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Sun Z, Pan L, Tian A, Chen P. Critically-ill COVID-19 susceptibility gene CCR3 shows natural selection in sub-Saharan Africans. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2024; 121:105594. [PMID: 38636619 DOI: 10.1016/j.meegid.2024.105594] [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: 01/15/2024] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
The prevalence of COVID-19 critical illness varies across ethnicities, with recent studies suggesting that genetic factors may contribute to this variation. The aim of this study was to investigate natural selection signals of genes associated with critically-ill COVID-19 in sub-Saharan Africans. Severe COVID-19 SNPs were obtained from the HGI website. Selection signals were assessed in 661 sub-Sahara Africans from 1000 Genomes Project using integrated haplotype score (iHS), cross-population extended haplotype homozygosity (XP-EHH), and fixation index (Fst). Allele frequency trajectory analysis of ancient DNA samples were used to validate the existing of selection in sub-Sahara Africans. We also used Mendelian randomization to decipher the correlation between natural selection and critically-ill COVID-19. We identified that CCR3 exhibited significant natural selection signals in sub-Sahara Africans. Within the CCR3 gene, rs17217831-A showed both high iHS (Standardized iHS = 2) and high XP-EHH (Standardized XP-EHH = 2.5) in sub-Sahara Africans. Allele frequency trajectory of CCR3 rs17217831-A revealed natural selection occurring in the recent 1,500 years. Natural selection resulted in increased CCR3 expression in sub-Sahara Africans. Mendelian Randomization provided evidence that increased blood CCR3 expression and eosinophil counts lowered the risk of critically ill COVID-19. Our findings suggest that sub-Saharan Africans are resistant to critically ill COVID-19 due to natural selection and identify CCR3 as a potential novel therapeutic target.
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Affiliation(s)
- Zewen Sun
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Lin Pan
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China; The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Aowen Tian
- Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China; Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, Jilin, China
| | - Peng Chen
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China; Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China; Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, Jilin, China.
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2
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Hu X, Wang J, Yang K, Fan H, Wu J, Ren J, Han G, Li J, Xue Z, Liu X, Lv X. The GWAS SNP rs80207740 modulates erythrocyte traits via allele-specific binding of IKZF1 and targeting XPO7 gene. FASEB J 2024; 38:e23666. [PMID: 38780091 DOI: 10.1096/fj.202302017r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/31/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Genome-wide association studies have identified many single nucleotide polymorphisms (SNPs) associated with erythrocyte traits. However, the functional variants and their working mechanisms remain largely unknown. Here, we reported that the SNP of rs80207740, which was associated with red blood cell (RBC) volume and hemoglobin content across populations, conferred enhancer activity to XPO7 gene via allele-differentially binding to Ikaros family zinc finger 1 (IKZF1). We showed that the region around rs80207740 was an erythroid-specific enhancer using reporter assays, and that the G-allele further enhanced activity. 3D genome evidence showed that the enhancer interacted with the XPO7 promoter, and eQTL analysis suggested that the G-allele upregulated expression of XPO7. We further showed that the rs80207740-G allele facilitated the binding of transcription factor IKZF1 in EMSA and ChIP analyses. Knockdown of IKZF1 and GATA1 resulted in decreased expression of Xpo7 in both human and mouse erythroid cells. Finally, we constructed Xpo7 knockout mouse by CRISPR/Cas9 and observed anemic phenotype with reduced volume and hemoglobin content of RBC, consistent to the effect of rs80207740 on erythrocyte traits. Overall, our study demonstrated that rs80207740 modulated erythroid indices by regulating IKZF1 binding and Xpo7 expression.
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Affiliation(s)
- Xinjun Hu
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Jiaxin Wang
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Ke Yang
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Hong Fan
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Jie Wu
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Jiuqiang Ren
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Gaijing Han
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Jing Li
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Zheng Xue
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Xuehui Liu
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Xiang Lv
- State Key Laboratory of Complex, Severe, and Rare Diseases, Haihe Laboratory of Cell Ecosystem, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
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3
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Fu J, Zhang Q, Wang J, Wang M, Zhang B, Zhu W, Qiu S, Geng Z, Cui G, Yu Y, Liao W, Zhang H, Gao B, Xu X, Han T, Yao Z, Qin W, Liu F, Liang M, Wang S, Xu Q, Xu J, Zhang P, Li W, Shi D, Wang C, Lui S, Yan Z, Chen F, Zhang J, Li J, Shen W, Miao Y, Wang D, Xian J, Gao JH, Zhang X, Xu K, Zuo XN, Zhang L, Ye Z, Cheng J, Li MJ, Yu C. Cross-ancestry genome-wide association studies of brain imaging phenotypes. Nat Genet 2024:10.1038/s41588-024-01766-y. [PMID: 38811844 DOI: 10.1038/s41588-024-01766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.
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Affiliation(s)
- Jilian Fu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Jianhua Wang
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
- Biomedical Institute, Henan Academy of Sciences, Zhengzhou, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Zuojun Geng
- Department of Medical Imaging, the Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province and Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Molecular Imaging Research Center of Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Zhang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, China
| | - Bo Gao
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen Qin
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Sijia Wang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jiance Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Yanwei Miao
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Kai Xu
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Mulin Jun Li
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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4
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Foyzun T, Whiting M, Velasco KK, Jacobsen JC, Connor M, Grimsey NL. Single nucleotide polymorphisms in the cannabinoid CB 2 receptor: Molecular pharmacology and disease associations. Br J Pharmacol 2024. [PMID: 38802979 DOI: 10.1111/bph.16383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/26/2024] [Accepted: 03/09/2024] [Indexed: 05/29/2024] Open
Abstract
Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non-synonymous SNP (ns-SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns-SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns-SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune-related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses.
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Affiliation(s)
- Tahira Foyzun
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde, New South Wales, Australia
| | - Maddie Whiting
- Department of Pharmacology and Clinical Pharmacology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of Medicine, School of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Kate K Velasco
- Department of Pharmacology and Clinical Pharmacology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of Medicine, School of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Jessie C Jacobsen
- School of Biological Sciences, Faculty of Science, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Mark Connor
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde, New South Wales, Australia
| | - Natasha L Grimsey
- Department of Pharmacology and Clinical Pharmacology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
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Kim A, Zhang Z, Legros C, Lu Z, de Smith A, Moore JE, Mancuso N, Gazal S. Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307556. [PMID: 38798383 PMCID: PMC11118635 DOI: 10.1101/2024.05.17.24307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (∼2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.
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Bagheri M, Bombin A, Shi M, Murthy VL, Shah R, Mosley JD, Ferguson JF. Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations. Front Genet 2024; 15:1392622. [PMID: 38812968 PMCID: PMC11133605 DOI: 10.3389/fgene.2024.1392622] [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: 02/27/2024] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.
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Affiliation(s)
- Minoo Bagheri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Andrei Bombin
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Venkatesh L. Murthy
- Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Ravi Shah
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jonathan D. Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jane F. Ferguson
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
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Reeve MP, Vehviläinen M, Luo S, Ritari J, Karjalainen J, Gracia-Tabuenca J, Mehtonen J, Padmanabhuni SS, Kolosov N, Artomov M, Siirtola H, Olilla HM, Graham D, Partanen J, Xavier RJ, Daly MJ, Ripatti S, Salo T, Siponen M. Oral and non-oral lichen planus show genetic heterogeneity and differential risk for autoimmune disease and oral cancer. Am J Hum Genet 2024:S0002-9297(24)00161-7. [PMID: 38776927 DOI: 10.1016/j.ajhg.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/23/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
Lichen planus (LP) is a T-cell-mediated inflammatory disease affecting squamous epithelia in many parts of the body, most often the skin and oral mucosa. Cutaneous LP is usually transient and oral LP (OLP) is most often chronic, so we performed a large-scale genetic and epidemiological study of LP to address whether the oral and non-oral subgroups have shared or distinct underlying pathologies and their overlap with autoimmune disease. Using lifelong records covering diagnoses, procedures, and clinic identity from 473,580 individuals in the FinnGen study, genome-wide association analyses were conducted on carefully constructed subcategories of OLP (n = 3,323) and non-oral LP (n = 4,356) and on the combined group. We identified 15 genome-wide significant associations in FinnGen and an additional 12 when meta-analyzed with UKBB (27 independent associations at 25 distinct genomic locations), most of which are shared between oral and non-oral LP. Many associations coincide with known autoimmune disease loci, consistent with the epidemiologic enrichment of LP with hypothyroidism and other autoimmune diseases. Notably, a third of the FinnGen associations demonstrate significant differences between OLP and non-OLP. We also observed a 13.6-fold risk for tongue cancer and an elevated risk for other oral cancers in OLP, in agreement with earlier reports that connect LP with higher cancer incidence. In addition to a large-scale dissection of LP genetics and comorbidities, our study demonstrates the use of comprehensive, multidimensional health registry data to address outstanding clinical questions and reveal underlying biological mechanisms in common but understudied diseases.
