1
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Shen J, Jiang L, Wang K, Wang A, Chen F, Newcombe PJ, Haiman CA, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction. Genet Epidemiol 2024; 48:241-257. [PMID: 38606643 DOI: 10.1002/gepi.22562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024]
Abstract
Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.
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Affiliation(s)
- Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Kan Wang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anqi Wang
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Fei Chen
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Paul J Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher A Haiman
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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2
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Burren OS, Dhindsa RS, Deevi SVV, Wen S, Nag A, Mitchell J, Hu F, Loesch DP, Smith KR, Razdan N, Olsson H, Platt A, Vitsios D, Wu Q, Codd V, Nelson CP, Samani NJ, March RE, Wasilewski S, Carss K, Fabre M, Wang Q, Pangalos MN, Petrovski S. Genetic architecture of telomere length in 462,666 UK Biobank whole-genome sequences. Nat Genet 2024:10.1038/s41588-024-01884-7. [PMID: 39192095 DOI: 10.1038/s41588-024-01884-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 07/25/2024] [Indexed: 08/29/2024]
Abstract
Telomeres protect chromosome ends from damage and their length is linked with human disease and aging. We developed a joint telomere length metric, combining quantitative PCR and whole-genome sequencing measurements from 462,666 UK Biobank participants. This metric increased SNP heritability, suggesting that it better captures genetic regulation of telomere length. Exome-wide rare-variant and gene-level collapsing association studies identified 64 variants and 30 genes significantly associated with telomere length, including allelic series in ACD and RTEL1. Notably, 16% of these genes are known drivers of clonal hematopoiesis-an age-related somatic mosaicism associated with myeloid cancers and several nonmalignant diseases. Somatic variant analyses revealed gene-specific associations with telomere length, including lengthened telomeres in individuals with large SRSF2-mutant clones, compared with shortened telomeres in individuals with clonal expansions driven by other genes. Collectively, our findings demonstrate the impact of rare variants on telomere length, with larger effects observed among genes also associated with clonal hematopoiesis.
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Affiliation(s)
- Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ryan S Dhindsa
- Center for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | - Sri V V Deevi
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Sean Wen
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Abhishek Nag
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jonathan Mitchell
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Fengyuan Hu
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Douglas P Loesch
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Neetu Razdan
- Biosciences COPD & IPF, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Henric Olsson
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Adam Platt
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Qiang Wu
- Center for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
- Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN, USA
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester and Leicester NIHR Biomedical Research Centre, Leicester, UK
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and Leicester NIHR Biomedical Research Centre, Leicester, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and Leicester NIHR Biomedical Research Centre, Leicester, UK
| | - Ruth E March
- Precision Medicine & Biosamples, Oncology R&D, AstraZeneca, Dublin, Ireland
| | - Sebastian Wasilewski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Margarete Fabre
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Haematology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Quanli Wang
- Center for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | | | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia.
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3
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Song Y, Li L, Jiang Y, Peng B, Jiang H, Chao Z, Chang X. Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. Schizophr Bull 2024:sbae145. [PMID: 39190819 DOI: 10.1093/schbul/sbae145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited. STUDY DESIGN To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG). STUDY RESULTS Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ. CONCLUSIONS Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.
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Affiliation(s)
- Yingchao Song
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Linzehao Li
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Yue Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Bichen Peng
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Hengxuan Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Xiao Chang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
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4
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Guo X, Ping J, Yang Y, Su X, Shu XO, Wen W, Chen Z, Zhang Y, Tao R, Jia G, He J, Cai Q, Zhang Q, Giles GG, Pearlman R, Rennert G, Vodicka P, Phipps A, Gruber SB, Casey G, Peters U, Long J, Lin W, Zheng W. Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes. Cancer Res 2024; 84:2707-2719. [PMID: 38759092 PMCID: PMC11326986 DOI: 10.1158/0008-5472.can-24-0521] [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: 02/13/2024] [Revised: 03/26/2024] [Accepted: 05/15/2024] [Indexed: 05/19/2024]
Abstract
Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.
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Affiliation(s)
- Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yaohua Yang
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, Virginia
| | - Xinwan Su
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yunjing Zhang
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Ran Tao
- Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jingni He
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
- Department of Medical Genetics, University of Calgary, Calgary, Canada
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Qingrun Zhang
- Department of Mathematics and Statistics, Alberta Children's Hospital Research Institute, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Amanda Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Stephen B Gruber
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Weiqiang Lin
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
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5
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Wu Y, Zheng Z, Thibaut2 L, Goddard ME, Wray NR, Visscher PM, Zeng J. Genome-wide fine-mapping improves identification of causal variants. RESEARCH SQUARE 2024:rs.3.rs-4759390. [PMID: 39149449 PMCID: PMC11326397 DOI: 10.21203/rs.3.rs-4759390/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.
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Affiliation(s)
- Yang Wu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, China
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - 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, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | | | - 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
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - 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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6
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Shore CJ, Villicaña S, El-Sayed Moustafa JS, Roberts AL, Gunn DA, Bataille V, Deloukas P, Spector TD, Small KS, Bell JT. Genetic effects on the skin methylome in healthy older twins. Am J Hum Genet 2024:S0002-9297(24)00254-4. [PMID: 39137780 DOI: 10.1016/j.ajhg.2024.07.010] [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: 12/05/2023] [Revised: 05/22/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.
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Affiliation(s)
- Christopher J Shore
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Veronique Bataille
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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7
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Wu Y, Zheng Z, Thibaut L, Goddard ME, Wray NR, Visscher PM, Zeng J. Genome-wide fine-mapping improves identification of causal variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.18.24310667. [PMID: 39072021 PMCID: PMC11275676 DOI: 10.1101/2024.07.18.24310667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 599 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.
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Affiliation(s)
- Yang Wu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, China
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - 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, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Loic Thibaut
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - 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
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - 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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8
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Lin Z. A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization. HGG ADVANCES 2024; 5:100339. [PMID: 39097774 DOI: 10.1016/j.xhgg.2024.100339] [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: 05/22/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024] Open
Abstract
The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the "no horizontal pleiotropy" assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.
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Affiliation(s)
- Zhaotong Lin
- Department of Statistics, Florida State University, Tallahassee, FL, USA; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
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9
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Sun X, Verma SP, Jia G, Wang X, Ping J, Guo X, Shu XO, Chen J, Derkach A, Cai Q, Liang X, Long J, Offit K, Hun Oh J, Reiner AS, Watt GP, Woods M, Yang Y, Ambrosone CB, Ambs S, Chen Y, Concannon P, Garcia-Closas M, Gu J, Haiman CA, Hu JJ, Huo D, John EM, Knight JA, Li CI, Lynch CF, Mellemkjær L, Nathanson KL, Nemesure B, Olopade OI, Olshan AF, Pal T, Palmer JR, Press MF, Sanderson M, Sandler DP, Troester MA, Zheng W, Bernstein JL, Buas MF, Shu X. Case-Case Genome-Wide Analyses Identify Subtype-Informative Variants That Confer Risk for Breast Cancer. Cancer Res 2024; 84:2533-2548. [PMID: 38832928 PMCID: PMC11293972 DOI: 10.1158/0008-5472.can-23-3854] [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: 12/08/2023] [Revised: 03/15/2024] [Accepted: 05/31/2024] [Indexed: 06/06/2024]
Abstract
Breast cancer includes several subtypes with distinct characteristic biological, pathologic, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate the development of improved prevention and treatment approaches. In this study, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case genome-wide association study (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which, two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared with luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates the enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to two-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among patients with breast cancer. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC. Significance: The discovery of subtype-informative genetic risk variants for breast cancer advances our understanding of the etiologic heterogeneity of breast cancer, which could accelerate the identification of targets and personalized strategies for prevention and treatment.
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Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology, Zhejiang Chinese Medical University, Zhejiang, China
| | - Shiv Prakash Verma
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jianhong Chen
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Andriy Derkach
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiaolin Liang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anne S. Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gordon P. Watt
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meghan Woods
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA, USA
| | - Christine B. Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yu Chen
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Patrick Concannon
- Genetics Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Montserrat Garcia-Closas
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jian Gu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jennifer J. Hu
- The University of Miami School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Esther M. John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Julia A. Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I. Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Charles F. Lynch
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA
| | - Lene Mellemkjær
- Diet, Cancer and Health, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Katherine L. Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara Nemesure
- Stony Brook Medicine, Department of Family, Population, and Preventive Medicine, Stony Brook, NY, USA
| | | | - Andrew F. Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Tuya Pal
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julie R. Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Michael F. Press
- Department of Pathology, Keck School of Medicine, USC/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Dale P. Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Melissa A. Troester
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonine L. Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew F. Buas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Westergaard D, Steinthorsdottir V, Stefansdottir L, Rohde PD, Wu X, Geller F, Tyrmi J, Havulinna AS, Solé-Navais P, Flatley C, Ostrowski SR, Pedersen OB, Erikstrup C, Sørensen E, Mikkelsen C, Bruun MT, Aagaard Jensen B, Brodersen T, Ullum H, Magnus P, Andreassen OA, Njolstad PR, Kolte AM, Krebs L, Nyegaard M, Hansen TF, Feenstra B, Daly M, Lindgren CM, Thorleifsson G, Stefansson OA, Sveinbjornsson G, Gudbjartsson DF, Thorsteinsdottir U, Banasik K, Jacobsson B, Laisk T, Laivuori H, Stefansson K, Brunak S, Nielsen HS. Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage. Nat Genet 2024; 56:1597-1603. [PMID: 39039282 PMCID: PMC11319197 DOI: 10.1038/s41588-024-01839-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: 08/24/2023] [Accepted: 06/21/2024] [Indexed: 07/24/2024]
Abstract
Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal-fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genes HAND2, TBX3 and RAP2C/FRMD7 at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.
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Affiliation(s)
- David Westergaard
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Methods and Analysis, Statistics Denmark, Copenhagen, Denmark
| | | | | | - Palle Duun Rohde
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Xiaoping Wu
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Frank Geller
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Jaakko Tyrmi
- Centre for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare - THL, Helsinki, Finland
| | - Pol Solé-Navais
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Christopher Flatley
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sisse Rye Ostrowski
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Pedersen
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical immunology, Zealand University Hospital, Køge, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Erik Sørensen
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Mie Topholm Bruun
- Clinical Immunological Research Unit, Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | - Thorsten Brodersen
- Department of Clinical immunology, Zealand University Hospital, Køge, Denmark
| | - Henrik Ullum
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Per Magnus
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Pål R Njolstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Astrid Marie Kolte
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Lone Krebs
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Thomas Folkmann Hansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Headache Center, Department of neurology, Copenhagen University Hospital, Glostrup, Denmark
| | - Bjarke Feenstra
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Mark Daly
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M Lindgren
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Big Data Institute Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK
| | | | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, Reykjavik University, Reykjavik, Iceland
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bo Jacobsson
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Centre for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynaecology, Tampere University Hospital, Tampere, Finland
| | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, Reykjavik University, Reykjavik, Iceland
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Henriette Svarre Nielsen
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Yang S, Tong T, Wang H, Li Z, Wang M, Ni K. Causal relationship between air pollution and infections: a two-sample Mendelian randomization study. Front Public Health 2024; 12:1409640. [PMID: 39148655 PMCID: PMC11324489 DOI: 10.3389/fpubh.2024.1409640] [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: 03/30/2024] [Accepted: 07/16/2024] [Indexed: 08/17/2024] Open
Abstract
Background Traditional observational studies exploring the association between air pollution and infections have been limited by small sample sizes and potential confounding factors. To address these limitations, we applied Mendelian randomization (MR) to investigate the potential causal relationships between particulate matter (PM2.5, PM2.5-10, and PM10), nitrogen dioxide, and nitrogen oxide and the risks of infections. Methods Single nucleotide polymorphisms (SNPs) related to air pollution were selected from the genome-wide association study (GWAS) of the UK Biobank. Publicly available summary data for infections were obtained from the FinnGen Biobank and the COVID-19 Host Genetics Initiative. The inverse variance weighted (IVW) meta-analysis was used as the primary method for obtaining the Mendelian randomization (MR) estimates. Complementary analyses were performed using the weighted median method, MR-Egger method, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test. Results The fixed-effect IVW estimate showed that PM2.5, PM2.5-10 and Nitrogen oxides were suggestively associated with COVID-19 [for PM2.5: IVW (fe): OR 3.573(1.218,5.288), PIVW(fe) = 0.021; for PM2.5-10: IVW (fe): OR 2.940(1.385,6.239), PIVW(fe) = 0.005; for Nitrogen oxides, IVW (fe): OR 1.898(1.318,2.472), PIVW(fe) = 0.010]. PM2.5, PM2.5-10, PM10, and Nitrogen oxides were suggestively associated with bacterial pneumonia [for PM2.5: IVW(fe): OR 1.720 (1.007, 2.937), PIVW(fe) = 0.047; for PM2.5-10: IVW(fe): OR 1.752 (1.111, 2.767), P IVW(fe) = 0.016; for PM10: IVW(fe): OR 2.097 (1.045, 4.208), PIVW(fe) = 0.037; for Nitrogen oxides, IVW(fe): OR 3.907 (1.209, 5.987), PIVW(fe) = 0.023]. Furthermore, Nitrogen dioxide was suggestively associated with the risk of acute upper respiratory infections, while all air pollution were not associated with intestinal infections. Conclusions Our results support a role of related air pollution in the Corona Virus Disease 2019, bacterial pneumonia and acute upper respiratory infections. More work is need for policy formulation to reduce the air pollution and the emission of toxic and of harmful gas.
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Affiliation(s)
- Shengyi Yang
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tong Tong
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hong Wang
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhenwei Li
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Wang
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaiwen Ni
- Department of Infection Control, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Lelievre R, Rakesh M, Hysi PG, Little J, Freeman EE, Roy-Gagnon MH. Effect modification by sex of genetic associations of vitamin C related metabolites in the Canadian Longitudinal study on aging. Front Genet 2024; 15:1411931. [PMID: 39144724 PMCID: PMC11322087 DOI: 10.3389/fgene.2024.1411931] [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: 04/03/2024] [Accepted: 07/04/2024] [Indexed: 08/16/2024] Open
Abstract
Introduction: Vitamin C is an essential nutrient. Sex differences in serum vitamin C concentrations have been observed but are not fully known. Investigation of levels of metabolites may help shed light on how dietary and other environmental exposures interact with molecular processes. O-methylascorbate and ascorbic acid 2-sulfate are two metabolites in the vitamin C metabolic pathway. Past research has found genetic factors that influence the levels of these two metabolites. Therefore, we investigated possible effect modification by sex of genetic variant-metabolite associations and characterized the biological function of these interactions. Methods: We included individuals of European descent from the Canadian Longitudinal Study on Aging with available genetic and metabolic data (n = 9004). We used linear mixed models to tests for genome-wide associations with O-methylascorbate and ascorbic acid 2-sulfate, with and without a sex interaction. We also investigated the biological function of the important genetic variant-sex interactions found for each metabolite. Results: Two genome-wide statistically significant (p value < 5 × 10-8) interaction effects and several suggestive (p value < 10-5) interaction effects were found. These suggestive interaction effects were mapped to several genes including HSD11B2, associated with sex hormones, and AGRP, associated with hunger drive. The genes mapped to O-methylascorbate were differently expressed in the testis tissues, and the genes mapped to ascorbic acid 2-sulfate were differently expressed in stomach tissues. Discussion: By understanding the genetic factors that impact metabolites associated with vitamin C, we can better understand its function in disease risk and the mechanisms behind sex differences in vitamin C concentrations.
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Affiliation(s)
- Rebecca Lelievre
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Mohan Rakesh
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Pirro G. Hysi
- Section of Ophthalmology, School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Ellen E. Freeman
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
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Tian H, Xie C, Teng B, Zeng Q, Zhao Y, Li F, Jiang C, Chen Z. The genetic effects of hormones modulated by the Pituitary-Thyroid/Adrenal/Gonadal axis on the risk of developing venous thromboembolism: a mendelian randomization study. BMC Cardiovasc Disord 2024; 24:383. [PMID: 39054435 PMCID: PMC11270940 DOI: 10.1186/s12872-024-04039-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND The aim of this study was to explore the genetic effects of hormones modulated through the pituitary-thyroid/adrenal/gonadal axis on the risk of developing venous thromboembolism (VTE) and to investigate the potentially causal relationships between them. METHODS A two-sample Mendelian randomization (MR) design was used. The single-nucleotide polymorphisms (SNPs) used as instrumental variables for various hormones and hormone-mediated diseases were derived from published genome-wide association studies (GWASs). Summary statistics for the risk of developing VTE (including deep venous thrombosis [DVT] and pulmonary embolism [PE]) were obtained from the UK Biobank and the FinnGen consortium. Inverse-variance weighting (IVW) was applied as the primary method to analyse causal associations. Other MR methods were used for supplementary estimates and sensitivity analysis. RESULTS A genetic predisposition to greater free thyroxine (FT4) concentrations was associated with a greater risk of developing DVT (OR = 1.0007, 95%CI [1.0001-1.0013], p = 0.0174) and VTE (OR = 1.0008, 95%CI [1.0002-1.0013], p = 0.0123). Genetically predicted hyperthyroidism was significantly associated with an increased risk of developing DVT (OR = 1.0685, 95%CI [1.0139-1.1261], p = 0.0134) and VTE (OR = 1.0740, 95%CI [1.0165-1.1348], p = 0.0110). According to the initial MR analysis, testosterone concentrations were positively associated with the risk of developing VTE (OR = 1.0038, 95%CI [1.004-1.0072], p = 0.0285). After sex stratification, estradiol concentrations were positively associated with the risk of developing DVT (OR = 1.0143, 95%CI [1.0020-1.0267], p = 0.0226) and VTE (OR = 1.0156, 95%CI [1.0029-1.0285], p = 0.0158) in females, while the significant relationship between testosterone and VTE did not persist. SHBG rs858518 was identified as the only SNP that was associated with an increased risk of developing VTE, mediated by estradiol, in females. CONCLUSIONS Genetically predicted hyperthyroidism and increased FT4 concentrations were positively associated with the risk of developing VTE. The effects of genetically predicted sex hormones on the risk of developing VTE differed between males and females. Greater genetically predicted estradiol concentrations were associated with an increased risk of developing VTE in females, while the SHBG rs858518 variant may become a potential prevention and treatment target for female VTE.
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Affiliation(s)
- Hao Tian
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Chaozheng Xie
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Biyun Teng
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Qiu Zeng
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Yu Zhao
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Fenghe Li
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Chuli Jiang
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China
| | - Zheng Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, 1 N. Youyi Street, Chongqing, 400016, China.
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Pozarickij A, Gan W, Lin K, Clarke R, Fairhurst-Hunter Z, Koido M, Kanai M, Okada Y, Kamatani Y, Bennett D, Du H, Chen Y, Yang L, Avery D, Guo Y, Yu M, Yu C, Schmidt Valle D, Lv J, Chen J, Peto R, Collins R, Li L, Chen Z, Millwood IY, Walters RG. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults. Nat Commun 2024; 15:6265. [PMID: 39048560 PMCID: PMC11269703 DOI: 10.1038/s41467-024-50297-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: 01/27/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, including 74 newly-reported associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated that BP traits contribute differently to the causal associations of BP with CVD. In particular, only pulse pressure was independently causally associated with carotid plaque. These findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their roles in disease risk.
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Affiliation(s)
- Alfred Pozarickij
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Wei Gan
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zammy Fairhurst-Hunter
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Masaru Koido
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, 113-0033, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, 230- 0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
| | - Yoichiro Kamatani
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Derrick Bennett
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, 100037, Beijing, China
| | - Min Yu
- Zhejiang CDC, Zhejiang, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China
| | - Dan Schmidt Valle
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China
| | - Junshi Chen
- China National Center For Food Safety Risk Assessment, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China.
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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15
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Ren S, Sun C, Zhai W, Wei W, Liu J. Gaining new insights into the etiology of ulcerative colitis through a cross-tissue transcriptome-wide association study. Front Genet 2024; 15:1425370. [PMID: 39092429 PMCID: PMC11291327 DOI: 10.3389/fgene.2024.1425370] [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: 04/29/2024] [Accepted: 06/25/2024] [Indexed: 08/04/2024] Open
Abstract
Background Genome-wide association studies (GWASs) have identified 38 loci associated with ulcerative colitis (UC) susceptibility, but the risk genes and their biological mechanisms remained to be comprehensively elucidated. Methods Multi-marker analysis of genomic annotation (MAGMA) software was used to annotate genes on GWAS summary statistics of UC from FinnGen database. Genetic analysis was performed to identify risk genes. Cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signatures (UTMOST) was performed to compare GWAS summary statistics with gene expression matrix (from Genotype-Tissue Expression Project) for data integration. Subsequently, we used FUSION software to select key genes from the individual tissues. Additionally, conditional and joint analysis was conducted to improve our understanding on UC. Fine-mapping of causal gene sets (FOCUS) software was employed to accurately locate risk genes. The results of the four genetic analyses (MAGMA, UTMOST, FUSION and FOCUS) were combined to obtain a set of UC risk genes. Finally, Mendelian randomization (MR) analysis and Bayesian colocalization analysis were conducted to determine the causal relationship between the risk genes and UC. To test the robustness of our findings, the same approaches were taken to verify the GWAS data of UC on IEU. Results Multiple correction tests screened PIM3 as a risk gene for UC. The results of Bayesian colocalization analysis showed that the posterior probability of hypothesis 4 was 0.997 and 0.954 in the validation dataset. MR was conducted using the inverse variance weighting method and two single nucleotide polymorphisms (SNPs, rs28645887 and rs62231924) were included in the analysis (p < 0.001, 95%CI: 1.45-1.89). In the validation dataset, MR result was p < 0.001, 95%CI: 1.19-1.72, indicating a clear causal relationship between PIM3 and UC. Conclusion Our study validated PIM3 as a key risk gene for UC and its expression level may be related to the risk of UC, providing a novel reference for further improving the current understanding on the genetic structure of UC.
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Affiliation(s)
- Shijie Ren
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Chaodi Sun
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Wenjing Zhai
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Wenli Wei
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Jianping Liu
- Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Department of Gastroenterology, The First Affiliated Hospital of Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
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16
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Chen DM, Dong R, Kachuri L, Hoffmann TJ, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Justice AC, Madduri RK, Rodriguez AA, Van Den Eeden SK, Chanock SJ, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer. HGG ADVANCES 2024; 5:100315. [PMID: 38845201 PMCID: PMC11262184 DOI: 10.1016/j.xhgg.2024.100315] [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: 01/17/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024] Open
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.
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Affiliation(s)
- Dorothy M Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - John P Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kerry R Schaffer
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Translational Medicine, Lund University, 21428 Malmö, Sweden
| | | | | | | | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan D Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA.
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
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17
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Lin Z, Pan W. A robust cis-Mendelian randomization method with application to drug target discovery. Nat Commun 2024; 15:6072. [PMID: 39025905 PMCID: PMC11258283 DOI: 10.1038/s41467-024-50385-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: 08/04/2023] [Accepted: 07/08/2024] [Indexed: 07/20/2024] Open
Abstract
Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA.
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA
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18
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Yang Y, Chen Y, Xu S, Guo X, Jia G, Ping J, Shu X, Zhao T, Yuan F, Wang G, Xie Y, Ci H, Liu H, Qi Y, Liu Y, Liu D, Li W, Ye F, Shu XO, Zheng W, Li L, Cai Q, Long J. Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk. Nat Commun 2024; 15:6071. [PMID: 39025880 PMCID: PMC11258330 DOI: 10.1038/s41467-024-50404-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: 09/06/2023] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.
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Affiliation(s)
- Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA.
| | - Yaxin Chen
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shuai Xu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tianying Zhao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fangcheng Yuan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gang Wang
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yufang Xie
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Ci
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongmo Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yawen Qi
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yongjun Liu
- Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Dan Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Li Li
- Department of Family Medicine, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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19
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Rahimov F, Nieminen P, Kumari P, Juuri E, Nikopensius T, Paraiso K, German J, Karvanen A, Kals M, Elnahas AG, Karjalainen J, Kurki M, Palotie A, Heliövaara A, Esko T, Jukarainen S, Palta P, Ganna A, Patni AP, Mar D, Bomsztyk K, Mathieu J, Ruohola-Baker H, Visel A, Fakhouri WD, Schutte BC, Cornell RA, Rice DP. High incidence and geographic distribution of cleft palate cases in Finland are associated with a regulatory variant in IRF6. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.09.24310146. [PMID: 39040165 PMCID: PMC11261923 DOI: 10.1101/2024.07.09.24310146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
In Finland the frequency of isolated cleft palate (CP) is higher than that of isolated cleft lip with or without cleft palate (CL/P). This trend contrasts to that in other European countries but its genetic underpinnings are unknown. We performed a genome-wide association study for orofacial clefts, which include CL/P and CP, in the Finnish population. We identified rs570516915, a single nucleotide polymorphism that is highly enriched in Finns and Estonians, as being strongly associated with CP ( P = 5.25 × 10 -34 , OR = 8.65, 95% CI 6.11-12.25), but not with CL/P ( P = 7.2 × 10 -5 ), with genome-wide significance. The risk allele frequency of rs570516915 parallels the regional variation of CP prevalence in Finland, and the association was replicated in independent cohorts of CP cases from Finland ( P = 8.82 × 10 -28 ) and Estonia ( P = 1.25 × 10 -5 ). The risk allele of rs570516915 disrupts a conserved binding site for the transcription factor IRF6 within a previously characterized enhancer upstream of the IRF6 gene. Through reporter assay experiments we found that the risk allele of rs570516915 diminishes the enhancer activity. Oral epithelial cells derived from CRISPR-Cas9 edited induced pluripotent stem cells demonstrate that the CP-associated allele of rs570516915 concomitantly decreases the binding of IRF6 and the expression level of IRF6 , suggesting impaired IRF6 autoregulation as a molecular mechanism underlying the risk for CP.
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20
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Saevarsdottir S, Bjarnadottir K, Markusson T, Berglund J, Olafsdottir TA, Halldorsson GH, Rutsdottir G, Gunnarsdottir K, Arnthorsson AO, Lund SH, Stefansdottir L, Gudmundsson J, Johannesson AJ, Sturluson A, Oddsson A, Halldorsson B, Ludviksson BR, Ferkingstad E, Ivarsdottir EV, Sveinbjornsson G, Grondal G, Masson G, Eldjarn GH, Thorisson GA, Kristjansdottir K, Knowlton KU, Moore KHS, Gudjonsson SA, Rognvaldsson S, Knight S, Nadauld LD, Holm H, Magnusson OT, Sulem P, Gudbjartsson DF, Rafnar T, Thorleifsson G, Melsted P, Norddahl GL, Jonsdottir I, Stefansson K. Start codon variant in LAG3 is associated with decreased LAG-3 expression and increased risk of autoimmune thyroid disease. Nat Commun 2024; 15:5748. [PMID: 38982041 PMCID: PMC11233504 DOI: 10.1038/s41467-024-50007-7] [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: 09/15/2023] [Accepted: 06/27/2024] [Indexed: 07/11/2024] Open
Abstract
Autoimmune thyroid disease (AITD) is a common autoimmune disease. In a GWAS meta-analysis of 110,945 cases and 1,084,290 controls, 290 sequence variants at 225 loci are associated with AITD. Of these variants, 115 are previously unreported. Multiomics analysis yields 235 candidate genes outside the MHC-region and the findings highlight the importance of genes involved in T-cell regulation. A rare 5'-UTR variant (rs781745126-T, MAF = 0.13% in Iceland) in LAG3 has the largest effect (OR = 3.42, P = 2.2 × 10-16) and generates a novel start codon for an open reading frame upstream of the canonical protein translation initiation site. rs781745126-T reduces mRNA and surface expression of the inhibitory immune checkpoint LAG-3 co-receptor on activated lymphocyte subsets and halves LAG-3 levels in plasma among heterozygotes. All three homozygous carriers of rs781745126-T have AITD, of whom one also has two other T-cell mediated diseases, that is vitiligo and type 1 diabetes. rs781745126-T associates nominally with vitiligo (OR = 5.1, P = 6.5 × 10-3) but not with type 1 diabetes. Thus, the effect of rs781745126-T is akin to drugs that inhibit LAG-3, which unleash immune responses and can have thyroid dysfunction and vitiligo as adverse events. This illustrates how a multiomics approach can reveal potential drug targets and safety concerns.
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Affiliation(s)
- Saedis Saevarsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
- Department of Medicine, Landspitali, the National University Hospital of Iceland, Reykjavik, Iceland.
| | | | - Thorsteinn Markusson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Thorunn A Olafsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Gisli H Halldorsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Gudrun Rutsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | - Ari J Johannesson
- Department of Medicine, Landspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | | | - Björn R Ludviksson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology, Landspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Erna V Ivarsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Gerdur Grondal
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Medicine, Landspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | | | | | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- School of Medicine, University of Utah, Salt Lake City, UT, USA
| | | | | | | | - Stacey Knight
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
| | | | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Pall Melsted
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Ingileif Jonsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology, Landspitali, the National University Hospital of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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21
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Ramírez J, van Duijvenboden S, Young WJ, Chen Y, Usman T, Orini M, Lambiase PD, Tinker A, Bell CG, Morris AP, Munroe PB. Fine mapping of candidate effector genes for heart rate. Hum Genet 2024:10.1007/s00439-024-02684-z. [PMID: 38969939 DOI: 10.1007/s00439-024-02684-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/19/2024] [Indexed: 07/07/2024]
Abstract
An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.
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Affiliation(s)
- Julia Ramírez
- Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain.
- Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Zaragoza, Spain.
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
| | - Stefan van Duijvenboden
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK.
- Institute of Cardiovascular Science, University College London, London, UK.
| | - William J Young
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, London, EC1A 7BE, UK
| | - Yutang Chen
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | | | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, London, EC1A 7BE, UK
| | - Andrew Tinker
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Barts Cardiovascular Biomedical Research Centre, National Institute of Health and Care Research, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Christopher G Bell
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Andrew P Morris
- Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- National Institute of Health and Care Research, Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
- Khalifa University, Abu Dhabi, United Arab Emirates.
- Barts Cardiovascular Biomedical Research Centre, National Institute of Health and Care Research, Queen Mary University of London, London, EC1M 6BQ, UK.
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22
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Hui D, Sanford E, Lorenz K, Damrauer SM, Assimes TL, Thom CS, Voight BF. Mendelian randomization analyses clarify the effects of height on cardiovascular diseases. PLoS One 2024; 19:e0298786. [PMID: 38959188 PMCID: PMC11221663 DOI: 10.1371/journal.pone.0298786] [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: 05/09/2023] [Accepted: 01/30/2024] [Indexed: 07/05/2024] Open
Abstract
An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by cardiovascular, anthropometric, lung function, and lifestyle-related risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.
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Affiliation(s)
- Daniel Hui
- Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Eric Sanford
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Kimberly Lorenz
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Institute for Translational Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, United States of America
| | - Scott M. Damrauer
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, United States of America
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Themistocles L. Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, United States of America
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Christopher S. Thom
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Institute for Translational Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, United States of America
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23
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Zhang DD, He XY, Yang L, Wu BS, Fu Y, Liu WS, Guo Y, Fei CJ, Kang JJ, Feng JF, Cheng W, Tan L, Yu JT. Exome sequencing identifies novel genetic variants associated with varicose veins. PLoS Genet 2024; 20:e1011339. [PMID: 38980841 PMCID: PMC11233024 DOI: 10.1371/journal.pgen.1011339] [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: 09/30/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS Our findings highlight the importance of causal genes for VV and provide new directions for treatment.
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Affiliation(s)
- Dan-Dan Zhang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - 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
| | - 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
| | - 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
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 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
| | - 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
| | - 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
| | - Ju-Jiao Kang
- 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
| | - Jian-Feng 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, United Kingdom
| | - 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, United Kingdom
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 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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Chen Y, Zheng X, Liu C, Liu T, Lin S, Xie H, Zhang H, Shi J, Liu X, Bu Z, Guo S, Huang Z, Deng L, Shi H. Anthropometrics and cancer prognosis: a multicenter cohort study. Am J Clin Nutr 2024; 120:47-55. [PMID: 38763424 DOI: 10.1016/j.ajcnut.2024.05.016] [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: 12/24/2023] [Revised: 03/21/2024] [Accepted: 05/01/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Anthropometric indicators have been shown to be associated with the prognosis of patients with cancer. However, any single anthropometric index has limitation in predicting the prognosis. OBJECTIVES This study aimed to observe the predictive role of 7 anthropometric indicators based on body size on the prognosis of patients with cancer. METHODS A principal component analysis (PCA) on 7 anthropometric measurements: height, weight, BMI, hand grip strength (HGS), triceps skinfold thickness (TSF), mid-upper arm circumference (MAC), and calf circumference (CAC) was conducted. Principal components (PCs) were derived from this analysis. Cox regression analysis was used to investigate the association between the prognosis of patients with cancer and the PCs. Subgroups and sensitivity analyses were also conducted. RESULTS Through PCA, 4 distinct PCs were identified, collectively explaining 88.3% of the variance. PC1, primarily characterized by general obesity, exhibited a significant inverse association with risk of cancer-related death (adjusted hazard ratio [HR]: 0.86; 95% confidence interval [CI]: 0.83, 0.88). PC2 (short stature with high TSF) was not significantly associated with cancer prognosis. PC3 (high BMI coupled with low HGS) demonstrated a significant increase with risk of cancer-related death (adjusted HR: 1.08; 95% CI: 1.05, 1.11). PC4 (tall stature with high TSF) exhibited a significant association with increased cancer risk (adjusted HR: 1.05; 95% CI: 1.02, 1.07). These associations varied across different cancer stages. The stability of the results was confirmed through sensitivity analyses. CONCLUSIONS Different body sizes are associated with distinct prognostic outcomes in patients with cancer. The impact of BMI on prognosis is influenced by both HGS and subcutaneous fat. This finding may influence the clinical care of cancer and improve the survival of cancer patients.
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Affiliation(s)
- Yue Chen
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Xin Zheng
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Chenan Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Tong Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Shiqi Lin
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Hailun Xie
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Heyang Zhang
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Jinyu Shi
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Xiaoyue Liu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Zhaoting Bu
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Shubin Guo
- Emergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Fengtai, China
| | - Zhenghui Huang
- Emergency Medicine Clinical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Fengtai, China
| | - Li Deng
- Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China.
| | - Hanping Shi
- The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China; Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, China; Laboratory for Clinical Medicine, Capital Medical University, Beijing, China; Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China.
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25
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Li JL, McClellan JC, Zhang H, Gao G, Huo D. Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer. J Natl Cancer Inst 2024; 116:1105-1115. [PMID: 38400758 PMCID: PMC11223833 DOI: 10.1093/jnci/djae041] [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: 09/22/2023] [Revised: 01/25/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown. METHODS We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium. RESULTS Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC. CONCLUSION Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.
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Affiliation(s)
- James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Julian C McClellan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, IL, USA
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26
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Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat SM, Kentistou KA, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman BR, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman ND, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Genetic drivers and cellular selection of female mosaic X chromosome loss. Nature 2024; 631:134-141. [PMID: 38867047 DOI: 10.1038/s41586-024-07533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/07/2024] [Indexed: 06/14/2024]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.
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Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Seyedeh M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valdislav Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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Chen BD, Lee C, Tapia AL, Reiner AP, Tang H, Kooperberg C, Manson JE, Li Y, Raffield LM. Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study. Genet Epidemiol 2024. [PMID: 38940271 DOI: 10.1002/gepi.22578] [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: 09/11/2023] [Revised: 05/26/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024]
Abstract
In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.
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Affiliation(s)
- Brian D Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chanhwa Lee
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Amanda L Tapia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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28
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Breeyear JH, Hellwege JN, Schroeder PH, House JS, Poisner HM, Mitchell SL, Charest B, Khakharia A, Basnet TB, Halladay CW, Reaven PD, Meigs JB, Rhee MK, Sun Y, Lynch MG, Bick AG, Wilson OD, Hung AM, Nealon CL, Iyengar SK, Rotroff DM, Buse JB, Leong A, Mercader JM, Sobrin L, Brantley MA, Peachey NS, Motsinger-Reif AA, Wilson PW, Sun YV, Giri A, Phillips LS, Edwards TL. Adaptive selection at G6PD and disparities in diabetes complications. Nat Med 2024:10.1038/s41591-024-03089-1. [PMID: 38918629 DOI: 10.1038/s41591-024-03089-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024]
Abstract
Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.
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Affiliation(s)
- Joseph H Breeyear
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Jacklyn N Hellwege
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Philip H Schroeder
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Hannah M Poisner
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Sabrina L Mitchell
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA, USA
| | - Anjali Khakharia
- Atlanta VA Medical Center, Decatur, GA, USA
- Department of Medicine and Geriatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Til B Basnet
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ, USA
- College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mary K Rhee
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yang Sun
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Administration Palo Alto Health Care System, Palo Alto, California, USA
| | | | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Otis D Wilson
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Cari L Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Department of Ophthalmology & Visual Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sudha K Iyengar
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH, USA
| | - John B Buse
- Division of Endocrinology & Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Aaron Leong
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M Mercader
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Lucia Sobrin
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Milam A Brantley
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neal S Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Peter W Wilson
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yan V Sun
- Atlanta VA Medical Center, Decatur, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Ayush Giri
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Lawrence S Phillips
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA.
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Adams MJ. Genome-wide study of half a million individuals with major depression identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.29.24306535. [PMID: 38746223 PMCID: PMC11092713 DOI: 10.1101/2024.04.29.24306535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
In a genome-wide association study (GWAS) of 685,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries and across diverse and admixed ancestries, we identify 697 independent associations at 636 genetic loci, 293 of which are novel. Using fine-mapping and functional genomic datasets, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. Leveraging new single-cell gene expression data, we conducted a causal neural cell type enrichment analysis that implicated excitatory and inhibitory midbrain and forebrain neurons, peptidergic neurons, and medium spiny neurons in MD. Critically, our findings are enriched for the targets of antidepressants and provide potential antidepressant repurposing opportunities (e.g., pregabalin and modafinil). Polygenic scores (PGS) from European ancestries explained up to 5.7% of the variance in liability to MD in European samples and PGS trained using either European or multi-ancestry data significantly predicted case control status across all four diverse ancestries. These findings represent a major advance in our understanding of MD across global populations. We provide evidence that MD GWAS reveals known and novel biological targets that may be used to target and develop pharmacotherapies addressing the considerable unmet need for effective treatment.
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Affiliation(s)
- Mark J Adams
- Psychiatric Genomics Consortium Major Depressive Disorder Working Group
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30
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Xu C, Ren X, Lin P, Jin S, Zhang Z. Exploring the causal effects of sleep characteristics on TMD-related pain: a two-sample Mendelian randomization study. Clin Oral Investig 2024; 28:384. [PMID: 38888691 DOI: 10.1007/s00784-024-05776-2] [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: 03/21/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES The study was to explore the causal effects of sleep characteristics on temporomandibular disorder (TMD)-related pain using Mendelian randomization (MR) analysis. MATERIALS AND METHODS Five sleep characteristics (short sleep, insomnia, chronotype, snoring, sleep apnea) were designated as exposure factors. Data were obtained from previous publicized genome-wide association studies and single nucleotide polymorphisms (SNPs) strongly associated with them were utilized as instrumental variables (IVs). TMD-related pain was designed as outcome variable and sourced from the FinnGens database. MR analysis was employed to explore the causal effects of the five sleep characteristics on TMD-related pain. The causal effect was analyzed using the inverse variance-weighted (IVW), weighted median, and MR-Egger methods. Subsequently, sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests. RESULTS A causal effect of short sleep on TMD-related pain was revealed by IVW (OR: 1.60, 95% CI: 1.06-2.41, P = 0.026). No causal relationship was identified between other sleep characteristics (insomnia, chronotype, snoring, sleep apnea) and TMD-related pain. CONCLUSIONS Our study suggests that short sleep may increase the risk of TMD-related pain, while there was no causal relationship between other sleep characteristics and TMD-related pain. Further studies are warranted to deepen and definitively clarify their relationship. CLINICAL RELEVANCE These findings reveal that the short sleep may be a risk factor of TMD-related pain and highlight the potential therapeutical effect of extending sleep time on alleviating TMD-related pain.
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Affiliation(s)
- Chao Xu
- Department of Orthodontics, Jinan Stomatological Hospital, 82 Weier Road, Jinan, Shandong, China
| | - Xusheng Ren
- Department of Orthodontics, Jinan Stomatological Hospital, 82 Weier Road, Jinan, Shandong, China
| | - Peng Lin
- Department of Orthodontics, Jinan Stomatological Hospital, 82 Weier Road, Jinan, Shandong, China
| | - Shumei Jin
- Department of Orthodontics, Jinan Stomatological Hospital, 82 Weier Road, Jinan, Shandong, China
| | - Zhichao Zhang
- Department of Oral and Maxillofacial Surgery, the Second Hospital of Shandong University, 247 Beiyuan Street, Jinan, Shandong, China.
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31
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Zou Y, Carbonetto P, Xie D, Wang G, Stephens M. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.14.536893. [PMID: 37425935 PMCID: PMC10327118 DOI: 10.1101/2023.04.14.536893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.
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Affiliation(s)
- Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Dongyue Xie
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Gao Wang
- Gertrude. H. Sergievsky Center, Department of Neurology, Columbia University, New York, NY, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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32
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Li L, Zhang M, Gu M, Li J, Li Z, Zhang R, Du C, Lv Y. The causal relationship between autoimmune diseases and age-related macular degeneration: A two-sample mendelian randomization study. PLoS One 2024; 19:e0303170. [PMID: 38857222 PMCID: PMC11164335 DOI: 10.1371/journal.pone.0303170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 04/18/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVE The aim of this study is to investigate the potential causal relationship between autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, and Type 1 diabetes, and age-related macular degeneration (AMD). By utilizing the two-sample Mendelian Randomization (MR) approach, we endeavor to address this complex medical issue. METHODS Genome-wide association study (GWAS) data for autoimmune diseases and AMD were obtained from the IEU Open GWAS database and the FinnGen consortium. A series of stringent SNP filtering steps was applied to ensure the reliability of the genetic instruments. MR analyses were conducted using the TwoSampleMR and MR-PRESSO packages in R. The inverse-variance weighted (IVW) method served as the primary analysis, complemented by multiple supplementary analyses and sensitivity tests. RESULTS Within the discovery sample, only a statistically significant inverse causal relationship between multiple sclerosis (MS) and AMD was observed (OR = 0.92, 95% CI: 0.88-0.97, P = 0.003). This finding was confirmed in the replication sample (OR = 0.85, 95% CI: 0.80-0.89, P = 3.32×10-12). No statistically significant associations were detected between systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, and Type 1 diabetes and AMD. CONCLUSION Strong evidence is provided by this study to support the existence of an inverse causal relationship between multiple sclerosis and age-related macular degeneration. However, no causal evidence was found linking other autoimmune diseases with AMD. These findings not only offer novel insights into the potential etiological mechanisms underlying AMD but also suggest possible directions for future clinical interventions.
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Affiliation(s)
- Linrui Li
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Mingyue Zhang
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Moxiu Gu
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Jun Li
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Zhiyuan Li
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Rong Zhang
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Chuanwang Du
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
| | - Yun Lv
- Department of Ophthalmology, Fushun People’s Hospital, Fushun, China. Sichuan Province, P.R. China
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Kim B, Kim DS, Shin JG, Leem S, Cho M, Kim H, Gu KN, Seo JY, You SW, Martin AR, Park SG, Kim Y, Jeong C, Kang NG, Won HH. Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color. Nat Commun 2024; 15:4874. [PMID: 38849341 PMCID: PMC11161515 DOI: 10.1038/s41467-024-49031-4] [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: 07/04/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
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Affiliation(s)
- Beomsu Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Joong-Gon Shin
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Sangseob Leem
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Minyoung Cho
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hanji Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Ki-Nam Gu
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Jung Yeon Seo
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Seung Won You
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02141, USA
| | - Sun Gyoo Park
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Yunkwan Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Nae Gyu Kang
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea.
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea.
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Tan MMX, Lawton MA, Pollard MI, Brown E, Real R, Carrasco AM, Bekadar S, Jabbari E, Reynolds RH, Iwaki H, Blauwendraat C, Kanavou S, Hubbard L, Malek N, Grosset KA, Bajaj N, Barker RA, Burn DJ, Bresner C, Foltynie T, Wood NW, Williams-Gray CH, Andreassen OA, Toft M, Elbaz A, Artaud F, Brice A, Corvol JC, Aasly J, Farrer MJ, Nalls MA, Singleton AB, Williams NM, Ben-Shlomo Y, Hardy J, Hu MTM, Grosset DG, Shoai M, Pihlstrøm L, Morris HR. Genome-wide determinants of mortality and motor progression in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:113. [PMID: 38849413 PMCID: PMC11161485 DOI: 10.1038/s41531-024-00729-8] [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: 02/05/2024] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
There are 90 independent genome-wide significant genetic risk variants for Parkinson's disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.
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Affiliation(s)
- Manuela M X Tan
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK.
- UCL Movement Disorders Centre, University College London, London, UK.
| | - Michael A Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Miriam I Pollard
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Emmeline Brown
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Raquel Real
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Alejandro Martinez Carrasco
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Samir Bekadar
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Edwin Jabbari
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Regina H Reynolds
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica, Washington DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Sofia Kanavou
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Leon Hubbard
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Naveed Malek
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Katherine A Grosset
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Nin Bajaj
- Clinical Neurosciences, University of Nottingham, Nottingham, UK
| | - Roger A Barker
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - David J Burn
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Catherine Bresner
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Nicholas W Wood
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
| | - Caroline H Williams-Gray
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alexis Elbaz
- Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP, 94807, Villejuif, France
| | - Fanny Artaud
- Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP, 94807, Villejuif, France
| | - Alexis Brice
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Departement of Neurology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jan Aasly
- Department of Neurology, St. Olavs Hospital, Trondheim, Norway
- Department of Neuromedicine and Movement Science (INB), Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Matthew J Farrer
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica, Washington DC, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Nigel M Williams
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Hardy
- UCL Movement Disorders Centre, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Michele T M Hu
- Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Department of Clinical Neurology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Donald G Grosset
- School of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Maryam Shoai
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK.
- UCL Movement Disorders Centre, University College London, London, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.
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Somerville EN, Krohn L, Senkevich K, Yu E, Ahmad J, Asayesh F, Ruskey JA, Speigelman D, Fahn S, Waters C, Sardi SP, Alcalay RN, Gan-Or Z. Genome-wide association study of glucocerebrosidase activity modifiers. RESEARCH SQUARE 2024:rs.3.rs-4425669. [PMID: 38883744 PMCID: PMC11177962 DOI: 10.21203/rs.3.rs-4425669/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
One of the most common genetic risk factors for Parkinson's disease (PD) are variants in GBA1, which encodes the lysosomal enzyme glucocerebrosidase (GCase). GCase deficiency has been associated with an increased PD risk, but not all individuals with low GCase activity are carriers of GBA1 mutations, suggesting other factors may be acting as modifiers. We aimed to discover common variants associated with GCase activity, as well as replicate previously reported associations, by performing a genome-wide association study using two independent cohorts: a Columbia University cohort consisting of 697 PD cases and 347 controls and the Parkinson's Progression Markers Initiative (PPMI) cohort consisting of 357 PD cases and 163 controls. As expected, GBA1 variants have the strongest association with decreased activity, led by p.N370S (beta = -4.36, se = 0.32, p = 5.05e-43). We also identify a novel association in the GAA locus (encoding for acid alpha-glucosidase, beta = -0.96, se = 0.17, p = 5.23e-09) that may be the result of an interaction between GCase and acid alpha-glucosidase based on various interaction analyses. Lastly, we show that several PD-risk loci are potentially associated with GCase activity. Further research will be needed to replicate and validate our findings and to uncover the functional connection between acid alpha-glucosidase and GCase.
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Affiliation(s)
- Emma N Somerville
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Lynne Krohn
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | | | - Eric Yu
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Jamil Ahmad
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Farnaz Asayesh
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Jennifer A Ruskey
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Dan Speigelman
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
| | - Stanley Fahn
- Department of Neurology, College of Physicians and Surgeons, Columbia University Medical Center
| | - Cheryl Waters
- Department of Neurology, College of Physicians and Surgeons, Columbia University Medical Center
| | - S Pablo Sardi
- Rare and Neurological Diseases Therapeutic Area, Sanofi
| | - Roy N Alcalay
- Department of Neurology, College of Physicians and Surgeons, Columbia University Medical Center
| | - Ziv Gan-Or
- The Neuro (Montréal Neurological Institute-Hospital), McGill University
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36
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Gao G, McClellan J, Barbeira AN, Fiorica PN, Li JL, Mu Z, Olopade OI, Huo D, Im HK. A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer. Am J Hum Genet 2024; 111:1100-1113. [PMID: 38733992 PMCID: PMC11179262 DOI: 10.1016/j.ajhg.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.
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Affiliation(s)
- Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Julian McClellan
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Peter N Fiorica
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Zepeng Mu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Olufunmilayo I Olopade
- Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
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37
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Weng LC, Khurshid S, Hall AW, Nauffal V, Morrill VN, Sun YV, Rämö JT, Beer D, Lee S, Nadkarni G, Johnson R, Andreasen L, Clayton A, Pullinger CR, Yoneda ZT, Friedman DJ, Hyman MC, Judy RL, Skanes AC, Orland KM, Jordà P, Treu TM, Oetjens MT, Subbiah R, Hartmann JP, May HT, Kane JP, Issa TZ, Nafissi NA, Leong-Sit P, Dubé MP, Roselli C, Choi SH, Tardif JC, Khan HR, Knight S, Svendsen JH, Walker B, Linnér RK, Gaziano JM, Tadros R, Fatkin D, Rader DJ, Shah SH, Roden DM, Marcus GM, Loos RJ, Damrauer SM, Haggerty CM, Cho K, Palotie A, Olesen MS, Eckhardt LL, Roberts JD, Cutler MJ, Shoemaker MB, Wilson PW, Ellinor PT, Lubitz SA. Meta-Analysis of Genome-Wide Association Studies Reveals Genetic Mechanisms of Supraventricular Arrhythmias. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004320. [PMID: 38804128 PMCID: PMC11187659 DOI: 10.1161/circgen.123.004320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 03/31/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS Analyses comprised 2384 AVNRT cases and 106 489 referents, and 2811 AVAP/AVRT cases and 1,483 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A, SCN10A, and TTN/CCDC141. Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.
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Affiliation(s)
- Lu-Chen Weng
- Cardiovascular Rsrch Ctr, Dept of Medicine, Dept of Neurology & Dept of Psychiatry, MGH, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- VA Boston Healthcare System
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Dept of Medicine, Dept of Neurology & Dept of Psychiatry, MGH, Boston
| | - Amelia Weber Hall
- Gene Regulation Observatory, The Broad Institute of MIT & Harvard, Cambridge
| | - Victor Nauffal
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- VA Boston Healthcare System
- Cardiovascular Medicine Division, Brigham and Women’s Hospital, Boston, MA
| | - Valerie N. Morrill
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Yan V. Sun
- Dept of Epidemiology, Emory Univ Rollins School of Public Health, Atlanta
- VA Atlanta Healthcare System, Decatur, GA
| | - Joel T. Rämö
- Inst for Molecular Medicine Finland (FIMM), Helsinki Inst of Life Science (HiLIFE), Univ of Helsinki, Helsinki, Finland
- The Broad Inst of MIT & Harvard, Cambridge, MA
| | | | - Simon Lee
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Renee Johnson
- Victor Chang Cardiac Rsrch Inst, Darlinghurst
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Laura Andreasen
- Laboratory for Molecular Cardiology, Dept of Cardiology, Copenhagen Univ Hospital, Rigshospitalet
- Dept of Biomedical Sciences, Univ of Copenhagen, Copenhagen, Denmark
| | - Anne Clayton
- Intermountain Heart Inst, Intermountain Medical Ctr, Murray, UT
| | - Clive R. Pullinger
- Cardiovascular Rsrch Inst & Dept of Physiological Nursing, Univ of California, San Francisco, CA
| | - Zachary T. Yoneda
- Dept of Medicine, Division of Cardiovascular Medicine, Vanderbilt Univ Medical Ctr, Nashville, TN
| | - Daniel J. Friedman
- Division of Cardiology, Dept of Medicine, