rivera-Spoljaric K, Robison RG, Gergen PJ, Busse WW, Villani AC, Cho JL, Medoff BD, Gern JE, Jackson DJ, Ober C, Dapas M. Gene-based association study of rare variants in children of diverse ancestries implicates TNFRSF21 in the development of allergic asthma. J Allergy Clin Immunol 2024; 153:809-820. [PMID: 37944567 PMCID: PMC10939893 DOI: 10.1016/j.jaci.2023.10.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/25/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Most genetic studies of asthma and allergy have focused on common variation in individuals primarily of European ancestry. Studying the role of rare variation in quantitative phenotypes and in asthma phenotypes in populations of diverse ancestries can provide additional, important insights into the development of these traits. OBJECTIVE We sought to examine the contribution of rare variants to different asthma- or allergy-associated quantitative traits in children with diverse ancestries and explore their role in asthma phenotypes. METHODS We examined whole-genome sequencing data from children participants in longitudinal studies of asthma (n = 1035; parent-identified as 67% Black and 25% Hispanic) to identify rare variants (minor allele frequency < 0.01). We assigned variants to genes and tested for associations using an omnibus variant-set test between each of 24,902 genes and 8 asthma-associated quantitative traits. On combining our results with external data on predicted gene expression in humans and mouse knockout studies, we identified 3 candidate genes. A burden of rare variants in each gene and in a combined 3-gene score was tested for its associations with clinical phenotypes of asthma. Finally, published single-cell gene expression data in lower airway mucosal cells after allergen challenge were used to assess transcriptional responses to allergen. RESULTS Rare variants in USF1 were significantly associated with blood neutrophil count (P = 2.18 × 10-7); rare variants in TNFRSF21 with total IgE (P = 6.47 × 10-6) and PIK3R6 with eosinophil count (P = 4.10 × 10-5) reached suggestive significance. These 3 findings were supported by independent data from human and mouse studies. A burden of rare variants in TNFRSF21 and in a 3-gene score was associated with allergy-related phenotypes in cohorts of children with mild and severe asthma. Furthermore, TNFRSF21 was significantly upregulated in bronchial basal epithelial cells from adults with allergic asthma but not in adults with allergies (but not asthma) after allergen challenge. CONCLUSIONS We report novel associations between rare variants in genes and allergic and inflammatory phenotypes in children with diverse ancestries, highlighting TNFRSF21 as contributing to the development of allergic asthma.
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Affiliation(s)
- Selene Clay
- Department of Human Genetics, University of Chicago, Chicago, Ill.
| | - Jehan Alladina
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| | - Neal P Smith
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Mass; Massachusetts General Hospital Cancer Center, Boston, Mass
| | | | - Robert A Wood
- Pediatric Allergy and Immunology Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
| | - George T O'Connor
- Department of Pediatrics, Boston University School of Medicine, Boston, Mass
| | - Robyn T Cohen
- Department of Pediatrics, Boston University School of Medicine, Boston, Mass
| | | | - Carolyn M Kercsmar
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Rebecca S Gruchalla
- Internal Medicine and Pediatrics, University of Texas Southwestern Medical Center, Dallas, Tex
| | - Michelle A Gill
- Pediatric Infectious Diseases, St. Louis Children's Hospital, St Louis, Mo
| | - Andrew H Liu
- Breathing Institute, Children's Hospital Colorado, Aurora, Colo
| | - Haejin Kim
- Allergy and Immunology, Henry Ford Health, Detroit, Mich
| | - Meyer Kattan
- Department of Pediatrics, Columbia University Medical Center, New York, NY
| | - Leonard B Bacharier
- Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt University Medical Center, Nashville, Tenn
| | - Deepa Rastogi
- Division of Pulmonology and Sleep Medicine, Children's National Hospital, Washington, DC
| | - Katherine Rivera-Spoljaric
- Department of Pediatric Allergy, Immunology, and Pulmonary Medicine, Washington University School of Medicine, St Louis, Mo
| | - Rachel G Robison
- Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt University Medical Center, Nashville, Tenn; Ann & Robert H. Lurie Children's Hospital, Chicago, Ill
| | - Peter J Gergen
- National Institute of Allergy and Infectious Diseases, Rockville, Md
| | - William W Busse
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Alexandra-Chloe Villani
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Mass; Massachusetts General Hospital Cancer Center, Boston, Mass
| | - Josalyn L Cho
- Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Benjamin D Medoff
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| | - James E Gern
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Daniel J Jackson
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Ill
| | - Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, Ill
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Du M, Xin J, Zheng R, Yuan Q, Wang Z, Liu H, Liu H, Cai G, Albanes D, Lam S, Tardon A, Chen C, Bojesen SE, Landi MT, Johansson M, Risch A, Bickeböller H, Wichmann HE, Rennert G, Arnold S, Brennan P, Field JK, Shete SS, Le Marchand L, Liu G, Andrew AS, Kiemeney LA, Zienolddiny S, Grankvist K, Johansson M, Caporaso NE, Cox A, Hong YC, Yuan JM, Schabath MB, Aldrich MC, Wang M, Shen H, Chen F, Zhang Z, Hung RJ, Amos CI, Wei Q, Lazarus P, Christiani DC. CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility. Cancer Res 2024; 84:616-625. [PMID: 38117513 DOI: 10.1158/0008-5472.can-23-0900] [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: 03/22/2023] [Revised: 07/28/2023] [Accepted: 12/14/2023] [Indexed: 12/21/2023]
Abstract
Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18 × 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis. SIGNIFICANCE The causal pathway connecting CYP2A6 genetic variability and activity, cigarette consumption, and lung cancer susceptibility in smokers highlights the need for behavior modification interventions based on host susceptibility for cancer prevention.
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Affiliation(s)
- Mulong Du
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Junyi Xin
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Rui Zheng
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Qianyu Yuan
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Zhihui Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Hanting Liu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Guoshuai Cai
- Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, NCI, US NIH, Bethesda, Maryland
| | - Stephen Lam
- British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Faculty of Medicine, University of Oviedo, ISPA and CIBERESP, Oviedo, Spain
| | - Chu Chen
- Program in Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, NCI, US NIH, Bethesda, Maryland
| | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Angela Risch
- University of Salzburg and Cancer Cluster Salzburg, Salzburg, Austria
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
- German Center for Lung Research (DZL), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg August University Göttingen, Göttingen, Germany
| | - H-Erich Wichmann
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University, Munich, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Statistics and Epidemiology, Technical University of Munich, Munich, Germany
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Susanne Arnold
- Markey Cancer Center, University of Kentucky, Lexington, Kentucky
| | - Paul Brennan
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - John K Field
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Sanjay S Shete
- Department of Epidemiology, Division of Cancer Prevention and Population Science, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Geoffrey Liu
- Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Angeline S Andrew
- Norris Cotton Cancer Center, Geisel School of Medicine, Hanover, New Hampshire
| | | | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | | | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, NCI, US NIH, Bethesda, Maryland
| | - Angela Cox
- Department of Oncology, University of Sheffield, Sheffield, United Kingdom
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of South Korea
| | - Jian-Min Yuan
- UPMC Hillman Cancer Center and Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Melinda C Aldrich
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P.R. China
| | - Rayjean J Hung
- Lunenfeld-Tanenbuaum Research Institute, Sinai Health System, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor Medical College, Houston, Texas
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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7
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Shook MS, Lu X, Chen X, Parameswaran S, Edsall L, Trimarchi MP, Ernst K, Granitto M, Forney C, Donmez OA, Diouf AA, VonHandorf A, Rothenberg ME, Weirauch MT, Kottyan LC. Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis. Am J Hum Genet 2024; 111:280-294. [PMID: 38183988 PMCID: PMC10870143 DOI: 10.1016/j.ajhg.2023.12.008] [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] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/08/2024] Open
Abstract
Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation, that develops in a small subset of people with food allergies. Genome-wide association studies (GWASs) have identified 9 independent EoE risk loci reaching genome-wide significance (p < 5 × 10-8) and 27 additional loci of suggestive significance (5 × 10-8 < p < 1 × 10-5). In the current study, we perform linkage disequilibrium (LD) expansion of these loci to nominate a set of 531 variants that are potentially causal. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their genomic sequence context cloned into a GFP reporter library. Analysis of reporter gene expression in TE-7, HaCaT, and Jurkat cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants, representing 6 genome-wide significant EoE loci and 7 suggestive EoE loci, that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in individuals with EoE. Transcription factor enrichment analyses reveal possible roles for cell-type-specific regulators, including GATA3. Our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing functional insights into the effects of genetic variation in this disease.
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Affiliation(s)
- Molly S Shook
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Xiaoming Lu
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Sreeja Parameswaran
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lee Edsall
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Michael P Trimarchi
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Kevin Ernst
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Marissa Granitto
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Carmy Forney
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Omer A Donmez
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Arame A Diouf
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Andrew VonHandorf
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Marc E Rothenberg
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
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8
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Jung J, Lu Z, de Smith A, Mancuso N. Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study. Hum Mol Genet 2024; 33:170-181. [PMID: 37824084 PMCID: PMC10772041 DOI: 10.1093/hmg/ddad174] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/14/2023] [Accepted: 10/09/2023] [Indexed: 10/13/2023] Open
Abstract
Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.
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Affiliation(s)
- Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles, CA 90033, United States
| | - Adam de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles, CA 90033, United States
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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9
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Zeng H, Zhang W, Lin Q, Gao Y, Teng J, Xu Z, Cai X, Zhong Z, Wu J, Liu Y, Diao S, Wei C, Gong W, Pan X, Li Z, Huang X, Chen X, Du J. PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs. Nucleic Acids Res 2024; 52:D980-D989. [PMID: 37956339 PMCID: PMC10767803 DOI: 10.1093/nar/gkad1080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/13/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.
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Affiliation(s)
- Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Wenjing Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhanming Zhong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Yuqiang Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Wentao Gong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zedong Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoyu Huang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xifan Chen
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jinshi Du
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
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10
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Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [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: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
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Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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11
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Clifford R, Munro D, Dochtermann D, Devineni P, Pyarajan S, Telese F, Palmer AA, Mohammadi P, Friedman R. Genome-Wide Association Study of Chronic Dizziness in the Elderly Identifies Loci Implicating MLLT10, BPTF, LINC01224, and ROS1. J Assoc Res Otolaryngol 2023; 24:575-591. [PMID: 38036714 PMCID: PMC10752854 DOI: 10.1007/s10162-023-00917-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: 02/06/2023] [Accepted: 11/12/2023] [Indexed: 12/02/2023] Open
Abstract
PURPOSE Chronic age-related imbalance is a common cause of falls and subsequent death in the elderly and can arise from dysfunction of the vestibular system, an elegant neuroanatomical group of pathways that mediates human perception of acceleration, gravity, and angular head motion. Studies indicate that 27-46% of the risk of age-related chronic imbalance is genetic; nevertheless, the underlying genes remain unknown. METHODS The cohort consisted of 50,339 cases and 366,900 controls in the Million Veteran Program. The phenotype comprised cases with two ICD diagnoses of vertigo or dizziness at least 6 months apart, excluding acute or recurrent vertiginous syndromes and other non-vestibular disorders. Genome-wide association studies were performed as individual logistic regressions on European, African American, and Hispanic ancestries followed by trans-ancestry meta-analysis. Downstream analysis included case-case-GWAS, fine mapping, probabilistic colocalization of significant variants and genes with eQTLs, and functional analysis of significant hits. RESULTS Two significant loci were identified in Europeans, another in the Hispanic population, and two additional in trans-ancestry meta-analysis, including three novel loci. Fine mapping revealed credible sets of intronic single nucleotide polymorphisms (SNPs) in MLLT10 - a histone methyl transferase cofactor, BPTF - a subunit of a nucleosome remodeling complex implicated in neurodevelopment, and LINC01224 - a proto-oncogene receptor tyrosine kinase. CONCLUSION Despite the difficulties of phenotyping the nature of chronic imbalance, we replicated two loci from previous vertigo GWAS studies and identified three novel loci. Findings suggest candidates for further study and ultimate treatment of this common elderly disorder.
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Affiliation(s)
- Royce Clifford
- Department of Otolaryngology-Head and Neck Surgery, University of California San Diego, La Jolla, CA, 92093, USA.
- Research Dept, Veteran Administration Hospitals, San Diego, CA, 92161, USA.
| | - Daniel Munro
- Dept. of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Dept. of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92093, USA
| | - Daniel Dochtermann
- Veterans Administrations Hospitals, Million Veteran Program, Boston, MA, 02130, USA
| | - Poornima Devineni
- Veterans Administrations Hospitals, Million Veteran Program, Boston, MA, 02130, USA
| | - Saiju Pyarajan
- Veterans Administrations Hospitals, Million Veteran Program, Boston, MA, 02130, USA
| | - Francesca Telese
- Dept. of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Abraham A Palmer
- Dept. of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, 98101, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Rick Friedman
- Department of Otolaryngology-Head and Neck Surgery, University of California San Diego, La Jolla, CA, 92093, USA
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12
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Wei J, Lambert TY, Valada A, Patel N, Walker K, Lenders J, Schmidt CJ, Iskhakova M, Alazizi A, Mair-Meijers H, Mash DC, Luca F, Pique-Regi R, Bannon MJ, Akbarian S. Single nucleus transcriptomics of ventral midbrain identifies glial activation associated with chronic opioid use disorder. Nat Commun 2023; 14:5610. [PMID: 37699936 PMCID: PMC10497570 DOI: 10.1038/s41467-023-41455-8] [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: 03/06/2023] [Accepted: 09/05/2023] [Indexed: 09/14/2023] Open
Abstract
Dynamic interactions of neurons and glia in the ventral midbrain mediate reward and addiction behavior. We studied gene expression in 212,713 ventral midbrain single nuclei from 95 individuals with history of opioid misuse, and individuals without drug exposure. Chronic exposure to opioids was not associated with change in proportions of glial and neuronal subtypes, however glial transcriptomes were broadly altered, involving 9.5 - 6.2% of expressed genes within microglia, oligodendrocytes, and astrocytes. Genes associated with activation of the immune response including interferon, NFkB signaling, and cell motility pathways were upregulated, contrasting with down-regulated expression of synaptic signaling and plasticity genes in ventral midbrain non-dopaminergic neurons. Ventral midbrain transcriptomic reprogramming in the context of chronic opioid exposure included 325 genes that previous genome-wide studies had linked to risk of substance use traits in the broader population, thereby pointing to heritable risk architectures in the genomic organization of the brain's reward circuitry.
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Affiliation(s)
- Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Tova Y Lambert
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aditi Valada
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nikhil Patel
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Kellie Walker
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Jayna Lenders
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Carl J Schmidt
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Marina Iskhakova
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Henriette Mair-Meijers
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Deborah C Mash
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, 48201, USA
- Department of Biology, University of Tor Vergata, Rome, 00133, Italy
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, 48201, USA
| | - Michael J Bannon
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Schahram Akbarian
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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13
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Pividori M, Lu S, Li B, Su C, Johnson ME, Wei WQ, Feng Q, Namjou B, Kiryluk K, Kullo IJ, Luo Y, Sullivan BD, Voight BF, Skarke C, Ritchie MD, Grant SFA, Greene CS. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nat Commun 2023; 14:5562. [PMID: 37689782 PMCID: PMC10492839 DOI: 10.1038/s41467-023-41057-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 08/18/2023] [Indexed: 09/11/2023] Open
Abstract
Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.
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Affiliation(s)
- Milton Pividori
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, 10032, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, 60611, USA
| | - Blair D Sullivan
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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14
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Gregga I, Pharoah PDP, Gayther SA, Manichaikul A, Im HK, Kar SP, Schildkraut JM, Wheeler HE. Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility. Cancer Epidemiol Biomarkers Prev 2023; 32:1198-1207. [PMID: 37409955 PMCID: PMC10528410 DOI: 10.1158/1055-9965.epi-23-0309] [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: 03/28/2023] [Revised: 05/30/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility. METHODS We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis. RESULTS Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62). CONCLUSIONS We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect. IMPACT PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.
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Affiliation(s)
- Isabelle Gregga
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Paul D. P. Pharoah
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Simon A. Gayther
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
| | - Siddhartha P. Kar
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Joellen M. Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
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15
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Hiam D, Jones P, Pitsiladis Y, Eynon N. Genomics and Biology of Exercise, Where Are We Now? Clin J Sport Med 2023; 33:e112-e114. [PMID: 37656977 DOI: 10.1097/jsm.0000000000001012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Affiliation(s)
- Danielle Hiam
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
- School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition (IPAN), Deakin University, Geelong, Australia; and
| | - Patrice Jones
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
| | - Yannis Pitsiladis
- School of Sport and Health Sciences, University of Brighton, Eastbourne, United Kingdom
| | - Nir Eynon
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
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16
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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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17
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Schlosser P, Zhang J, Liu H, Surapaneni AL, Rhee EP, Arking DE, Yu B, Boerwinkle E, Welling PA, Chatterjee N, Susztak K, Coresh J, Grams ME. Transcriptome- and proteome-wide association studies nominate determinants of kidney function and damage. Genome Biol 2023; 24:150. [PMID: 37365616 DOI: 10.1186/s13059-023-02993-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND The pathophysiological causes of kidney disease are not fully understood. Here we show that the integration of genome-wide genetic, transcriptomic, and proteomic association studies can nominate causal determinants of kidney function and damage. RESULTS Through transcriptome-wide association studies (TWAS) in kidney cortex, kidney tubule, liver, and whole blood and proteome-wide association studies (PWAS) in plasma, we assess for effects of 12,893 genes and 1342 proteins on kidney filtration (glomerular filtration rate (GFR) estimated by creatinine; GFR estimated by cystatin C; and blood urea nitrogen) and kidney damage (albuminuria). We find 1561 associations distributed among 260 genomic regions that are supported as putatively causal. We then prioritize 153 of these genomic regions using additional colocalization analyses. Our genome-wide findings are supported by existing knowledge (animal models for MANBA, DACH1, SH3YL1, INHBB), exceed the underlying GWAS signals (28 region-trait combinations without significant GWAS hit), identify independent gene/protein-trait associations within the same genomic region (INHBC, SPRYD4), nominate tissues underlying the associations (tubule expression of NRBP1), and distinguish markers of kidney filtration from those with a role in creatinine and cystatin C metabolism. Furthermore, we follow up on members of the TGF-beta superfamily of proteins and find a prognostic value of INHBC for kidney disease progression even after adjustment for measured glomerular filtration rate (GFR). CONCLUSION In summary, this study combines multimodal, genome-wide association studies to generate a catalog of putatively causal target genes and proteins relevant to kidney function and damage which can guide follow-up studies in physiology, basic science, and clinical medicine.
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Affiliation(s)
- Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hongbo Liu
- Department of Medicine and Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aditya L Surapaneni
- Welch Center for Prevention Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bing Yu
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Paul A Welling
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Katalin Susztak
- Department of Medicine and Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY, USA
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18
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Huffman JE, Nicolas J, Hahn J, Heath AS, Raffield LM, Yanek LR, Brody JA, Thibord F, Almasy L, Bartz TM, Bielak LF, Bowler RP, Carrasquilla GD, Chasman DI, Chen MH, Emmert DB, Ghanbari M, Haessle J, Hottenga JJ, Kleber ME, Le NQ, Lee J, Lewis JP, Li-Gao R, Luan J, Malmberg A, Mangino M, Marioni RE, Martinez-Perez A, Pankratz N, Polasek O, Richmond A, Rodriguez BA, Rotter JI, Steri M, Suchon P, Trompet S, Weiss S, Zare M, Auer P, Cho MH, Christofidou P, Davies G, de Geus E, Deleuze JF, Delgado GE, Ekunwe L, Faraday N, Gögele M, Greinacher A, He G, Howard T, Joshi PK, Kilpeläinen TO, Lahti J, Linneberg A, Naitza S, Noordam R, Paüls-Vergés F, Rich SS, Rosendaal FR, Rudan I, Ryan KA, Souto JC, van Rooij FJ, Wang H, Zhao W, Becker LC, Beswick A, Brown MR, Cade BE, Campbell H, Cho K, Crapo JD, Curran JE, de Maat MP, Doyle M, Elliott P, Floyd JS, Fuchsberger C, Grarup N, Guo X, Harris SE, Hou L, Kolcic I, Kooperberg C, Menni C, Nauck M, O'Connell JR, Orrù V, Psaty BM, Räikkönen K, Smith JA, Soria JM, Stott DJ, van Hylckama Vlieg A, Watkins H, Willemsen G, Wilson P, Ben-Shlomo Y, Blangero J, Boomsma D, Cox SR, Dehghan A, Eriksson JG, Fiorillo E, Fornage M, Hansen T, Hayward C, Ikram MA, Jukema JW, Kardia SL, Lange LA, März W, Mathias RA, Mitchell BD, Mook-Kanamori DO, Morange PE, Pedersen O, Pramstaller PP, Redline S, Reiner A, Ridker PM, Silverman EK, Spector TD, Völker U, Wareham N, Wilson JF, Yao J, Trégouët DA, Johnson AD, Wolberg AS, de Vries PS, Sabater-Lleal M, Morrison AC, Smith NL. Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.07.23291095. [PMID: 37398003 PMCID: PMC10312878 DOI: 10.1101/2023.06.07.23291095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( SERPINA1, ZFP36L2 , and TLR10) signals contain predicted deleterious missense variants. Two loci, SOCS3 and HPN , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( FGG;FGB;FGA ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation. Key Points Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.
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19
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Nikpay M. Genome-wide search identified DNA methylation sites that regulate the metabolome. Front Genet 2023; 14:1093882. [PMID: 37274792 PMCID: PMC10233745 DOI: 10.3389/fgene.2023.1093882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 05/09/2023] [Indexed: 06/07/2023] Open
Abstract
Background: Identifying DNA methylation sites that regulate the metabolome is important for several purposes. In this study, publicly available GWAS data were integrated to find methylation sites that impact metabolome through a discovery and replication scheme and by using Mendelian randomization. Results: The outcome of analyses revealed 107 methylation sites associated with 84 metabolites at the genome-wide significance level (p<5e-8) at both the discovery and replication stages. A large percentage of the observed associations (85%) were with lipids, significantly higher than expected (p = 0.0003). A number of CpG (methylation) sites showed specificity e.g., cg20133200 within PFKP was associated with glucose only and cg10760299 within GATM impacted the level of creatinine; in contrast, there were sites associated with numerous metabolites e.g., cg20102877 on the 2p23.3 region was associated with 39 metabolites. Integrating transcriptome data enabled identifying genes (N = 82) mediating the impact of methylation sites on the metabolome and cardiometabolic traits. For example, PABPC4 mediated the impact of cg15123755-HDL on type-2 diabetes. KCNK7 mediated the impact of cg21033440-lipids on hypertension. POC5, ILRUN, FDFT1, and NEIL2 mediated the impact of CpG sites on obesity through metabolic pathways. Conclusion: This study provides a catalog of DNA methylation sites that regulate the metabolome for downstream applications.
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20
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Ursini G, Di Carlo P, Mukherjee S, Chen Q, Han S, Kim J, Deyssenroth M, Marsit CJ, Chen J, Hao K, Punzi G, Weinberger DR. Prioritization of potential causative genes for schizophrenia in placenta. Nat Commun 2023; 14:2613. [PMID: 37188697 PMCID: PMC10185564 DOI: 10.1038/s41467-023-38140-1] [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: 08/09/2022] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone.
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Affiliation(s)
- Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Pasquale Di Carlo
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Sreya Mukherjee
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Jiyoung Kim
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Maya Deyssenroth
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Carmen J Marsit
- Departments of Environmental Health and Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jia Chen
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Giovanna Punzi
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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21
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Han SK, McNulty MT, Benway CJ, Wen P, Greenberg A, Onuchic-Whitford AC, Jang D, Flannick J, Burtt NP, Wilson PC, Humphreys BD, Wen X, Han Z, Lee D, Sampson MG. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat Commun 2023; 14:2229. [PMID: 37076491 PMCID: PMC10115815 DOI: 10.1038/s41467-023-37691-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 03/27/2023] [Indexed: 04/21/2023] Open
Abstract
Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.
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Affiliation(s)
- Seong Kyu Han
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Michelle T McNulty
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Christopher J Benway
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Pei Wen
- Center for Precision Disease Modeling, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Anya Greenberg
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Ana C Onuchic-Whitford
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jason Flannick
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Noël P Burtt
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Parker C Wilson
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xiaoquan Wen
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhe Han
- Center for Precision Disease Modeling, University of Maryland, School of Medicine, Baltimore, MD, USA.
| | - Dongwon Lee
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA.
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA, USA.
| | - Matthew G Sampson
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA.
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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22
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Jung J, Lu Z, de Smith A, Mancuso N. Novel insight into the etiology of ischemic stroke gained by integrative transcriptome-wide association study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.30.23287918. [PMID: 37034585 PMCID: PMC10081428 DOI: 10.1101/2023.03.30.23287918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N=1,296,908) for IS together with mRNA, splicing, enhancer RNA (eRNA) and protein expression data (N=11,588) from 50 tissues. We identify 136 genes/eRNA/proteins associated with IS risk across 54 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.
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Affiliation(s)
- Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Biostatistics Division, 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, USA
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23
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Wei J, Lambert TY, Valada A, Patel N, Walker K, Lenders J, Schmidt CJ, Iskhakova M, Alazizi A, Mair-Meijers H, Mash DC, Luca F, Pique-Regi R, Bannon MJ, Akbarian S. Single Nucleus Transcriptomics Reveals Pervasive Glial Activation in Opioid Overdose Cases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531400. [PMID: 36945611 PMCID: PMC10028861 DOI: 10.1101/2023.03.07.531400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Dynamic interactions of neurons and glia in the ventral midbrain (VM) mediate reward and addiction behavior. We studied gene expression in 212,713 VM single nuclei from 95 human opioid overdose cases and drug-free controls. Chronic exposure to opioids left numerical proportions of VM glial and neuronal subtypes unaltered, while broadly affecting glial transcriptomes, involving 9.5 - 6.2% of expressed genes within microglia, oligodendrocytes, and astrocytes, with prominent activation of the immune response including interferon, NFkB signaling, and cell motility pathways, sharply contrasting with down-regulated expression of synaptic signaling and plasticity genes in VM non-dopaminergic neurons. VM transcriptomic reprogramming in the context of opioid exposure and overdose included 325 genes with genetic variation linked to substance use traits in the broader population, thereby pointing to heritable risk architectures in the genomic organization of the brain's reward circuitry.
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Affiliation(s)
- Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201
| | - Tova Y. Lambert
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Aditi Valada
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Nikhil Patel
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI 48201
| | - Kellie Walker
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI 48201
| | - Jayna Lenders
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI 48201
| | - Carl J. Schmidt
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI 48109
| | - Marina Iskhakova
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201
| | - Henriette Mair-Meijers
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201
| | - Deborah C. Mash
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL 33136
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201
- Department of Biology, University of Tor Vergata, Rome, Italy, 00133
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201
| | - Michael J Bannon
- Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI 48201
| | - Schahram Akbarian
- Department of Psychiatry, Department of Neuroscience and Department of Genetics and Genomic Sciences, Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY 10029
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24
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Hemerich D, Smit RAJ, Preuss M, Stalbow L, van der Laan SW, Asselbergs FW, van Setten J, Tragante V. Effect of tissue-grouped regulatory variants associated to type 2 diabetes in related secondary outcomes. Sci Rep 2023; 13:3579. [PMID: 36864090 PMCID: PMC9981672 DOI: 10.1038/s41598-023-30369-6] [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: 06/29/2022] [Accepted: 02/21/2023] [Indexed: 03/04/2023] Open
Abstract
Genome-wide association studies have identified over five hundred loci that contribute to variation in type 2 diabetes (T2D), an established risk factor for many diseases. However, the mechanisms and extent through which these loci contribute to subsequent outcomes remain elusive. We hypothesized that combinations of T2D-associated variants acting on tissue-specific regulatory elements might account for greater risk for tissue-specific outcomes, leading to diversity in T2D disease progression. We searched for T2D-associated variants acting on regulatory elements and expression quantitative trait loci (eQTLs) in nine tissues. We used T2D tissue-grouped variant sets as genetic instruments to conduct 2-Sample Mendelian Randomization (MR) in ten related outcomes whose risk is increased by T2D using the FinnGen cohort. We performed PheWAS analysis to investigate whether the T2D tissue-grouped variant sets had specific predicted disease signatures. We identified an average of 176 variants acting in nine tissues implicated in T2D, and an average of 30 variants acting on regulatory elements that are unique to the nine tissues of interest. In 2-Sample MR analyses, all subsets of regulatory variants acting in different tissues were associated with increased risk of the ten secondary outcomes studied on similar levels. No tissue-grouped variant set was associated with an outcome significantly more than other tissue-grouped variant sets. We did not identify different disease progression profiles based on tissue-specific regulatory and transcriptome information. Bigger sample sizes and other layers of regulatory information in critical tissues may help identify subsets of T2D variants that are implicated in certain secondary outcomes, uncovering system-specific disease progression.
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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
| | - Roelof A J Smit
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren Stalbow
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Jessica van Setten
- Department of Cardiology, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Vinicius Tragante
- Department of Cardiology, UMC Utrecht, Utrecht University, Utrecht, The Netherlands.
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25
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Lyle SM, Ahmed S, Elliott JE, Stener-Victorin E, Nachtigal MW, Drögemöller BI. Transcriptome-wide association analyses identify an association between ARL14EP and polycystic ovary syndrome. J Hum Genet 2023; 68:347-353. [PMID: 36720993 DOI: 10.1038/s10038-023-01120-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 02/02/2023]
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine disorder, which is accompanied by a variety of comorbidities including metabolic, reproductive, and psychiatric disorders. Genome-wide association studies have identified several genetic variants that are associated with PCOS. However, these variants often occur outside of coding regions and require further investigation to understand their contribution to PCOS. A transcriptome-wide association study (TWAS) was performed to uncover heritable gene expression profiles that are associated with PCOS in two independent cohorts. Causal gene prioritization was subsequently performed and expression of genes prioritized through these analyses was examined in 49 PCOS patients and 30 controls. TWAS analyses revealed that increased expression of ARL14EP was significantly associated with PCOS risk in the discovery (P = 1.6 × 10-6) and replication cohorts (P = 2.0 × 10-13). Gene prioritization pipelines provided further evidence that ARL14EP is the most likely causal gene at this locus. ARL14EP gene expression was shown to be significantly different between PCOS cases and controls, after adjusting for body mass index, age and testosterone levels (P = 1.2 × 10-13). This study has provided evidence for the role of ARL14EP in PCOS. Given that ARL14EP has been reported to play an important role in chromatin remodeling, variants affecting the expression of ARL14EP may also affect the expression of other genes that contribute to PCOS pathogenesis.
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Affiliation(s)
- Sarah M Lyle
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Samah Ahmed
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Jason E Elliott
- Department of Obstetrics, Gynecology and Reproductive Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
| | | | - Mark W Nachtigal
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Department of Obstetrics, Gynecology and Reproductive Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada
| | - Britt I Drögemöller
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada. .,Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada. .,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada.
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26
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Okamoto J, Wang L, Yin X, Luca F, Pique-Regi R, Helms A, Im HK, Morrison J, Wen X. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 2023; 110:44-57. [PMID: 36608684 PMCID: PMC9892769 DOI: 10.1016/j.ajhg.2022.12.002] [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: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 01/07/2023] Open
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
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Affiliation(s)
- Jeffrey Okamoto
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Adam Helms
- University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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27
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Oliva M, Demanelis K, Lu Y, Chernoff M, Jasmine F, Ahsan H, Kibriya MG, Chen LS, Pierce BL. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 2023; 55:112-122. [PMID: 36510025 PMCID: PMC10249665 DOI: 10.1038/s41588-022-01248-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/26/2022] [Indexed: 12/14/2022]
Abstract
Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.
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Affiliation(s)
- Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Kathryn Demanelis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yihao Lu
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Meytal Chernoff
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Farzana Jasmine
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA
- Department of Medicine, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
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28
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Bonaguro L, Schulte-Schrepping J, Carraro C, Sun LL, Reiz B, Gemünd I, Saglam A, Rahmouni S, Georges M, Arts P, Hoischen A, Joosten LA, van de Veerdonk FL, Netea MG, Händler K, Mukherjee S, Ulas T, Schultze JL, Aschenbrenner AC. Human variation in population-wide gene expression data predicts gene perturbation phenotype. iScience 2022; 25:105328. [PMID: 36310583 PMCID: PMC9614568 DOI: 10.1016/j.isci.2022.105328] [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: 03/02/2022] [Revised: 07/13/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.
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Affiliation(s)
- Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Laura L. Sun
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | | | - Ioanna Gemünd
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, 3010 VIC, Australia
| | - Adem Saglam
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Peer Arts
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, 5000 SA, Australia
| | - Alexander Hoischen
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Leo A.B. Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Medical Genetics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Frank L. van de Veerdonk
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Immunology and Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Kristian Händler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Anna C. Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
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29
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Benjamin KJM, Chen Q, Jaffe AE, Stolz JM, Collado-Torres L, Huuki-Myers LA, Burke EE, Arora R, Feltrin AS, Barbosa AR, Radulescu E, Pergola G, Shin JH, Ulrich WS, Deep-Soboslay A, Tao R, Hyde TM, Kleinman JE, Erwin JA, Weinberger DR, Paquola ACM. Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes. Nat Neurosci 2022; 25:1559-1568. [PMID: 36319771 PMCID: PMC10599288 DOI: 10.1038/s41593-022-01182-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 09/13/2022] [Indexed: 11/06/2022]
Abstract
Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum's dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Emily E Burke
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Arthur S Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Mathematics, Computation and Cognition, Federal University of ABC, Santo André, Brazil
| | - André Rocha Barbosa
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Inter-Institutional Graduate Program on Bioinformatics, University of São Paulo, São Paulo, Brazil
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | | | - Giulio Pergola
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Ran Tao
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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30
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Yang Z, Wang J, Huang Y, Wang S, Wei L, Liu D, Weng Y, Xiang J, Zhu Q, Yang Z, Nie X, Yu Y, Yang Z, Yang QY. CottonMD: a multi-omics database for cotton biological study. Nucleic Acids Res 2022; 51:D1446-D1456. [PMID: 36215030 PMCID: PMC9825545 DOI: 10.1093/nar/gkac863] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/08/2022] [Accepted: 09/24/2022] [Indexed: 01/30/2023] Open
Abstract
Cotton is an important economic crop, and many loci for important traits have been identified, but it remains challenging and time-consuming to identify candidate or causal genes/variants and clarify their roles in phenotype formation and regulation. Here, we first collected and integrated the multi-omics datasets including 25 genomes, transcriptomes in 76 tissue samples, epigenome data of five species and metabolome data of 768 metabolites from four tissues, and genetic variation, trait and transcriptome datasets from 4180 cotton accessions. Then, a cotton multi-omics database (CottonMD, http://yanglab.hzau.edu.cn/CottonMD/) was constructed. In CottonMD, multiple statistical methods were applied to identify the associations between variations and phenotypes, and many easy-to-use analysis tools were provided to help researchers quickly acquire the related omics information and perform multi-omics data analysis. Two case studies demonstrated the power of CottonMD for identifying and analyzing the candidate genes, as well as the great potential of integrating multi-omics data for cotton genetic breeding and functional genomics research.
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Affiliation(s)
| | | | | | - Shengbo Wang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lulu Wei
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dongxu Liu
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yonglin Weng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinhai Xiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiang Zhu
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhaoen Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Xinhui Nie
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Bingtuan, Agricultural College, Shihezi University, Shihezi, Xinjiang 832000, China
| | - Yu Yu
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, Xinjiang 832000, China
| | - Zuoren Yang
- Correspondence may also be addressed to Zuoren Yang. Tel: +86 371 55912660;
| | - Qing-Yong Yang
- To whom correspondence should be addressed. Tel: +86 27 87288509;
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31
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Pérez-Granado J, Piñero J, Furlong LI. Benchmarking post-GWAS analysis tools in major depression: Challenges and implications. Front Genet 2022; 13:1006903. [PMID: 36276939 PMCID: PMC9579284 DOI: 10.3389/fgene.2022.1006903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/20/2022] [Indexed: 12/05/2022] Open
Abstract
Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.
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Affiliation(s)
- Judith Pérez-Granado
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
- *Correspondence: Laura I. Furlong,
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32
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LaPierre MP, Lawler K, Godbersen S, Farooqi IS, Stoffel M. MicroRNA-7 regulates melanocortin circuits involved in mammalian energy homeostasis. Nat Commun 2022; 13:5733. [PMID: 36175420 PMCID: PMC9522793 DOI: 10.1038/s41467-022-33367-w] [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: 02/07/2022] [Accepted: 09/14/2022] [Indexed: 11/09/2022] Open
Abstract
MicroRNAs (miRNAs) modulate physiological responses by repressing the expression of gene networks. We found that global deletion of microRNA-7 (miR-7), the most enriched miRNA in the hypothalamus, causes obesity in mice. Targeted deletion of miR-7 in Single-minded homolog 1 (Sim1) neurons, a critical component of the hypothalamic melanocortin pathway, causes hyperphagia, obesity and increased linear growth, mirroring Sim1 and Melanocortin-4 receptor (MC4R) haplo-insufficiency in mice and humans. We identified Snca (α-Synuclein) and Igsf8 (Immunoglobulin Superfamily Member 8) as miR-7 target genes that act in Sim1 neurons to regulate body weight and endocrine axes. In humans, MIR-7-1 is located in the last intron of HNRNPK, whose promoter drives the expression of both genes. Genetic variants at the HNRNPK locus that reduce its expression are associated with increased height and truncal fat mass. These findings demonstrate that miR-7 suppresses gene networks involved in the hypothalamic melanocortin pathway to regulate mammalian energy homeostasis.
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Affiliation(s)
- Mary P LaPierre
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland
| | - Katherine Lawler
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Svenja Godbersen
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Markus Stoffel
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland. .,Medical Faculty, University of Zürich, 8091, Zürich, Switzerland.
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33
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Rocheleau G, Forrest IS, Duffy Á, Bafna S, Dobbyn A, Verbanck M, Won HH, Jordan DM, Do R. A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank. Commun Biol 2022; 5:849. [PMID: 35987940 PMCID: PMC9392744 DOI: 10.1038/s42003-022-03820-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries.
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Affiliation(s)
- Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA
| | - Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA
| | - Shantanu Bafna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Amanda Dobbyn
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Marie Verbanck
- UR 7537 - BioSTM, Biostatistique, Traitement et Modélisation des données biologiques, Faculté de Pharmacie de Paris, Université de Paris, 4 avenue de l'Observatoire, 75270, Paris, France
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY, 10029, USA.
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34
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Mizikovsky D, Naval Sanchez M, Nefzger CM, Cuellar Partida G, Palpant NJ. Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations. Nucleic Acids Res 2022; 50:e87. [PMID: 35716123 PMCID: PMC9410900 DOI: 10.1093/nar/gkac413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/28/2022] [Accepted: 05/09/2022] [Indexed: 12/28/2022] Open
Abstract
Genome wide association studies provide statistical measures of gene–trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein–protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.
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Affiliation(s)
- Dalia Mizikovsky
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Marina Naval Sanchez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Christian M Nefzger
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | | | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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35
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Hu X, Qiao D, Kim W, Moll M, Balte PP, Lange LA, Bartz TM, Kumar R, Li X, Yu B, Cade BE, Laurie CA, Sofer T, Ruczinski I, Nickerson DA, Muzny DM, Metcalf GA, Doddapaneni H, Gabriel S, Gupta N, Dugan-Perez S, Cupples LA, Loehr LR, Jain D, Rotter JI, Wilson JG, Psaty BM, Fornage M, Morrison AC, Vasan RS, Washko G, Rich SS, O'Connor GT, Bleecker E, Kaplan RC, Kalhan R, Redline S, Gharib SA, Meyers D, Ortega V, Dupuis J, London SJ, Lappalainen T, Oelsner EC, Silverman EK, Barr RG, Thornton TA, Wheeler HE, Cho MH, Im HK, Manichaikul A. Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program. Am J Hum Genet 2022; 109:857-870. [PMID: 35385699 PMCID: PMC9118106 DOI: 10.1016/j.ajhg.2022.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/04/2022] [Indexed: 12/17/2022] Open
Abstract
While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.
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Affiliation(s)
- Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Pallavi P Balte
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Rajesh Kumar
- Division of Allergy and Clinical Immunology, Ann and Robert H. Lurie Children's Hospital, Chicago, IL 60611, USA; Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xingnan Li
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Donna M Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger A Metcalf
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Stacy Gabriel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shannon Dugan-Perez
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura R Loehr
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA 98195, 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 90502, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA 98101, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ramachandran S Vasan
- Boston University and the National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA; Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA
| | - George Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - George T O'Connor
- Pulmonary Center, Boston University, School of Medicine, Boston, MA 02118, USA
| | - Eugene Bleecker
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Ravi Kalhan
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sina A Gharib
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA 98109, USA
| | - Deborah Meyers
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Victor Ortega
- Pulmonary and Critical Care, School of Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Durham, NC 27709, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Elizabeth C Oelsner
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | | | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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36
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Hukku A, Sampson MG, Luca F, Pique-Regi R, Wen X. Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility. Am J Hum Genet 2022; 109:825-837. [PMID: 35523146 PMCID: PMC9118134 DOI: 10.1016/j.ajhg.2022.04.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/04/2022] [Indexed: 11/29/2022] Open
Abstract
Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.
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Affiliation(s)
- Abhay Hukku
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Matthew G Sampson
- Division of Nephrology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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37
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An analysis of genetically regulated gene expression and the role of co-expression networks across 16 psychiatric and substance use phenotypes. Eur J Hum Genet 2022; 30:560-566. [PMID: 35217801 PMCID: PMC9090912 DOI: 10.1038/s41431-022-01037-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified thousands of risk loci for psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (GWAS sample size range, N = 9725-807,553) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation of genetically regulated expression between phenotype pairs, and compared the results with the genetic correlations. We identified 393 genes with at least one significant phenotype association, comprising 458 significant associations across 16 phenotypes. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, had significantly higher transcriptomic correlation (r = 0.84) than genetic correlation (r = 0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from human prefrontal cortex samples. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.
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38
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Crouse WL, Das SK, Le T, Keele G, Holl K, Seshie O, Craddock A, Sharma NK, Comeau ME, Langefeld C, Hawkins GA, Mott R, Valdar W, Solberg Woods LC. Transcriptome-wide analyses of adipose tissue in outbred rats reveal genetic regulatory mechanisms relevant for human obesity. Physiol Genomics 2022; 54:206-219. [PMID: 35467982 DOI: 10.1152/physiolgenomics.00172.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene co-expression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men". We used weighted gene co-expression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight co-expressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b) or no previously known role in obesity (e.g., St14, Msa4a6). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.
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Affiliation(s)
- Wesley L Crouse
- University of North Carolina at Chapel Hill, Department of Genetics, Chapel Hill, NC, United States
| | - Swapan Kumar Das
- Wake Forest University School of Medicine, Department of Internal Medicine, Winston Salem, NC, United States
| | - Thu Le
- University College London, Department of Genetics, Evolution and Environment, Division of Biosciences, London, United Kingdom
| | - Gregory Keele
- Jackson Laboratories, Roux Center for Genomics and Computational Biology, Bar Harbor, ME, United States
| | - Katie Holl
- Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, United States
| | - Osborne Seshie
- Wake Forest University School of Medicine, Department of Internal Medicine, Winston Salem, NC, United States
| | - Ann Craddock
- Wake Forest University School of Medicine, Department of Biochemistry, Winston Salem, NC, United States
| | - Neeraj Kumar Sharma
- Wake Forest University School of Medicine, Department of Internal Medicine, Winston Salem, NC, United States
| | - Mary Elizabeth Comeau
- Wake Forest University School of Medicine, Department of Biostatistics and Data Sciences, Winston Salem, NC, United States
| | - Carl Langefeld
- Wake Forest University School of Medicine, Department of Biostatistics and Data Sciences, Winston Salem, NC, United States
| | - Gregory A Hawkins
- Wake Forest University School of Medicine, Department of Biochemistry, Winston Salem, NC, United States
| | - Richard Mott
- University College London, Department of Genetics, Evolution and Environment, Division of Biosciences, London, United Kingdom
| | - William Valdar
- University of North Carolina at Chapel Hill, Department of Genetics, Chapel Hill, NC, United States
| | - Leah C Solberg Woods
- Wake Forest University School of Medicine, Department of Internal Medicine, Winston Salem, NC, United States
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39
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Pathak GA, Singh K, Wendt FR, Fleming TW, Overstreet C, Koller D, Tylee DS, De Angelis F, Cabrera Mendoza B, Levey DF, Koenen KC, Krystal JH, Pietrzak RH, O' Donell C, Gaziano JM, Falcone G, Stein MB, Gelernter J, Pasaniuc B, Mancuso N, Davis LK, Polimanti R. Genetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits. Mol Psychiatry 2022; 27:1394-1404. [PMID: 35241783 PMCID: PMC9210390 DOI: 10.1038/s41380-022-01488-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/03/2022] [Accepted: 02/14/2022] [Indexed: 12/15/2022]
Abstract
Posttraumatic stress disorder (PTSD) is a psychiatric disorder that may arise in response to severe traumatic event and is diagnosed based on three main symptom clusters (reexperiencing, avoidance, and hyperarousal) per the Diagnostic Manual of Mental Disorders (version DSM-IV-TR). In this study, we characterized the biological heterogeneity of PTSD symptom clusters by performing a multi-omics investigation integrating genetically regulated gene, splicing, and protein expression in dorsolateral prefrontal cortex tissue within a sample of US veterans enrolled in the Million Veteran Program (N total = 186,689). We identified 30 genes in 19 regions across the three PTSD symptom clusters. We found nine genes to have cell-type specific expression, and over-representation of miRNA-families - miR-148, 30, and 8. Gene-drug target prioritization approach highlighted cyclooxygenase and acetylcholine compounds. Next, we tested molecular-profile based phenome-wide impact of identified genes with respect to 1678 phenotypes derived from the Electronic Health Records of the Vanderbilt University biorepository (N = 70,439). Lastly, we tested for local genetic correlation across PTSD symptom clusters which highlighted metabolic (e.g., obesity, diabetes, vascular health) and laboratory traits (e.g., neutrophil, eosinophil, tau protein, creatinine kinase). Overall, this study finds comprehensive genomic evidence including clinical and regulatory profiles between PTSD, hematologic and cardiometabolic traits, that support comorbidities observed in epidemiologic studies of PTSD.
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Affiliation(s)
- Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Tyne W Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Dora Koller
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brenda Cabrera Mendoza
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, CT, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- Clinical Neurosciences Division, U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, New Haven, CT, USA
| | - Christopher O' Donell
- Cardiology Section, Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Guido Falcone
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, 15 York Street, LLCI 1004D, Box 208018, New Haven, CT, 06520, USA
| | - Murray B Stein
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
| | - Bogdan Pasaniuc
- Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, 06516, USA.
- VA CT Healthcare Center, West Haven, CT, 06516, USA.
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40
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Schubert R, Geoffroy E, Gregga I, Mulford AJ, Aguet F, Ardlie K, Gerszten R, Clish C, Van Den Berg D, Taylor KD, Durda P, Johnson WC, Cornell E, Guo X, Liu Y, Tracy R, Conomos M, Blackwell T, Papanicolaou G, Lappalainen T, Mikhaylova AV, Thornton TA, Cho MH, Gignoux CR, Lange L, Lange E, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Protein prediction for trait mapping in diverse populations. PLoS One 2022; 17:e0264341. [PMID: 35202437 PMCID: PMC8870552 DOI: 10.1371/journal.pone.0264341] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/08/2022] [Indexed: 11/18/2022] Open
Abstract
Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.
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Affiliation(s)
- Ryan Schubert
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, United States of America
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Elyse Geoffroy
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Isabelle Gregga
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
| | - Ashley J. Mulford
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Francois Aguet
- Broad Institute, Cambridge, MA, United States of America
| | - Kristin Ardlie
- Broad Institute, Cambridge, MA, United States of America
| | - Robert Gerszten
- Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Clary Clish
- Broad Institute, Cambridge, MA, United States of America
| | - David Van Den Berg
- University of Southern California, Los Angeles, CA, United States of America
| | - 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, United States of America
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - W. Craig Johnson
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, United States of America
| | - Elaine Cornell
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | - Russell Tracy
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Matthew Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Tom Blackwell
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
| | - George Papanicolaou
- Epidemiology Branch, National Heart, Lung and Blood Institute, Bethesda, MD, United States of America
| | - Tuuli Lappalainen
- New York Genome Center and Department of Systems Biology, Columbia University, New York, NY United States of America
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Christopher R. Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Ethan Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - 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, United States of America
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
- * E-mail:
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41
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Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data. PLoS Genet 2022; 18:e1009571. [PMID: 35100255 PMCID: PMC8830793 DOI: 10.1371/journal.pgen.1009571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 02/10/2022] [Accepted: 01/07/2022] [Indexed: 11/22/2022] Open
Abstract
Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits. The gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a tissue-specific manner, by training from population-scale transcriptomic profiles designed to identify expression quantitative loci (eQTLs). Imputing gene expression levels is shown to help understand the genetic basis of human disease through Transcriptome-wide association analysis (TWAS) and Mendelian Randomization (MR). However, it has been unclear how to integrate multiple imputation models trained from individual datasets to maximize their accuracy without having to access individual genotypes and expression levels that are often protected for privacy concerns. We developed SWAM (Smartly Weighted Averaging across Multiple datasets), a meta-imputation framework which can accurately impute gene expression levels from genotypes by integrating multiple imputation models without requiring individual-level data. Our method examines the similarity or differences between resources and borrowing information most relevant to the tissue of interest. We demonstrate that SWAM outperforms existing single-tissue and multi-tissue imputation models and continue to increase accuracy when integrating additional imputation models.
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42
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Wu P, Feng Q, Kerchberger VE, Nelson SD, Chen Q, Li B, Edwards TL, Cox NJ, Phillips EJ, Stein CM, Roden DM, Denny JC, Wei WQ. Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension. Nat Commun 2022; 13:46. [PMID: 35013250 PMCID: PMC8748496 DOI: 10.1038/s41467-021-27751-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/10/2021] [Indexed: 12/12/2022] Open
Abstract
Discovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.
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Affiliation(s)
- Patrick Wu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott D Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qingxia Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Todd L Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth J Phillips
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Infectious Diseases and Immunology, Murdoch University, Murdoch, Western Australia, Australia
| | - C Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Joshua C Denny
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
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43
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Maturation and application of phenome-wide association studies. Trends Genet 2022; 38:353-363. [PMID: 34991903 DOI: 10.1016/j.tig.2021.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/12/2021] [Accepted: 12/02/2021] [Indexed: 12/12/2022]
Abstract
In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.
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44
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Al-Barghouthi BM, Rosenow WT, Du KP, Heo J, Maynard R, Mesner L, Calabrese G, Nakasone A, Senwar B, Gerstenfeld L, Larner J, Ferguson V, Ackert-Bicknell C, Morgan E, Brautigan D, Farber CR. Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations. eLife 2022; 11:77285. [PMID: 36416764 PMCID: PMC9683789 DOI: 10.7554/elife.77285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Genome-wide association studies (GWASs) for bone mineral density (BMD) in humans have identified over 1100 associations to date. However, identifying causal genes implicated by such studies has been challenging. Recent advances in the development of transcriptome reference datasets and computational approaches such as transcriptome-wide association studies (TWASs) and expression quantitative trait loci (eQTL) colocalization have proven to be informative in identifying putatively causal genes underlying GWAS associations. Here, we used TWAS/eQTL colocalization in conjunction with transcriptomic data from the Genotype-Tissue Expression (GTEx) project to identify potentially causal genes for the largest BMD GWAS performed to date. Using this approach, we identified 512 genes as significant using both TWAS and eQTL colocalization. This set of genes was enriched for regulators of BMD and members of bone relevant biological processes. To investigate the significance of our findings, we selected PPP6R3, the gene with the strongest support from our analysis which was not previously implicated in the regulation of BMD, for further investigation. We observed that Ppp6r3 deletion in mice decreased BMD. In this work, we provide an updated resource of putatively causal BMD genes and demonstrate that PPP6R3 is a putatively causal BMD GWAS gene. These data increase our understanding of the genetics of BMD and provide further evidence for the utility of combined TWAS/colocalization approaches in untangling the genetics of complex traits.
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Affiliation(s)
- Basel Maher Al-Barghouthi
- Center for Public Health Genomics, School of Medicine, University of VirginiaCharlottesvilleUnited States,Department of Biochemistry and Molecular Genetics, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Will T Rosenow
- Center for Public Health Genomics, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Kang-Ping Du
- Department of Radiation Oncology, University of VirginiaCharlottesvilleUnited States
| | - Jinho Heo
- Department of Microbiology, Immunology, and Cancer Biology, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Robert Maynard
- Department of Orthopedics, Anschutz Medical Campus, University of ColoradoAuroraUnited States
| | - Larry Mesner
- Center for Public Health Genomics, School of Medicine, University of VirginiaCharlottesvilleUnited States,Department of Public Health Sciences, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Gina Calabrese
- Center for Public Health Genomics, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Aaron Nakasone
- Department of Mechanical Engineering, Boston UniversityBostonUnited States
| | - Bhavya Senwar
- Department of Mechanical Engineering, University of Colorado BoulderBoulderUnited States
| | - Louis Gerstenfeld
- Department of Orthopaedic Surgery, Boston University Medical CenterBostonUnited States
| | - James Larner
- Department of Radiation Oncology, University of VirginiaCharlottesvilleUnited States
| | - Virginia Ferguson
- Department of Mechanical Engineering, University of Colorado BoulderBoulderUnited States
| | - Cheryl Ackert-Bicknell
- Department of Orthopedics, Anschutz Medical Campus, University of ColoradoAuroraUnited States
| | - Elise Morgan
- Department of Mechanical Engineering, Boston UniversityBostonUnited States
| | - David Brautigan
- Department of Microbiology, Immunology, and Cancer Biology, School of Medicine, University of VirginiaCharlottesvilleUnited States
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of VirginiaCharlottesvilleUnited States,Department of Biochemistry and Molecular Genetics, School of Medicine, University of VirginiaCharlottesvilleUnited States,Department of Public Health Sciences, School of Medicine, University of VirginiaCharlottesvilleUnited States
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45
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Ngwa JS, Yanek LR, Kammers K, Kanchan K, Taub MA, Scharpf RB, Faraday N, Becker LC, Mathias RA, Ruczinski I. Secondary analyses for genome-wide association studies using expression quantitative trait loci. Genet Epidemiol 2022; 46:170-181. [PMID: 35312098 PMCID: PMC9086181 DOI: 10.1002/gepi.22448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/19/2021] [Accepted: 01/20/2022] [Indexed: 01/01/2023]
Abstract
Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.
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Affiliation(s)
- Julius S. Ngwa
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Lisa R. Yanek
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Kai Kammers
- Department of OncologyJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Kanika Kanchan
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Margaret A. Taub
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Robert B. Scharpf
- Department of OncologyJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Lewis C. Becker
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Rasika A. Mathias
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Ingo Ruczinski
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
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46
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Lucero D, Dikilitas O, Mendelson MM, Aligabi Z, Islam P, Neufeld EB, Bansal AT, Freeman LA, Vaisman B, Tang J, Combs CA, Li Y, Voros S, Kullo IJ, Remaley AT. Transgelin: A New Gene Involved in LDL Endocytosis Identified by a Genome-wide CRISPR-Cas9 Screen. J Lipid Res 2021; 63:100160. [PMID: 34902367 PMCID: PMC8953622 DOI: 10.1016/j.jlr.2021.100160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 11/18/2021] [Accepted: 12/02/2021] [Indexed: 12/26/2022] Open
Abstract
A significant proportion of patients with elevated LDL and a clinical presentation of familial hypercholesterolemia do not carry known genetic mutations associated with hypercholesterolemia, such as defects in the LDL receptor. To identify new genes involved in the cellular uptake of LDL, we developed a novel whole-genome clustered regularly interspaced short palindromic repeat-Cas9 KO screen in HepG2 cells. We identified transgelin (TAGLN), an actin-binding protein, as a potentially new gene involved in LDL endocytosis. In silico validation demonstrated that genetically predicted differences in expression of TAGLN in human populations were significantly associated with elevated plasma lipids (triglycerides, total cholesterol, and LDL-C) in the Global Lipids Genetics Consortium and lipid-related phenotypes in the UK Biobank. In biochemical studies, TAGLN-KO HepG2 cells showed a reduction in cellular LDL uptake, as measured by flow cytometry. In confocal microscopy imaging, TAGLN-KO cells had disrupted actin filaments as well as an accumulation of LDL receptor on their surface because of decreased receptor internalization. Furthermore, TAGLN-KO cells exhibited a reduction in total and free cholesterol content, activation of SREBP2, and a compensatory increase in cholesterol biosynthesis. TAGLN deficiency also disrupted the uptake of VLDL and transferrin, other known cargoes for receptors that depend upon clathrin-mediated endocytosis. Our data suggest that TAGLN is a novel factor involved in the actin-dependent phase of clathrin-mediated endocytosis of LDL. The identification of novel genes involved in the endocytic uptake of LDL may improve the diagnosis of hypercholesterolemia and provide future therapeutic targets for the prevention of cardiovascular disease.
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Affiliation(s)
- Diego Lucero
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ozan Dikilitas
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, MN, USA
| | - Michael M Mendelson
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zahra Aligabi
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Promotto Islam
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Edward B Neufeld
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Aruna T Bansal
- Acclarogen Ltd, St John's Innovation Centre, Cambridge, United Kingdom
| | - Lita A Freeman
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Boris Vaisman
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jingrong Tang
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christian A Combs
- NHLBI Light Microscopy Facility, National Institutes of Health, Bethesda, MD, USA
| | - Yuesheng Li
- DNA Sequencing and Genomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA
| | - Alan T Remaley
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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47
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Polikowsky HG, Shaw DM, Petty LE, Chen HH, Pruett DG, Linklater JP, Viljoen KZ, Beilby JM, Highland HM, Levitt B, Avery CL, Mullan Harris K, Jones RM, Below JE, Kraft SJ. Population-based genetic effects for developmental stuttering. HGG ADVANCES 2021; 3:100073. [PMID: 35047858 PMCID: PMC8756529 DOI: 10.1016/j.xhgg.2021.100073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022] Open
Abstract
Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = -5.576, p = 2.46 × 10-8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10-6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.
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Affiliation(s)
- Hannah G. Polikowsky
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Douglas M. Shaw
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren E. Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dillon G. Pruett
- Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | | | | | - Janet M. Beilby
- Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Heather M. Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brandt Levitt
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Robin M. Jones
- Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA,Corresponding author
| | - Shelly Jo Kraft
- Communication Sciences and Disorders, Wayne State University, Detroit, MI, USA,Corresponding author
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48
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Perrot N, Pelletier W, Bourgault J, Couture C, Li Z, Mitchell PL, Ghodsian N, Bossé Y, Thériault S, Mathieu P, Arsenault BJ. A trans-omic Mendelian randomization study of parental lifespan uncovers novel aging biology and therapeutic candidates for chronic diseases. Aging Cell 2021; 20:e13497. [PMID: 34704651 PMCID: PMC8590095 DOI: 10.1111/acel.13497] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 05/20/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022] Open
Abstract
The study of parental lifespan has emerged as an innovative tool to advance aging biology and our understanding of the genetic architecture of human longevity and aging-associated diseases. Here, we leveraged summary statistics of a genome-wide association study including over one million parental lifespans to identify genetically regulated genes from the Genotype-Tissue Expression project. Through a combination of multi-tissue transcriptome-wide association analyses and genetic colocalization, we identified novel genes that may be associated with parental lifespan. Mendelian randomization (MR) analyses also identified circulating proteins and metabolites causally associated with parental lifespan and chronic diseases offering new drug repositioning opportunities such as those targeting apolipoprotein-B-containing lipoproteins. Liver expression of HP, the gene encoding haptoglobin, and plasma haptoglobin levels were causally linked with parental lifespan. Phenome-wide MR analyses were used to map genetically regulated genes, proteins and metabolites with other human traits as well as the disease-related phenome in the FinnGen cohorts (n = 135,638). Altogether, this study identified new candidate genes, circulating proteins and metabolites that may influence human aging as well as potential therapeutic targets for chronic diseases that warrant further investigation.
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Affiliation(s)
- Nicolas Perrot
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - William Pelletier
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Jérôme Bourgault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Christian Couture
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Zhonglin Li
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Patricia L. Mitchell
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Nooshin Ghodsian
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
| | - Yohan Bossé
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of Molecular MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Sébastien Thériault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of Molecular Biology, Medical Biochemistry and PathologyFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Patrick Mathieu
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of SurgeryFaculty of MedicineUniversité LavalQuébecQCCanada
| | - Benoit J. Arsenault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de QuébecQuébecQCCanada
- Department of MedicineFaculty of MedicineUniversité LavalQuébecQCCanada
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49
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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50
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Findley AS, Zhang X, Boye C, Lin YL, Kalita CA, Barreiro L, Lohmueller KE, Pique-Regi R, Luca F. A signature of Neanderthal introgression on molecular mechanisms of environmental responses. PLoS Genet 2021; 17:e1009493. [PMID: 34570765 PMCID: PMC8509894 DOI: 10.1371/journal.pgen.1009493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 10/12/2021] [Accepted: 08/18/2021] [Indexed: 12/17/2022] Open
Abstract
Ancient human migrations led to the settlement of population groups in varied environmental contexts worldwide. The extent to which adaptation to local environments has shaped human genetic diversity is a longstanding question in human evolution. Recent studies have suggested that introgression of archaic alleles in the genome of modern humans may have contributed to adaptation to environmental pressures such as pathogen exposure. Functional genomic studies have demonstrated that variation in gene expression across individuals and in response to environmental perturbations is a main mechanism underlying complex trait variation. We considered gene expression response to in vitro treatments as a molecular phenotype to identify genes and regulatory variants that may have played an important role in adaptations to local environments. We investigated if Neanderthal introgression in the human genome may contribute to the transcriptional response to environmental perturbations. To this end we used eQTLs for genes differentially expressed in a panel of 52 cellular environments, resulting from 5 cell types and 26 treatments, including hormones, vitamins, drugs, and environmental contaminants. We found that SNPs with introgressed Neanderthal alleles (N-SNPs) disrupt binding of transcription factors important for environmental responses, including ionizing radiation and hypoxia, and for glucose metabolism. We identified an enrichment for N-SNPs among eQTLs for genes differentially expressed in response to 8 treatments, including glucocorticoids, caffeine, and vitamin D. Using Massively Parallel Reporter Assays (MPRA) data, we validated the regulatory function of 21 introgressed Neanderthal variants in the human genome, corresponding to 8 eQTLs regulating 15 genes that respond to environmental perturbations. These findings expand the set of environments where archaic introgression may have contributed to adaptations to local environments in modern humans and provide experimental validation for the regulatory function of introgressed variants.
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Affiliation(s)
- Anthony S. Findley
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Xinjun Zhang
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
| | - Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Yen Lung Lin
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Cynthia A. Kalita
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Luis Barreiro
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Kirk E. Lohmueller
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
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