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Affiliation(s)
- Mary Pat Reeve
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Mari Vehviläinen
- Department of Oral and Maxillofacial Diseases, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Shuang Luo
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Jarmo Ritari
- Finnish Red Cross Blood Service, Helsinki, Finland
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Javier Gracia-Tabuenca
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Juha Mehtonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Shanmukha Sampath Padmanabhuni
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Nikita Kolosov
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA; Ohio State University College of Medicine, Columbus, OH, USA
| | - Mykyta Artomov
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA; Ohio State University College of Medicine, Columbus, OH, USA
| | - Harri Siirtola
- TAUCHI Research Center, Tampere University, Tampere, Finland
| | - Hanna M Olilla
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel Graham
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Tuula Salo
- Research Unit of Population Health, Department of Oral Pathology, University of Oulu and Oulu University Hospital, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, Finland; Department of Oral and Maxillofacial Diseases, and Translational Immunology Program (TRIMM), University of Helsinki, Helsinki, Finland
| | - Maria Siponen
- Institute of Dentistry, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; Odontology Education Unit, and Oral and Maxillofacial Diseases Clinic, Kuopio University Hospital, Kuopio, Finland
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Zhang F, Jiang F, Yao Z, Luo H, Xu S, Zhang Y, Wang X, Liu Z. Causal association of blood cell traits with inflammatory bowel diseases: a Mendelian randomization study. Front Nutr 2024; 11:1256832. [PMID: 38774261 PMCID: PMC11106477 DOI: 10.3389/fnut.2024.1256832] [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: 07/21/2023] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
Background Observational studies have found associations between blood cell traits and inflammatory bowel diseases (IBDs), whereas the causality and dose-effect relationships are still undetermined. Methods Two-sample Mendelian randomization (MR) analyses using linear regression approaches, as well as Bayesian model averaging (MR-BMA), were conducted to identify and prioritize the causal blood cell traits for Crohn's disease (CD) and ulcerative colitis (UC). An observational study was also performed using restricted cubic spline (RCS) to explore the relationship between important blood cell traits and IBDs. Results Our uvMR analysis using the random effects inverse variance weighted (IVW) method identified eosinophil (EOS) as a causal factor for UC (OR = 1.36; 95% CI: 1.13, 1.63). Our MR-BMA analysis further prioritized that high level of lymphocyte (LYM) decreased CD risk (MIP = 0.307; θ ^ MACE = -0.059; PP = 0.189; θ ^ λ = -0.173), whereas high level of EOS increased UC risk (MIP = 0.824; θ ^ MACE = 0.198; PP = 0.627; θ ^ λ = 0.239). Furthermore, the observational study clearly depicts the nonlinear relationship between important blood cell traits and the risk of IBDs. Conclusion Using MR approaches, several blood cell traits were identified as risk factors of CD and UC, which could be used as potential targets for the management of IBDs. Stratified genome-wide association studies (GWASs) based on the concentration of traits would be helpful owing to the nonlinear relationships between blood cell traits and IBDs, as demonstrated in our clinical observational study. Together, these findings could shed light on the clinical strategies applied to the management of CD and UC.
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Affiliation(s)
- Fangyuan Zhang
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feiyu Jiang
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ziqin Yao
- Sir Run Run Shaw Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongbin Luo
- Sir Run Run Shaw Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Shoufang Xu
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingying Zhang
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinhui Wang
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Sir Run Run Shaw Hospital, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhiwei Liu
- Department of Blood Transfusion, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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9
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Peng Q, Liu X, Li W, Jing H, Li J, Gao X, Luo Q, Breeze CE, Pan S, Zheng Q, Li G, Qian J, Yuan L, Yuan N, You C, Du S, Zheng Y, Yuan Z, Tan J, Jia P, Wang J, Zhang G, Lu X, Shi L, Guo S, Liu Y, Ni T, Wen B, Zeng C, Jin L, Teschendorff AE, Liu F, Wang S. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nat Genet 2024; 56:846-860. [PMID: 38641644 DOI: 10.1038/s41588-023-01494-9] [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: 11/17/2021] [Accepted: 08/02/2023] [Indexed: 04/21/2024]
Abstract
Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.
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Affiliation(s)
- Qianqian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenran Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Han Jing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jiarui Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xingjian Gao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | | | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Guochao Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiaqiang Qian
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Chenglong You
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyu Yuan
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Xianping Lu
- Shenzhen Chipscreen Biosciences Co. Ltd., Shenzhen, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Shicheng Guo
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Wen
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- The Fifth People's Hospital of Shanghai and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Changqing Zeng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia.
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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10
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Zheng Z, Liu S, Sidorenko J, Wang Y, Lin T, Yengo L, Turley P, Ani A, Wang R, Nolte IM, Snieder H, Yang J, Wray NR, Goddard ME, Visscher PM, Zeng J. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet 2024; 56:767-777. [PMID: 38689000 PMCID: PMC11096109 DOI: 10.1038/s41588-024-01704-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/05/2024] [Indexed: 05/02/2024]
Abstract
We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.
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Affiliation(s)
- Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Shouye Liu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rujia Wang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
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11
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Leung PBM, Liu Z, Zhong Y, Tubbs JD, Di Forti M, Murray RM, So HC, Sham PC, Lui SSY. Bidirectional two-sample Mendelian randomization study of differential white blood cell counts and schizophrenia. Brain Behav Immun 2024; 118:22-30. [PMID: 38355025 DOI: 10.1016/j.bbi.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 01/15/2024] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Schizophrenia and white blood cell counts (WBC) are both complex and polygenic traits. Previous evidence suggests that increased WBC are associated with higher all-cause mortality, and other studies have found elevated WBC in first-episode psychosis and chronic schizophrenia. However, these observational findings may be confounded by antipsychotic exposures and their effects on WBC. Mendelian randomization (MR) is a useful method for examining the directions of genetically-predicted relationships between schizophrenia and WBC. METHODS We performed a two-sample MR using summary statistics from genome-wide association studies (GWAS) conducted by the Psychiatric Genomics Consortium Schizophrenia Workgroup (N = 130,644) and the Blood Cell Consortium (N = 563,946). The MR methods included inverse variance weighted (IVW), MR Egger, weighted median, MR-PRESSO, contamination mixture, and a novel approach called mixture model reciprocal causal inference (MRCI). False discovery rate was employed to correct for multiple testing. RESULTS Multiple MR methods supported bidirectional genetically-predicted relationships between lymphocyte count and schizophrenia: IVW (b = 0.026; FDR p-value = 0.008), MR Egger (b = 0.026; FDR p-value = 0.008), weighted median (b = 0.013; FDR p-value = 0.049), and MR-PRESSO (b = 0.014; FDR p-value = 0.010) in the forward direction, and IVW (OR = 1.100; FDR p-value = 0.021), MR Egger (OR = 1.231; FDR p-value < 0.001), weighted median (OR = 1.136; FDR p-value = 0.006) and MRCI (OR = 1.260; FDR p-value = 0.026) in the reverse direction. MR Egger (OR = 1.171; FDR p-value < 0.001) and MRCI (OR = 1.154; FDR p-value = 0.026) both suggested genetically-predicted eosinophil count is associated with schizophrenia, but MR Egger (b = 0.060; FDR p-value = 0.010) and contamination mixture (b = -0.013; FDR p-value = 0.045) gave ambiguous results on whether genetically predicted liability to schizophrenia would be associated with eosinophil count. MR Egger (b = 0.044; FDR p-value = 0.010) and MR-PRESSO (b = 0.009; FDR p-value = 0.045) supported genetically predicted liability to schizophrenia is associated with elevated monocyte count, and the opposite direction was also indicated by MR Egger (OR = 1.231; FDR p-value = 0.045). Lastly, unidirectional genetic liability from schizophrenia to neutrophil count were proposed by MR-PRESSO (b = 0.011; FDR p-value = 0.028) and contamination mixture (b = 0.011; FDR p-value = 0.045) method. CONCLUSION This MR study utilised multiple MR methods to obtain results suggesting bidirectional genetic genetically-predicted relationships for elevated lymphocyte counts and schizophrenia risk. In addition, moderate evidence also showed bidirectional genetically-predicted relationships between schizophrenia and monocyte counts, and unidirectional effect from genetic liability for eosinophil count to schizophrenia and from genetic liability for schizophrenia to neutrophil count. The influence of schizophrenia to eosinophil count is less certain. Our findings support the role of WBC in schizophrenia and concur with the hypothesis of neuroinflammation in schizophrenia.
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Affiliation(s)
- Perry B M Leung
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Centre for Child Health, Guangzhou, China
| | - Yuanxin Zhong
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marta Di Forti
- Social, Genetics and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region; Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Simon S Y Lui
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
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12
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Asgel Z, Kouakou MR, Koller D, Pathak GA, Cabrera-Mendoza B, Polimanti R. Unraveling COVID-19 relationship with anxiety disorders and symptoms using genome-wide data. J Affect Disord 2024; 352:333-341. [PMID: 38382819 PMCID: PMC10939738 DOI: 10.1016/j.jad.2024.02.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND There is still a limited understanding of the dynamics contributing to the comorbidity of COVID-19 and anxiety outcomes. METHODS To dissect the pleiotropic mechanisms contributing to COVID-19/anxiety comorbidity, we used genome-wide data from UK Biobank (up to 420,531 participants), FinnGen Project (up to 329,077 participants), Million Veteran Program (175,163 participants), and COVID-19 Host Genetics Initiative (up to 122,616 cases and 2,475,240 controls). Specifically, we assessed global and local genetic correlation and genetically inferred effects linking COVID-19 outcomes (infection, hospitalization, and severe respiratory symptoms) to anxiety disorders and symptoms. RESULTS We observed a strong genetic correlation of anxiety disorder with COVID-19 positive status (rg = 0.35, p = 2×10-4) and COVID-19 hospitalization (rg = 0.31, p = 7.2×10-4). Among anxiety symptoms, "Tense, sore, or aching muscles during worst period of anxiety" was genetically correlated with COVID-19 positive status (rg = 0.33, p = 0.001), while "Frequent trouble falling or staying asleep during worst period of anxiety" was genetically correlated with COVID-19 hospitalization (rg = 0.24, p = 0.004). Through a latent causal variable analysis, we observed that COVID-19 outcomes have statistically significant genetic causality proportion (gcp) on anxiety symptoms (e.g., COVID-19 positive status→"Recent easy annoyance or irritability" │gcp│ = 0.18, p = 6.72×10-17). Conversely, anxiety disorders appear to have a possible causal effect on COVID-19 (│gcp│ = 0.38, p = 3.17×10-9). Additionally, we also identified multiple loci with evidence of local genetic correlation between anxiety and COVID-19. These appear to be related to genetic effects shared with lung function, brain morphology, alcohol and tobacco use, and hematologic parameters. CONCLUSIONS This study provided insights into the pleiotropic mechanisms linking COVID-19 and anxiety outcomes, suggesting differences between dynamics related to anxiety disorders and those related to anxiety symptoms.
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Affiliation(s)
- Zeynep Asgel
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Manuela R Kouakou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Catalonia, Spain
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
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13
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Schmit SL, Tsai YY, Bonner JD, Sanz-Pamplona R, Joshi AD, Ugai T, Lindsey SS, Melas M, McDonnell KJ, Idos GE, Walker CP, Qu C, Kast WM, Da Silva DM, Glickman JN, Chan AT, Giannakis M, Nowak JA, Rennert HS, Robins HS, Ogino S, Greenson JK, Moreno V, Rennert G, Gruber SB. Germline genetic regulation of the colorectal tumor immune microenvironment. BMC Genomics 2024; 25:409. [PMID: 38664626 PMCID: PMC11046907 DOI: 10.1186/s12864-024-10295-1] [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: 05/01/2023] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVE To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC). METHODS Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication. RESULTS We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa. CONCLUSIONS Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.
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Affiliation(s)
- Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA.
| | - Ya-Yu Tsai
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Joseph D Bonner
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Rebeca Sanz-Pamplona
- Catalan Institute of Oncology (ICO), Hospitalet de Llobregat, Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sidney S Lindsey
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Marilena Melas
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Kevin J McDonnell
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Gregory E Idos
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Christopher P Walker
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Chenxu Qu
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - W Martin Kast
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Diane M Da Silva
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | | | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Marios Giannakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Hedy S Rennert
- B. Rappaport Faculty of Medicine, Technion and the Association for Promotion of Research in Precision Medicine (APRPM), Haifa, Israel
| | | | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan
| | - Joel K Greenson
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Victor Moreno
- Catalan Institute of Oncology (ICO), Hospitalet de Llobregat, Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Gad Rennert
- B. Rappaport Faculty of Medicine, Technion and the Association for Promotion of Research in Precision Medicine (APRPM), Haifa, Israel
| | - Stephen B Gruber
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA.
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14
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Mosley JD, Shelley JP, Dickson AL, Zanussi J, Daniel LL, Zheng NS, Bastarache L, Wei WQ, Shi M, Jarvik GP, Rosenthal EA, Khan A, Sherafati A, Kullo IJ, Walunas TL, Glessner J, Hakonarson H, Cox NJ, Roden DM, Frangakis SG, Vanderwerff B, Stein CM, Van Driest SL, Borinstein SC, Shu XO, Zawistowski M, Chung CP, Kawai VK. Clinical associations with a polygenic predisposition to benign lower white blood cell counts. Nat Commun 2024; 15:3384. [PMID: 38649760 PMCID: PMC11035609 DOI: 10.1038/s41467-024-47804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.
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Affiliation(s)
- Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - John P Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alyson L Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacy Zanussi
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura L Daniel
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil S Zheng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Yale School of Medicine, New Haven, CT, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington Medical Center, Seattle, WA, USA
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
| | - Atlas Khan
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Alborz Sherafati
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Theresa L Walunas
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Joseph Glessner
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hakon Hakonarson
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nancy J Cox
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephan G Frangakis
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Brett Vanderwerff
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sara L Van Driest
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott C Borinstein
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Matthew Zawistowski
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | - Vivian K Kawai
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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15
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Jakubek YA, Ma X, Stilp AM, Yu F, Bacon J, Wong JW, Aguet F, Ardlie K, Arnett D, Barnes K, Bis JC, Blackwell T, Becker LC, Boerwinkle E, Bowler RP, Budoff MJ, Carson AP, Chen J, Cho MH, Coresh J, Cox N, de Vries PS, DeMeo DL, Fardo DW, Fornage M, Guo X, Hall ME, Heard-Costa N, Hidalgo B, Irvin MR, Johnson AD, Kenny EE, Levy D, Li Y, Lima JA, Liu Y, Loos RJF, Machiela MJ, Mathias RA, Mitchell BD, Murabito J, Mychaleckyj JC, North K, Orchard P, Parker SC, Pershad Y, Peyser PA, Pratte KA, Psaty BM, Raffield LM, Redline S, Rich SS, Rotter JI, Shah SJ, Smith JA, Smith AP, Smith A, Taub M, Tiwari HK, Tracy R, Tuftin B, Bick AG, Sankaran VG, Reiner AP, Scheet P, Auer PL. Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.16.24305851. [PMID: 38699360 PMCID: PMC11065036 DOI: 10.1101/2024.04.16.24305851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.
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16
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Troubat L, Fettahoglu D, Henches L, Aschard H, Julienne H. Multi-trait GWAS for diverse ancestries: mapping the knowledge gap. BMC Genomics 2024; 25:375. [PMID: 38627641 PMCID: PMC11022331 DOI: 10.1186/s12864-024-10293-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. METHODS Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)). RESULTS We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria. CONCLUSIONS Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.
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Affiliation(s)
- Lucie Troubat
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Deniz Fettahoglu
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France.
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, F-75015, France.
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17
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Ishida Y, Matsushita M, Yoneshiro T, Saito M, Fuse S, Hamaoka T, Kuroiwa M, Tanaka R, Kurosawa Y, Nishimura T, Motoi M, Maeda T, Nakayama K. Genetic evidence for involvement of β2-adrenergic receptor in brown adipose tissue thermogenesis in humans. Int J Obes (Lond) 2024:10.1038/s41366-024-01522-6. [PMID: 38632325 DOI: 10.1038/s41366-024-01522-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Sympathetic activation of brown adipose tissue (BAT) thermogenesis can ameliorate obesity and related metabolic abnormalities. However, crucial subtypes of the β-adrenergic receptor (AR), as well as effects of its genetic variants on functions of BAT, remains unclear in humans. We conducted association analyses of genes encoding β-ARs and BAT activity in human adults. METHODS Single nucleotide polymorphisms (SNPs) in β1-, β2-, and β3-AR genes (ADRB1, ADRB2, and ADRB3) were tested for the association with BAT activity under mild cold exposure (19 °C, 2 h) in 399 healthy Japanese adults. BAT activity was measured using fluorodeoxyglucose-positron emission tomography and computed tomography (FDG-PET/CT). To validate the results, we assessed the effects of SNPs in the two independent populations comprising 277 healthy East Asian adults using near-infrared time-resolved spectroscopy (NIRTRS) or infrared thermography (IRT). Effects of SNPs on physiological responses to intensive cold exposure were tested in 42 healthy Japanese adult males using an artificial climate chamber. RESULTS We found a significant association between a functional SNP (rs1042718) in ADRB2 and BAT activity assessed with FDG-PET/CT (p < 0.001). This SNP also showed an association with cold-induced thermogenesis in the population subset. Furthermore, the association was replicated in the two other independent populations; BAT activity was evaluated by NIRTRS or IRT (p < 0.05). This SNP did not show associations with oxygen consumption and cold-induced thermogenesis under intensive cold exposure, suggesting the irrelevance of shivering thermogenesis. The SNPs of ADRB1 and ADRB3 were not associated with these BAT-related traits. CONCLUSIONS The present study supports the importance of β2-AR in the sympathetic regulation of BAT thermogenesis in humans. The present collection of DNA samples is the largest to which information on the donor's BAT activity has been assigned and can serve as a reference for further in-depth understanding of human BAT function.
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Affiliation(s)
- Yuka Ishida
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8562, Japan
| | - Mami Matsushita
- Department of Nutrition, School of Nursing and Nutrition, Tenshi College, Sapporo, Hokkaido, 065-0013, Japan
| | - Takeshi Yoneshiro
- Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, 153-8904, Japan
- Department of Molecular Metabolism and Physiology, Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, 980-8575, Japan
| | - Masayuki Saito
- Department of Nutrition, School of Nursing and Nutrition, Tenshi College, Sapporo, Hokkaido, 065-0013, Japan
- Laboratory of Biochemistry, Faculty of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, 060-0818, Japan
| | - Sayuri Fuse
- Department of Sports Medicine for Health Promotion, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan
| | - Takafumi Hamaoka
- Department of Sports Medicine for Health Promotion, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan
| | - Miyuki Kuroiwa
- Department of Sports Medicine for Health Promotion, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan
| | - Riki Tanaka
- Faculty of Sports and Health Science, Fukuoka University, Fukuoka, Fukuoka, 814-0180, Japan
| | - Yuko Kurosawa
- Department of Sports Medicine for Health Promotion, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan
| | - Takayuki Nishimura
- Department of Human Life Design and Science, Faculty of Design, Kyushu University, Fukuoka, Fukuoka, 815-8540, Japan
- Physiological Anthropology Research Center, Faculty of Design, Kyushu University, Fukuoka, Fukuoka, 815-8540, Japan
| | - Midori Motoi
- Department of Human Life Design and Science, Faculty of Design, Kyushu University, Fukuoka, Fukuoka, 815-8540, Japan
| | - Takafumi Maeda
- Department of Human Life Design and Science, Faculty of Design, Kyushu University, Fukuoka, Fukuoka, 815-8540, Japan
- Physiological Anthropology Research Center, Faculty of Design, Kyushu University, Fukuoka, Fukuoka, 815-8540, Japan
| | - Kazuhiro Nakayama
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8562, Japan.
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18
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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19
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Kock KH, Kimes PK, Gisselbrecht SS, Inukai S, Phanor SK, Anderson JT, Ramakrishnan G, Lipper CH, Song D, Kurland JV, Rogers JM, Jeong R, Blacklow SC, Irizarry RA, Bulyk ML. DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues. Nat Commun 2024; 15:3110. [PMID: 38600112 PMCID: PMC11006913 DOI: 10.1038/s41467-024-47396-0] [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: 08/16/2023] [Accepted: 03/29/2024] [Indexed: 04/12/2024] Open
Abstract
Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.
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Affiliation(s)
- Kian Hong Kock
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
| | - Patrick K Kimes
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephen S Gisselbrecht
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sabrina K Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - James T Anderson
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Gayatri Ramakrishnan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Boston Bangalore Biosciences Beginnings Program, Harvard University, Cambridge, MA, USA
| | - Colin H Lipper
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Dongyuan Song
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jesse V Kurland
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Julia M Rogers
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA
| | - Stephen C Blacklow
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Rafael A Irizarry
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA.
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA.
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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20
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Moksnes MR, Hansen AF, Wolford BN, Thomas LF, Rasheed H, Simić A, Bhatta L, Brantsæter AL, Surakka I, Zhou W, Magnus P, Njølstad PR, Andreassen OA, Syversen T, Zheng J, Fritsche LG, Evans DM, Warrington NM, Nøst TH, Åsvold BO, Flaten TP, Willer CJ, Hveem K, Brumpton BM. A genome-wide association study provides insights into the genetic etiology of 57 essential and non-essential trace elements in humans. Commun Biol 2024; 7:432. [PMID: 38594418 PMCID: PMC11004147 DOI: 10.1038/s42003-024-06101-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: 05/09/2023] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Trace elements are important for human health but may exert toxic or adverse effects. Mechanisms of uptake, distribution, metabolism, and excretion are partly under genetic control but have not yet been extensively mapped. Here we report a comprehensive multi-element genome-wide association study of 57 essential and non-essential trace elements. We perform genome-wide association meta-analyses of 14 trace elements in up to 6564 Scandinavian whole blood samples, and genome-wide association studies of 43 trace elements in up to 2819 samples measured only in the Trøndelag Health Study (HUNT). We identify 11 novel genetic loci associated with blood concentrations of arsenic, cadmium, manganese, selenium, and zinc in genome-wide association meta-analyses. In HUNT, several genome-wide significant loci are also indicated for other trace elements. Using two-sample Mendelian randomization, we find several indications of weak to moderate effects on health outcomes, the most precise being a weak harmful effect of increased zinc on prostate cancer. However, independent validation is needed. Our current understanding of trace element-associated genetic variants may help establish consequences of trace elements on human health.
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Affiliation(s)
- Marta R Moksnes
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
| | - Ailin F Hansen
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Brooke N Wolford
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Laurent F Thomas
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- BioCore-Bioinformatics Core Facility, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Humaira Rasheed
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Anica Simić
- Department of Chemistry, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Laxmi Bhatta
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Lise Brantsæter
- Department of Food Safety, Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tore Syversen
- Department of Neuroscience, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - David M Evans
- Institute for Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Nicole M Warrington
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Institute for Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Therese H Nøst
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Bjørn Olav Åsvold
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Trond Peder Flaten
- Department of Chemistry, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen J Willer
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway
| | - Ben M Brumpton
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway.
- Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
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21
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Maihofer AX, Ratanatharathorn A, Hemmings SMJ, Costenbader KH, Michopoulos V, Polimanti R, Rothbaum AO, Seedat S, Mikita EA, Smith AK, Salem RM, Shaffer RA, Wu T, Sebat J, Ressler KJ, Stein MB, Koenen KC, Wolf EJ, Sumner JA, Nievergelt CM. Effects of genetically predicted posttraumatic stress disorder on autoimmune phenotypes. Transl Psychiatry 2024; 14:172. [PMID: 38561342 PMCID: PMC10984931 DOI: 10.1038/s41398-024-02869-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/21/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Observational studies suggest that posttraumatic stress disorder (PTSD) increases risk for various autoimmune diseases. Insights into shared biology and causal relationships between these diseases may inform intervention approaches to PTSD and co-morbid autoimmune conditions. We investigated the shared genetic contributions and causal relationships between PTSD, 18 autoimmune diseases, and 3 immune/inflammatory biomarkers. Univariate MiXeR was used to contrast the genetic architectures of phenotypes. Genetic correlations were estimated using linkage disequilibrium score regression. Bi-directional, two-sample Mendelian randomization (MR) was performed using independent, genome-wide significant single nucleotide polymorphisms; inverse variance weighted and weighted median MR estimates were evaluated. Sensitivity analyses for uncorrelated (MR PRESSO) and correlated horizontal pleiotropy (CAUSE) were also performed. PTSD was considerably more polygenic (10,863 influential variants) than autoimmune diseases (median 255 influential variants). However, PTSD evidenced significant genetic correlation with nine autoimmune diseases and three inflammatory biomarkers. PTSD had putative causal effects on autoimmune thyroid disease (p = 0.00009) and C-reactive protein (CRP) (p = 4.3 × 10-7). Inferences were not substantially altered by sensitivity analyses. Additionally, the PTSD-autoimmune thyroid disease association remained significant in multivariable MR analysis adjusted for genetically predicted inflammatory biomarkers as potential mechanistic pathway variables. No autoimmune disease had a significant causal effect on PTSD (all p values > 0.05). Although causal effect models were supported for associations of PTSD with CRP, shared pleiotropy was adequate to explain a putative causal effect of CRP on PTSD (p = 0.18). In summary, our results suggest a significant genetic overlap between PTSD, autoimmune diseases, and biomarkers of inflammation. PTSD has a putative causal effect on autoimmune thyroid disease, consistent with existing epidemiologic evidence. A previously reported causal effect of CRP on PTSD is potentially confounded by shared genetics. Together, results highlight the nuanced links between PTSD, autoimmune disorders, and associated inflammatory signatures, and suggest the importance of targeting related pathways to protect against disease and disability.
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Affiliation(s)
- Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA.
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA.
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Sian M J Hemmings
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, Western Cape, South Africa
- South African Medical Research Council/Genomics of Brain Disorders Research Unit, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Vasiliki Michopoulos
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Renato Polimanti
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Alex O Rothbaum
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, Western Cape, South Africa
- South African Medical Research Council/Genomics of Brain Disorders Research Unit, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Elizabeth A Mikita
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Alicia K Smith
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
| | - Rany M Salem
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Richard A Shaffer
- Department of Epidemiology and Health Sciences, Naval Health Research Center, San Diego, CA, USA
| | - Tianying Wu
- Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kerry J Ressler
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Murray B Stein
- 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
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Erika J Wolf
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Jennifer A Sumner
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
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22
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [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: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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23
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He XY, Wu BS, Yang L, Guo Y, Deng YT, Li ZY, Fei CJ, Liu WS, Ge YJ, Kang J, Feng J, Cheng W, Dong Q, Yu JT. Genetic associations of protein-coding variants in venous thromboembolism. Nat Commun 2024; 15:2819. [PMID: 38561338 PMCID: PMC10984941 DOI: 10.1038/s41467-024-47178-8] [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: 07/12/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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24
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Braat S, Fielding KL, Han J, Jackson VE, Zaloumis S, Xu JXH, Moir-Meyer G, Blaauwendraad SM, Jaddoe VWV, Gaillard R, Parkin PC, Borkhoff CM, Keown-Stoneman CDG, Birken CS, Maguire JL, Bahlo M, Davidson EM, Pasricha SR. Haemoglobin thresholds to define anaemia from age 6 months to 65 years: estimates from international data sources. Lancet Haematol 2024; 11:e253-e264. [PMID: 38432242 PMCID: PMC10983828 DOI: 10.1016/s2352-3026(24)00030-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Detection of anaemia is crucial for clinical medicine and public health. Current WHO anaemia definitions are based on statistical thresholds (fifth centiles) set more than 50 years ago. We sought to establish evidence for the statistical haemoglobin thresholds for anaemia that can be applied globally and inform WHO and clinical guidelines. METHODS In this analysis we identified international data sources from populations in the USA, England, Australia, China, the Netherlands, Canada, Ecuador, and Bangladesh with sufficient clinical and laboratory information collected between 1998 and 2020 to obtain a healthy reference sample. Individuals with clinical or biochemical evidence of a condition that could reduce haemoglobin concentrations were excluded. We estimated haemoglobin thresholds (ie, 5th centiles) for children aged 6-23 months, 24-59 months, 5-11 years, and 12-17 years, and adults aged 18-65 years (including during pregnancy) for individual datasets and pooled across data sources. We also collated findings from three large-scale genetic studies to summarise genetic variants affecting haemoglobin concentrations in different ancestral populations. FINDINGS We identified eight data sources comprising 18 individual datasets that were eligible for inclusion in the analysis. In pooled analyses, the haemoglobin fifth centile was 104·4 g/L (90% CI 103·5-105·3) in 924 children aged 6-23 months, 110·2 g/L (109·5-110·9) in 1874 children aged 24-59 months, and 114·4 g/L (113·6-115·2) in 1839 children aged 5-11 years. Values diverged by sex in adolescents and adults. In pooled analyses, the fifth centile was 122·2 g/L (90% CI 121·3-123·1) in 1741 female adolescents aged 12-17 years and 128·2 g/L (126·4-130·0) in 1103 male adolescents aged 12-17 years. In pooled analyses of adults aged 18-65 years, the fifth centile was 119·7 g/L (90% CI 119·1-120·3) in 3640 non-pregnant females and 134·9 g/L (134·2-135·6) in 2377 males. Fifth centiles in pregnancy were 110·3 g/L (90% CI 109·5-111·0) in the first trimester (n=772) and 105·9 g/L (104·0-107·7) in the second trimester (n=111), with insufficient data for analysis in the third trimester. There were insufficient data for adults older than 65 years. We did not identify ancestry-specific high prevalence of non-clinically relevant genetic variants that influence haemoglobin concentrations. INTERPRETATION Our results enable global harmonisation of clinical and public health haemoglobin thresholds for diagnosis of anaemia. Haemoglobin thresholds are similar between sexes until adolescence, after which males have higher thresholds than females. We did not find any evidence that thresholds should differ between people of differering ancestries. FUNDING World Health Organization and the Bill & Melinda Gates Foundation.
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Affiliation(s)
- Sabine Braat
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Methods and Implementation Support for Clinical and Health research Hub, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Katherine L Fielding
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Clinical Haematology, The Austin Hospital, Heidelberg, VIC, Australia
| | - Jiru Han
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Victoria E Jackson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Sophie Zaloumis
- Methods and Implementation Support for Clinical and Health research Hub, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Jessica Xu Hui Xu
- Methods and Implementation Support for Clinical and Health research Hub, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Gemma Moir-Meyer
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Sophia M Blaauwendraad
- Generation R Study Group, and Department of Pediatrics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Vincent W V Jaddoe
- Generation R Study Group, and Department of Pediatrics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Romy Gaillard
- Generation R Study Group, and Department of Pediatrics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Patricia C Parkin
- Division of Pediatric Medicine and the Pediatric Outcomes Research Team, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Cornelia M Borkhoff
- Division of Pediatric Medicine and the Pediatric Outcomes Research Team, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Charles D G Keown-Stoneman
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Unity Health Toronto, Toronto, ON, Canada
| | - Catherine S Birken
- Division of Pediatric Medicine and the Pediatric Outcomes Research Team, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Jonathon L Maguire
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Unity Health Toronto, Toronto, ON, Canada
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Eliza M Davidson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
| | - Sant-Rayn Pasricha
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Diagnostic Haematology, The Royal Melbourne Hospital, Parkville, VIC, Australia; Clinical Haematology, Peter MacCallum Cancer Centre and The Royal Melbourne Hospital, Parkville, VIC, Australia.
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25
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Zhang XX, Yu XY, Xu SJ, Shi XQ, Chen Y, Shi Q, Sun C. rs2736098, a synonymous polymorphism, is associated with carcinogenesis and cell count in multiple tissue types by regulating TERT expression. Heliyon 2024; 10:e27802. [PMID: 38496869 PMCID: PMC10944260 DOI: 10.1016/j.heliyon.2024.e27802] [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: 12/18/2023] [Revised: 02/19/2024] [Accepted: 03/06/2024] [Indexed: 03/19/2024] Open
Abstract
rs2736098 is a synonymous polymorphism in TERT (telomerase reverse transcriptase), an enzyme involved in tumor onset of multiple tissues, and should play no roles in carcinogenesis. However, a search in cancer somatic mutation database indicated that the mutation frequency at rs2736098 is much higher than the average one for TERT. Moreover, there are significant H3K4me1 and H3K27Ac signals, two universal histone modifications for active enhancers, surrounding rs2736098. Therefore, we hypothesized that rs2736098 might be within an enhancer region, regulate TERT expression and influence cancer risk. Through luciferase assay, it was verified that the enhancer activity of rs2736098C allele is significantly higher than that of T in multiple tissues. Transfection of plasmids containing TERT coding region with two different alleles indicated that rs2736098C allele can induce a significantly higher TERT expression than T. By chromatin immunoprecipitation, it was observed that the fragment spanning rs2736098 can interact with USF1 (upstream transcription factor 1). The two alleles of rs2736098 present evidently different binding affinity with nuclear proteins. Database and literature search indicated that rs2736098 is significantly associated with carcinogenesis in multiple tissues and count of multiple cell types. All these facts indicated that rs2736098 is also an oncogenic polymorphism and plays important role in cell proliferation.
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Affiliation(s)
- Xin-Xin Zhang
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
| | - Xin-Yi Yu
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
| | - Shuang-Jia Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
| | - Xiao-Qian Shi
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
| | - Ying Chen
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
| | - Qiang Shi
- College of Biology Pharmacy and Food Engineering, Shangluo University, Shangluo, Shaanxi, 726000, PR China
| | - Chang Sun
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi, 710119, PR China
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Chang Z, Wang S, Liu K, Lin R, Liu C, Zhang J, Wei D, Nie Y, Chen Y, He J, Li H, Cheng ZJ, Sun B. Peripheral blood indicators and COVID-19: an observational and bidirectional Mendelian randomization study. BMC Med Genomics 2024; 17:81. [PMID: 38549094 PMCID: PMC10979573 DOI: 10.1186/s12920-024-01844-4] [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: 05/05/2023] [Accepted: 03/01/2024] [Indexed: 04/01/2024] Open
Abstract
Blood is critical for health, supporting key functions like immunity and oxygen transport. While studies have found links between common blood clinical indicators and COVID-19, they cannot provide causal inference due to residual confounding and reverse causality. To identify indicators affecting COVID-19, we analyzed clinical data (n = 2,293, aged 18-65 years) from Guangzhou Medical University's first affiliated hospital (2022-present), identifying 34 significant indicators differentiating COVID-19 patients from healthy controls. Utilizing bidirectional Mendelian randomization analyses, integrating data from over 2.46 million participants from various large-scale studies, we established causal links for six blood indicators with COVID-19 risk, five of which is consistent with our observational findings. Specifically, elevated Troponin I and Platelet Distribution Width levels are linked with increased COVID-19 susceptibility, whereas higher Hematocrit, Hemoglobin, and Neutrophil counts confer a protective effect. Reverse MR analysis confirmed four blood biomarkers influenced by COVID-19, aligning with our observational data for three of them. Notably, COVID-19 exhibited a positive causal relationship with Troponin I (Tnl) and Serum Amyloid Protein A, while a negative association was observed with Plateletcrit. These findings may help identify high-risk individuals and provide further direction on the management of COVID-19.
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Affiliation(s)
- Zhenglin Chang
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangdong Province, Guangzhou, 510005, China
| | - Suilin Wang
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kemin Liu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Runpei Lin
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Changlian Liu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jiale Zhang
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Daqiang Wei
- Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
| | - Yuxi Nie
- Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
| | - Yuerong Chen
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jiawei He
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Haiyang Li
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| | - Zhangkai J Cheng
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangdong Province, Guangzhou, 510005, China.
| | - Baoqing Sun
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangdong Province, Guangzhou, 510005, China.
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Liu Y, Zhu Q, Guo G, Xie Z, Li S, Lai C, Wu Y, Wang L, Zhong S. Causal associations of genetically predicted gut microbiota and blood metabolites with inflammatory states and risk of infections: a Mendelian randomization analysis. Front Microbiol 2024; 15:1342653. [PMID: 38585702 PMCID: PMC10995310 DOI: 10.3389/fmicb.2024.1342653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/28/2024] [Indexed: 04/09/2024] Open
Abstract
Background Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections). Methods Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO. Results After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = -0.040) and SAA1 (Beta IVW = -0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = -0.034) and pneumonia risk (Beta IVW = -0.391). After correction by FDR, only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = -0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia. Conclusion This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.
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Affiliation(s)
- Yingjian Liu
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Qian Zhu
- Department of Neurosurgery, Huizhou Third People's Hospital, Guangzhou Medical University, Huizhou, Guangdong, China
| | - Gongjie Guo
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Zhipeng Xie
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Senlin Li
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Chengyang Lai
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yonglin Wu
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Liansheng Wang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shilong Zhong
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
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Yeyeodu S, Hanafi D, Webb K, Laurie NA, Kimbro KS. Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease. Front Endocrinol (Lausanne) 2024; 14:1286979. [PMID: 38577257 PMCID: PMC10991756 DOI: 10.3389/fendo.2023.1286979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/07/2023] [Indexed: 04/06/2024] Open
Abstract
Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.
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Affiliation(s)
- Susan Yeyeodu
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
- Charles River Discovery Services, Morrisville, NC, United States
| | - Donia Hanafi
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - Kenisha Webb
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
| | - Nikia A. Laurie
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - K. Sean Kimbro
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
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Strausz S, Abner E, Blacker G, Galloway S, Hansen P, Feng Q, Lee BT, Jones SE, Haapaniemi H, Raak S, Nahass GR, Sanders E, Soodla P, Võsa U, Esko T, Sinnott-Armstrong N, Weissman IL, Daly M, Aivelo T, Tal MC, Ollila HM. SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease. Nat Commun 2024; 15:2041. [PMID: 38503741 PMCID: PMC10950847 DOI: 10.1038/s41467-024-45983-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/06/2024] [Indexed: 03/21/2024] Open
Abstract
Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.
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Affiliation(s)
- Satu Strausz
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Oral and Maxillofacial Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Plastic Surgery, Cleft Palate and Craniofacial Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Erik Abner
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Grace Blacker
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sarah Galloway
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paige Hansen
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qingying Feng
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brandon T Lee
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel E Jones
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Hele Haapaniemi
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Sten Raak
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - George Ronald Nahass
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Erin Sanders
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pilleriin Soodla
- Department of Infectious Diseases, Internal Medicine Clinic, Tartu University Hospital, Tartu, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nasa Sinnott-Armstrong
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Irving L Weissman
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Mark Daly
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Tuomas Aivelo
- Organismal and Evolutionary Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Michal Caspi Tal
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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30
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Artaza H, Eriksson D, Lavrichenko K, Aranda-Guillén M, Bratland E, Vaudel M, Knappskog P, Husebye ES, Bensing S, Wolff ASB, Kämpe O, Røyrvik EC, Johansson S. Rare copy number variation in autoimmune Addison's disease. Front Immunol 2024; 15:1374499. [PMID: 38562931 PMCID: PMC10982488 DOI: 10.3389/fimmu.2024.1374499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility.
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Affiliation(s)
- Haydee Artaza
- Department of Clinical Science, University of Bergen, Bergen, Norway
- K. G. Jebsen Center for Autoimmune Diseases, University of Bergen, Bergen, Norway
| | - Daniel Eriksson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Center for Molecular Medicine, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
| | - Ksenia Lavrichenko
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Maribel Aranda-Guillén
- Center for Molecular Medicine, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
| | - Eirik Bratland
- K. G. Jebsen Center for Autoimmune Diseases, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Per Knappskog
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Eystein S. Husebye
- K. G. Jebsen Center for Autoimmune Diseases, University of Bergen, Bergen, Norway
- Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Sophie Bensing
- Department of Endocrinology, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anette S. B. Wolff
- Department of Clinical Science, University of Bergen, Bergen, Norway
- K. G. Jebsen Center for Autoimmune Diseases, University of Bergen, Bergen, Norway
- Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Olle Kämpe
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ellen C. Røyrvik
- Department of Clinical Science, University of Bergen, Bergen, Norway
- K. G. Jebsen Center for Autoimmune Diseases, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Bergen, Norway
| | - Stefan Johansson
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
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31
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Constantinescu AE, Hughes DA, Bull CJ, Fleming K, Mitchell RE, Zheng J, Kar S, Timpson NJ, Amulic B, Vincent EE. A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis. Hum Genomics 2024; 18:26. [PMID: 38491524 PMCID: PMC10941368 DOI: 10.1186/s40246-024-00585-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). RESULTS We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. CONCLUSION Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Louisiana State University, Louisiana, USA
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
- Health Data Research UK, London, UK
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases, National Health Commission, Shanghai, People's Republic of China
- Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Early Cancer Insitute, University of Cambridge, Cambridge, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Borko Amulic
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- School of Translational Health Sciences, University of Bristol, Bristol, UK.
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32
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Ingold N, Seviiri M, Ong JS, Gordon S, Neale RE, Whiteman DC, Olsen CM, MacGregor S, Law MH. Genetic Analysis of Perceived Youthfulness Reveals Differences in How Men's and Women's Age Is Assessed. J Invest Dermatol 2024:S0022-202X(24)00180-5. [PMID: 38460809 DOI: 10.1016/j.jid.2024.02.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 03/11/2024]
Abstract
Skin aging is a natural process that occurs over time but can be accelerated by sun exposure. Measuring skin age in a large population can provide insight into the extent of skin damage from sun exposure and skin cancer risk. Understanding the genetics of skin aging, within and across sexes (males and females), could improve our understanding of the genetic drivers of both skin aging and skin cancer. We used UK Biobank data to examine the genetic overlap between perceived youthfulness and traits relevant to actinic photoaging. Our GWAS identified 22 genome-wide significant loci for women and 43 for men. The genetic correlation (rg) between perceived youthfulness in men and women was significantly less than unity (rg = 0.75, 95% confidence interval = 0.69-0.80), suggesting a gene-by-sex interaction. In women, perceived youthfulness was modestly correlated with keratinocyte cancer (rg = -0.19) and skin tanning (rg = 0.18). In men, perceived youthfulness was correlated with male-pattern baldness (rg = -0.23). This suggests that the genetic architecture of perceived youthfulness may differ between sexes, with genes influencing skin tanning and skin cancer susceptibility driving the difference in women, whereas genes influencing male-pattern baldness and other puberty-related traits drive the difference in men. We recommend that future genetic analysis of skin aging include a sex-stratified component.
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Affiliation(s)
- Nathan Ingold
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia; Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
| | - Mathias Seviiri
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia; Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Jue-Sheng Ong
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Rachel E Neale
- Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Herston, Australia
| | - David C Whiteman
- Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health, University of Queensland, Herston, Australia
| | - Catherine M Olsen
- Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Herston, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia; Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
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Wang MY, Zhang Z, Zhao S, Onodera T, Sun XN, Zhu Q, Li C, Li N, Chen S, Paredes M, Gautron L, Charron MJ, Marciano DK, Gordillo R, Drucker DJ, Scherer PE. Downregulation of the kidney glucagon receptor, essential for renal function and systemic homeostasis, contributes to chronic kidney disease. Cell Metab 2024; 36:575-597.e7. [PMID: 38237602 PMCID: PMC10932880 DOI: 10.1016/j.cmet.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 09/10/2023] [Accepted: 12/19/2023] [Indexed: 02/12/2024]
Abstract
The glucagon receptor (GCGR) in the kidney is expressed in nephron tubules. In humans and animal models with chronic kidney disease, renal GCGR expression is reduced. However, the role of kidney GCGR in normal renal function and in disease development has not been addressed. Here, we examined its role by analyzing mice with constitutive or conditional kidney-specific loss of the Gcgr. Adult renal Gcgr knockout mice exhibit metabolic dysregulation and a functional impairment of the kidneys. These mice exhibit hyperaminoacidemia associated with reduced kidney glucose output, oxidative stress, enhanced inflammasome activity, and excess lipid accumulation in the kidney. Upon a lipid challenge, they display maladaptive responses with acute hypertriglyceridemia and chronic proinflammatory and profibrotic activation. In aged mice, kidney Gcgr ablation elicits widespread renal deposition of collagen and fibronectin, indicative of fibrosis. Taken together, our findings demonstrate an essential role of the renal GCGR in normal kidney metabolic and homeostatic functions. Importantly, mice deficient for kidney Gcgr recapitulate some of the key pathophysiological features of chronic kidney disease.
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Affiliation(s)
- May-Yun Wang
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Zhuzhen Zhang
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shangang Zhao
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Sam and Ann Barshop Institute for Longevity and Aging Studies, Division of Endocrinology, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Toshiharu Onodera
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xue-Nan Sun
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qingzhang Zhu
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Chao Li
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Na Li
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shiuhwei Chen
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Megan Paredes
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Laurent Gautron
- Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Maureen J Charron
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Denise K Marciano
- Division of Nephrology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ruth Gordillo
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Daniel J Drucker
- Lunenfeld-TanenbaumResearchInstitute, Mt. Sinai Hospital, Toronto, ON M5G1X5, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 1X5, Canada
| | - Philipp E Scherer
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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Bick AG, Metcalf GA, Mayo KR, Lichtenstein L, Rura S, Carroll RJ, Musick A, Linder JE, Jordan IK, Nagar SD, Sharma S, Meller R, Basford M, Boerwinkle E, Cicek MS, Doheny KF, Eichler EE, Gabriel S, Gibbs RA, Glazer D, Harris PA, Jarvik GP, Philippakis A, Rehm HL, Roden DM, Thibodeau SN, Topper S, Blegen AL, Wirkus SJ, Wagner VA, Meyer JG, Cicek MS, Muzny DM, Venner E, Mawhinney MZ, Griffith SML, Hsu E, Ling H, Adams MK, Walker K, Hu J, Doddapaneni H, Kovar CL, Murugan M, Dugan S, Khan Z, Boerwinkle E, Lennon NJ, Austin-Tse C, Banks E, Gatzen M, Gupta N, Henricks E, Larsson K, McDonough S, Harrison SM, Kachulis C, Lebo MS, Neben CL, Steeves M, Zhou AY, Smith JD, Frazar CD, Davis CP, Patterson KE, Wheeler MM, McGee S, Lockwood CM, Shirts BH, Pritchard CC, Murray ML, Vasta V, Leistritz D, Richardson MA, Buchan JG, Radhakrishnan A, Krumm N, Ehmen BW, Schwartz S, Aster MMT, Cibulskis K, Haessly A, Asch R, Cremer A, Degatano K, Shergill A, Gauthier LD, Lee SK, Hatcher A, Grant GB, Brandt GR, Covarrubias M, Banks E, Able A, Green AE, Carroll RJ, Zhang J, Condon HR, Wang Y, Dillon MK, Albach CH, Baalawi W, Choi SH, Wang X, Rosenthal EA, Ramirez AH, Lim S, Nambiar S, Ozenberger B, Wise AL, Lunt C, Ginsburg GS, Denny JC. Genomic data in the All of Us Research Program. Nature 2024; 627:340-346. [PMID: 38374255 PMCID: PMC10937371 DOI: 10.1038/s41586-023-06957-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/08/2023] [Indexed: 02/21/2024]
Abstract
Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.
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Zheng T, Roda G, Zabana Y, Escudero-Hernández C, Liu X, Chen Y, Camargo Tavares L, Bonfiglio F, Mellander MR, Janczewska I, Vigren L, Sjöberg K, Ohlsson B, Almer S, Halfvarson J, Miehlke S, Madisch A, Lieb W, Kupčinskas J, Weersma RK, Bujanda L, Julià A, Marsal S, Esteve M, Guagnozzi D, Fernández-Bañares F, Ferrer C, Peter I, Ludvigsson JF, Pardi D, Verhaegh B, Jonkers D, Pierik M, Münch A, Franke A, Bresso F, Khalili H, Colombel JF, D'Amato M. Human Leukocyte Antigen Signatures as Pathophysiological Discriminants of Microscopic Colitis Subtypes. J Crohns Colitis 2024; 18:349-359. [PMID: 37768647 DOI: 10.1093/ecco-jcc/jjad165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/29/2023] [Accepted: 09/26/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND AND AIMS Microscopic colitis [MC] is currently regarded as an inflammatory bowel disease that manifests as two subtypes: collagenous colitis [CC] and lymphocytic colitis [LC]. Whether these represent a clinical continuum or distinct entities is, however, an open question. Genetic investigations may contribute important insight into their respective pathophysiologies. METHODS We conducted a genome-wide association study [GWAS] meta-analysis in 1498 CC, 373 LC patients, and 13 487 controls from Europe and the USA, combined with publicly available MC GWAS data from UK Biobank and FinnGen [2599 MC cases and 552 343 controls in total]. Human leukocyte antigen [HLA] alleles and polymorphic residues were imputed and tested for association, including conditional analyses for the identification of key causative variants and residues. Genetic correlations with other traits and diagnoses were also studied. RESULTS We detected strong HLA association with CC, and conditional analyses highlighted the DRB1*03:01 allele and its residues Y26, N77, and R74 as key to this association (best p = 1.4 × 10-23, odds ratio [OR] = 1.96). Nominally significant genetic correlations were detected between CC and pneumonia [rg = 0.77; p = 0.048] and oesophageal diseases [rg = 0.45, p = 0.023]. An additional locus was identified in MC GWAS analyses near the CLEC16A and RMI2 genes on chromosome 16 [rs35099084, p = 2.0 × 10-8, OR = 1.31]. No significant association was detected for LC. CONCLUSION Our results suggest CC and LC have distinct pathophysiological underpinnings, characterised by an HLA predisposing role only in CC. This challenges existing classifications, eventually calling for a re-evaluation of the utility of MC umbrella definitions.
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Affiliation(s)
- Tenghao Zheng
- School of Biological Sciences, Monash University, Clayton, VIC, Australia
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Giulia Roda
- Biostructures and Biosystems National Institute, Rome, Italy
| | - Yamile Zabana
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Celia Escudero-Hernández
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Xingrong Liu
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ye Chen
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ferdinando Bonfiglio
- School of Biological Sciences, Monash University, Clayton, VIC, Australia
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Lina Vigren
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Klas Sjöberg
- Department of Clinical Sciences, Lund University, Skane University Hospital, Malmo, Sweden
| | - Bodil Ohlsson
- Department of Clinical Sciences, Lund University, Skane University Hospital, Malmo, Sweden
| | - Sven Almer
- Division of Gastroenterology, Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Jonas Halfvarson
- Department of Gastroenterology, Faculty of Medicine and Health, Orebro University Hospital, Örebro, Sweden
| | - Stephan Miehlke
- Centre for Digestive Diseases, Internal Medicine Centre Eppendorf, and Centre for Oesophageal Disorders, University Hospital Eppendorf, Hamburg, Germany
| | - Ahmed Madisch
- Department of Gastroenterology, CRH Clinic Siloah, Hannover, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Juozas Kupčinskas
- Department of Gastroenterology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Luis Bujanda
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
- Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute, Donostia University Hospital, Universidad del País Vasco, San Sebastian, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d' Hebron Research Institute, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d' Hebron Research Institute, Barcelona, Spain
| | - Maria Esteve
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Danila Guagnozzi
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
- Department of Gastroenterology, Vall d'Hebron University Hospital, Neuro-Immuno-Gastroenterology Group, Digestive System Research Unit, Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Fernando Fernández-Bañares
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Carmen Ferrer
- Pathology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Darrell Pardi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Bas Verhaegh
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daisy Jonkers
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marieke Pierik
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andreas Münch
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Department of Gastroenterology and Hepatology, Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Francesca Bresso
- Division of Gastroenterology, Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Jean-Frederic Colombel
- Dr Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mauro D'Amato
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Gastrointestinal Genetics Lab, CIC bioGUNE - BRTA, Derio, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Department of Medicine and Surgery, LUM University, Casamassima, Italy
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Tindula G, Issac B, Mukherjee SK, Ekramullah SM, Arman DM, Islam J, Suchanda HS, Sun L, Rockowitz S, Christiani DC, Warf BC, Mazumdar M. Genome-wide analysis of spina bifida risk variants in a case-control study from Bangladesh. Birth Defects Res 2024; 116:e2331. [PMID: 38526198 PMCID: PMC10963057 DOI: 10.1002/bdr2.2331] [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: 11/01/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Human studies of genetic risk factors for neural tube defects, severe birth defects associated with long-term health consequences in surviving children, have predominantly been restricted to a subset of candidate genes in specific biological pathways including folate metabolism. METHODS In this study, we investigated the association of genetic variants spanning the genome with risk of spina bifida (i.e., myelomeningocele and meningocele) in a subset of families enrolled from December 2016 through December 2022 in a case-control study in Bangladesh, a population often underrepresented in genetic studies. Saliva DNA samples were analyzed using the Illumina Global Screening Array. We performed genetic association analyses to compare allele frequencies between 112 case and 121 control children, 272 mothers, and 128 trios. RESULTS In the transmission disequilibrium test analyses with trios only, we identified three novel exonic spina bifida risk loci, including rs140199800 (SULT1C2, p = 1.9 × 10-7), rs45580033 (ASB2, p = 4.2 × 10-10), and rs75426652 (LHPP, p = 7.2 × 10-14), after adjusting for multiple hypothesis testing. Association analyses comparing cases and controls, as well as models that included their mothers, did not identify genome-wide significant variants. CONCLUSIONS This study identified three novel single nucleotide polymorphisms involved in biological pathways not previously associated with neural tube defects. The study warrants replication in larger groups to validate findings and to inform targeted prevention strategies.
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Affiliation(s)
- Gwen Tindula
- Department of Neurology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Department of Neurology, Harvard Medical School, Boston, MA, 02115, United States
| | - Biju Issac
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
| | - Sudipta Kumer Mukherjee
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Sheikh Muhammad Ekramullah
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - DM Arman
- Department of Paediatric Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Joynul Islam
- Department of Clinical Neurosurgery, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Hafiza Sultana Suchanda
- Pediatric Neurosurgery Research Committee, National Institute of Neurosciences and Hospital (NINS), Sher-e-Bangla Nagar, Agargoan, Dhaka-1207, Bangladesh
| | - Liang Sun
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
| | - Shira Rockowitz
- Research Computing, Information Technology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, 02115, United States
- The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, United States
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, United States
| | - Benjamin C. Warf
- Department of Neurosurgery, Boston Children's Hospital, Boston, MA, 02115, United States
| | - Maitreyi Mazumdar
- Department of Neurology, Boston Children’s Hospital, Boston, MA, 02115, United States
- Department of Neurology, Harvard Medical School, Boston, MA, 02115, United States
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, United States
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37
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Kubota Y, Viny AD. Germline predisposition for clonal hematopoiesis. Semin Hematol 2024; 61:61-67. [PMID: 38311514 PMCID: PMC11103258 DOI: 10.1053/j.seminhematol.2024.01.007] [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: 11/03/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 02/06/2024]
Abstract
Clonal hematopoiesis (CH) is an entity hallmarked by skewed hematopoiesis with persistent overrepresentation of cells from a common stem/progenitor lineage harboring single-nucleotide variants and/or insertions/deletions. CH is a common and age-related phenomenon that is associated with an increased risk of hematological malignancies, cardiovascular disease, and all-cause mortality. While CH is a term of the hematological aspect, there exists a complex interaction with other organ systems, especially the cardiovascular system. The strongest factor in the development of CH is aging, however, other multiple factors also affect the development of CH including lifestyle-related factors and co-morbid diseases. In recent years, germline genetic factors have been linked to CH risk. In this review, we synthesize what is currently known about how genetic variation affects the risk of CH, how this genetic architecture intersects with myeloid neoplasms, and future prospects for CH.
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Affiliation(s)
- Yasuo Kubota
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH.
| | - Aaron D Viny
- Division of Hematology & Oncology, Department of Medicine, and Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY.
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38
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Guo J, Walter K, Quiros PM, Gu M, Baxter EJ, Danesh J, Di Angelantonio E, Roberts D, Guglielmelli P, Harrison CN, Godfrey AL, Green AR, Vassiliou GS, Vuckovic D, Nangalia J, Soranzo N. Inherited polygenic effects on common hematological traits influence clonal selection on JAK2 V617F and the development of myeloproliferative neoplasms. Nat Genet 2024; 56:273-280. [PMID: 38233595 PMCID: PMC10864174 DOI: 10.1038/s41588-023-01638-x] [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: 12/22/2022] [Accepted: 12/01/2023] [Indexed: 01/19/2024]
Abstract
Myeloproliferative neoplasms (MPNs) are chronic cancers characterized by overproduction of mature blood cells. Their causative somatic mutations, for example, JAK2V617F, are common in the population, yet only a minority of carriers develop MPN. Here we show that the inherited polygenic loci that underlie common hematological traits influence JAK2V617F clonal expansion. We identify polygenic risk scores (PGSs) for monocyte count and plateletcrit as new risk factors for JAK2V617F positivity. PGSs for several hematological traits influenced the risk of different MPN subtypes, with low PGSs for two platelet traits also showing protective effects in JAK2V617F carriers, making them two to three times less likely to have essential thrombocythemia than carriers with high PGSs. We observed that extreme hematological PGSs may contribute to an MPN diagnosis in the absence of somatic driver mutations. Our study showcases how polygenic backgrounds underlying common hematological traits influence both clonal selection on somatic mutations and the subsequent phenotype of cancer.
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Affiliation(s)
- Jing Guo
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | | | - Pedro M Quiros
- Wellcome Sanger Institute, Hinxton, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Muxin Gu
- Wellcome Sanger Institute, Hinxton, UK
| | - E Joanna Baxter
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - John Danesh
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Fondazione Human Technopole, Milan, Italy
| | - David Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant-Oxford Centre, John Radcliffe Hospital and Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Paola Guglielmelli
- Department of Experimental and Clinical Medicine, Center for Research and Innovation of Myeloproliferative Neoplasms (CRIMM), AOU Careggi, University of Florence, Florence, Italy
| | - Claire N Harrison
- Department of Haematology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Anthony R Green
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - George S Vassiliou
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Dragana Vuckovic
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Jyoti Nangalia
- Wellcome Sanger Institute, Hinxton, UK.
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
- Cambridge University Hospitals NHS Trust, Cambridge, UK.
- Department of Haematology, University of Cambridge, Cambridge, UK.
| | - Nicole Soranzo
- Wellcome Sanger Institute, Hinxton, UK.
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Fondazione Human Technopole, Milan, Italy.
- Department of Haematology, University of Cambridge, Cambridge, UK.
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Hayeck TJ, Li Y, Mosbruger TL, Bradfield JP, Gleason AG, Damianos G, Shaw GTW, Duke JL, Conlin LK, Turner TN, Fernández-Viña MA, Sarmady M, Monos DS. The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection. Genome Biol Evol 2024; 16:evae009. [PMID: 38302106 PMCID: PMC10853003 DOI: 10.1093/gbe/evae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.
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Affiliation(s)
- Tristan J Hayeck
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy L Mosbruger
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Adam G Gleason
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - George Damianos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Grace Tzun-Wen Shaw
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jamie L Duke
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Laura K Conlin
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marcelo A Fernández-Viña
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Mahdi Sarmady
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitri S Monos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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40
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Rowan TN, Schnabel RD, Decker JE. Uncovering the architecture of selection in two Bos taurus cattle breeds. Evol Appl 2024; 17:e13666. [PMID: 38405336 PMCID: PMC10883790 DOI: 10.1111/eva.13666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/18/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.
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Affiliation(s)
- Troy N. Rowan
- Division of Animal SciencesUniversity of MissouriColumbiaMissouriUSA
- Genetics Area ProgramUniversity of MissouriColumbiaMissouriUSA
- Department of Animal ScienceUniversity of Tennessee Institute of AgricultureKnoxvilleTennesseeUSA
- Department of Large Animal Clinical Sciences, College of Veterinary MedicineUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Robert D. Schnabel
- Division of Animal SciencesUniversity of MissouriColumbiaMissouriUSA
- Genetics Area ProgramUniversity of MissouriColumbiaMissouriUSA
- Institute for Data Science and InformaticsUniversity of MissouriColumbiaMissouriUSA
| | - Jared E. Decker
- Division of Animal SciencesUniversity of MissouriColumbiaMissouriUSA
- Genetics Area ProgramUniversity of MissouriColumbiaMissouriUSA
- Institute for Data Science and InformaticsUniversity of MissouriColumbiaMissouriUSA
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41
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Liu X, Tong X, Zou L, Ju Y, Liu M, Han M, Lu H, Yang H, Wang J, Zong Y, Liu W, Xu X, Jin X, Xiao L, Jia H, Guo R, Zhang T. A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome. Commun Biol 2024; 7:139. [PMID: 38291185 PMCID: PMC10828421 DOI: 10.1038/s42003-024-05822-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024] Open
Abstract
The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.
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Affiliation(s)
- Xiaomin Liu
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xin Tong
- BGI Research, Shenzhen, 518083, China
| | | | - Yanmei Ju
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Mo Han
- BGI Research, Shenzhen, 518083, China
| | - Haorong Lu
- China National Genebank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Huanming Yang
- BGI Research, Shenzhen, 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jian Wang
- BGI Research, Shenzhen, 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Yang Zong
- BGI Research, Shenzhen, 518083, China
| | | | - Xun Xu
- BGI Research, Shenzhen, 518083, China
| | - Xin Jin
- BGI Research, Shenzhen, 518083, China
| | - Liang Xiao
- BGI Research, Shenzhen, 518083, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen, 518083, China
| | - Huijue Jia
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China.
- School of Life Sciences, Fudan University, Shanghai, China.
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42
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Cheng ZJ, Wu H, Chang Z, Cheng J, Wang S, Liu C, Zhang Y, Xu S, Wan Q, Ron J, Liu K, Sun B. The genetic etiology of body fluids on chronic obstructive airways disease. Respir Res 2024; 25:46. [PMID: 38243265 PMCID: PMC10797732 DOI: 10.1186/s12931-023-02661-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/29/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Numerous studies have documented significant alterations in the bodily fluids of Chronic Obstructive Pulmonary Disease (COPD) patients. However, existing literature lacks causal inference due to residual confounding and reverse causality. METHODS Summary-level data for COPD were obtained from two national biobanks: the UK Biobank, comprising 1,605 cases and 461,328 controls, and FinnGen, with 6,915 cases and 186,723 controls. We also validated our findings using clinical data from 2,690 COPD patients and 3,357 healthy controls from the First Affiliated Hospital of Guangzhou Medical University. A total of 44 bodily fluid biomarkers were selected as candidate risk factors. Mendelian randomization (MR) and meta-analyses were used to evaluate the causal effects of these bodily fluids on COPD and lung function (FEV1/FVC). RESULTS Mendelian randomization (MR) and meta-analyses, by integrating data from the UK Biobank and FinnGen cohort, found that 3 bodily fluids indicators (HDLC, EOS, and TP) were causally associated with the risk of COPD, two (EOS and TP) of which is consistent with our observational findings. Moreover, we noticed EOS and TP were causally associated with the risk of lung function (FEV1/FVC). CONCLUSIONS The MR findings and clinical data highlight the independent and significant roles of EOS and TP in the development of COPD and lung function (FEV1/FVC), which might provide a deeper insight into COPD risk factors and supply potential preventative strategies.
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Affiliation(s)
- Zhangkai J Cheng
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangzhou, 510005, Guangdong Province, China
| | - Haojie Wu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Zhenglin Chang
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangzhou, 510005, Guangdong Province, China
| | - Jiahao Cheng
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Suilin Wang
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Changlian Liu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yanxi Zhang
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Shiliang Xu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Qiongqiong Wan
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - JinWen Ron
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Kemin Liu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| | - Baoqing Sun
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
- Guangzhou Laboratory, Guangzhou International Bio Island, XingDaoHuanBei Road, Guangzhou, 510005, Guangdong Province, China.
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43
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Qin C, Chen M, Yu Q, Wang X, Hu T, Lei B, Yan Z, Cheng S. Causal relationship between the blood immune cells and intervertebral disc degeneration: univariable, bidirectional and multivariable Mendelian randomization. Front Immunol 2024; 14:1321295. [PMID: 38268919 PMCID: PMC10806224 DOI: 10.3389/fimmu.2023.1321295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024] Open
Abstract
Background Intervertebral disc degeneration (IVDD) is a prominent contributor to chronic low back pain, impacting millions of individuals annually. Current research on disc degeneration is placing a growing emphasis on the role of the immune system in this process. Nevertheless, the precise relationship between immunity and disc degeneration remains to be fully elucidated. Method We obtained GWAS data for immune cells from the latest summary-level GWAS, including 6,620 individuals from Sardinian and 746,667 individuals from five global populations. Summary results for IVDD were sourced from the FinnGen consortium, comprising 20,001 cases and 164,682 controls. We conducted a comprehensive univariable Mendelian randomization (MR) analysis to explore the potential causal relationship between immune cells and IVDD. Primary estimation was carried out using Inverse-Variance Weighting (IVW). To ensure robustness, we employed additional MR methods such as MR-Egger, Weighted Median, Weighted Mode, and Simple Mode. Various tests were employed to assess pleiotropy and heterogeneity, including the Cochran Q test, leave-one-out test, MR-Egger intercept analysis and MR-PRESSO test. To account for potential confounding factors among the immune cells, we conducted a multivariable MR analysis. Finally, we investigated the possibility of a reverse association between immune cells and IVDD through bidirectional MR. Result In total, our study identified 15 immune cells significantly associated with IVDD through univariable MR. Among these, 9 immune cell types were indicated as potential contributors to IVDD, while 6 were found to have protective effects. Importantly, we observed no evidence of heterogeneity or pleiotropy, signifying the robustness of our results. To mitigate confounding among immune cells, we utilized multivariable MR, leading to the discovery that only 9 immune cell types exerted independent effects on IVDD. These encompassed 7 as risk factors and 2 as protective factors. Additionally, our analysis revealed a bidirectional causal relationship between CD39+ CD4+ T cell %CD4+ T cell and IVDD. Conclusion Our findings suggest a connection between immune cells and the risk of IVDD, shedding light on potential therapeutic avenues for modulating immune cell function in individuals with IVDD. However, the specific underlying mechanisms warrant further investigation in future experiments.
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Affiliation(s)
| | | | | | | | | | | | - Zhengjian Yan
- Department of Orthopedics, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Si Cheng
- Department of Orthopedics, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Pan S, Kang H, Liu X, Li S, Yang P, Wu M, Yuan N, Lin S, Zheng Q, Jia P. COLOCdb: a comprehensive resource for multi-model colocalization of complex traits. Nucleic Acids Res 2024; 52:D871-D881. [PMID: 37941154 PMCID: PMC10767919 DOI: 10.1093/nar/gkad939] [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: 08/15/2023] [Revised: 10/01/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.
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Affiliation(s)
- Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Shuhua Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Mingqiu Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
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45
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Parker N, Cheng W, Hindley GFL, O'Connell KS, Karthikeyan S, Holen B, Shadrin AA, Rahman Z, Karadag N, Bahrami S, Lin A, Steen NE, Ueland T, Aukrust P, Djurovic S, Dale AM, Smeland OB, Frei O, Andreassen OA. Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts. Biol Psychiatry 2024; 95:62-71. [PMID: 37348803 DOI: 10.1016/j.biopsych.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). METHODS Linear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. RESULTS Cortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = -0.13, false discovery rate-corrected p = 4.17 × 10-3; rg_thickness = -0.13, false discovery rate-corrected p = 4.00 × 10-2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. CONCLUSIONS The findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.
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Affiliation(s)
- Nadine Parker
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandeep Karthikeyan
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway; KG Jebsen Thrombosis Research and Expertise Centre, University of Tromsø, Tromsø, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Section of Clinical Immunology and Infectious Disease, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California; Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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46
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Homilius M, Zhu W, Eddy SS, Thompson PC, Zheng H, Warren CN, Evans CG, Kim DD, Xuan LL, Nsubuga C, Strecker Z, Pettit CJ, Cho J, Howie MN, Thaler AS, Wilson E, Wollison B, Smith C, Nascimben JB, Nascimben DN, Lunati GM, Folks HC, Cupelo M, Sridaran S, Rheinstein C, McClennen T, Goto S, Truslow JG, Vandenwijngaert S, MacRae CA, Deo RC. Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases. Nat Genet 2024; 56:37-50. [PMID: 38049662 PMCID: PMC10786715 DOI: 10.1038/s41588-023-01600-x] [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: 03/06/2023] [Accepted: 10/27/2023] [Indexed: 12/06/2023]
Abstract
Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.
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Affiliation(s)
- Max Homilius
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Wandi Zhu
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Samuel S Eddy
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Patrick C Thompson
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Huahua Zheng
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Caleb N Warren
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Chiara G Evans
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David D Kim
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Lucius L Xuan
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Cissy Nsubuga
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Zachary Strecker
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Christopher J Pettit
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jungwoo Cho
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mikayla N Howie
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra S Thaler
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Evan Wilson
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Bruce Wollison
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Courtney Smith
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Julia B Nascimben
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Diana N Nascimben
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Gabriella M Lunati
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Hassan C Folks
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Cupelo
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Suriya Sridaran
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Carolyn Rheinstein
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Taylor McClennen
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shinichi Goto
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - James G Truslow
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sara Vandenwijngaert
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Calum A MacRae
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Rahul C Deo
- One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Atman Health Inc, Needham, MA, USA.
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47
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Cardone KM, Dudek S, Keat K, Bradford Y, Cindi Z, Daar ES, Gulick R, Riddler SA, Lennox JL, Sinxadi P, Haas DW, Ritchie MD. Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:594-610. [PMID: 38160309 PMCID: PMC10764076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ∼26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ∼7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ∼6-10% of variability in multivariate models (including age, sex, and PCs) but only ∼1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.
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Affiliation(s)
- Kathleen M Cardone
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
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48
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O'Brien KO, Pressman EK. Reply to M Jefferds et al. Am J Clin Nutr 2024; 119:234-235. [PMID: 38176778 DOI: 10.1016/j.ajcnut.2023.09.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 01/06/2024] Open
Affiliation(s)
| | - Eva K Pressman
- Department of Obstetrics and Gynecology, University of Rochester School of Medicine, Rochester, NY
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49
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Constantinescu AE, Bull CJ, Jones N, Mitchell R, Burrows K, Dimou N, Bézieau S, Brenner H, Buchanan DD, D’Amato M, Jenkins MA, Moreno V, Pai RK, Um CY, White E, Murphy N, Gunter M, Timpson NJ, Huyghe JR, Vincent EE. Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study. Int J Cancer 2024; 154:94-103. [PMID: 37578112 PMCID: PMC10864681 DOI: 10.1002/ijc.34691] [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: 04/17/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 08/15/2023]
Abstract
Observational studies have suggested a protective role for eosinophils in colorectal cancer (CRC) development and implicated neutrophils, but the causal relationships remain unclear. Here, we aimed to estimate the causal effect of circulating white blood cell (WBC) counts (N = ~550 000) for basophils, eosinophils, monocytes, lymphocytes and neutrophils on CRC risk (N = 52 775 cases and 45 940 controls) using Mendelian randomisation (MR). For comparison, we also examined this relationship using individual-level data from UK Biobank (4043 incident CRC cases and 332 773 controls) in a longitudinal cohort analysis. The inverse-variance weighted (IVW) MR analysis suggested a protective effect of increased basophil count and eosinophil count on CRC risk [OR per 1-SD increase: 0.88, 95% CI: 0.78-0.99, P = .04; OR: 0.93, 95% CI: 0.88-0.98, P = .01]. The protective effect of eosinophils remained [OR per 1-SD increase: 0.88, 95% CI: 0.80-0.97, P = .01] following adjustments for all other WBC subtypes, to account for genetic correlation between the traits, using multivariable MR. A protective effect of increased lymphocyte count on CRC risk was also found [OR: 0.84, 95% CI: 0.76-0.93, P = 6.70e-4] following adjustment. Consistent with MR results, a protective effect for eosinophils in the cohort analysis in the fully adjusted model [RR per 1-SD increase: 0.96, 95% CI: 0.93-0.99, P = .02] and following adjustment for the other WBC subtypes [RR: 0.96, 95% CI: 0.93-0.99, P = .001] was observed. Our study implicates peripheral blood immune cells, in particular eosinophils and lymphocytes, in CRC development, highlighting a need for mechanistic studies to interrogate these relationships.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas Jones
- Institute of Life Science, Swansea University Medical School, Swansea, United Kingdom
| | - Ruth Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010 Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria 3010 Australia
- Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Mauro D’Amato
- Department of Medicine and Surgery, LUM University, Casamassima, Italy
- Gastrointestinal Genetics Lab, CIC bioGUNE - BRTA, Derio, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, Georgia, USA
| | - Emily White
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Marc Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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50
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Johnson AD. Cell perturbation and lasers illuminate the genetics of latent blood cell traits. Nat Genet 2024; 56:16-18. [PMID: 38135721 DOI: 10.1038/s41588-023-01623-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Affiliation(s)
- Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA.
- The Framingham Heart Study, Framingham, MA, USA.
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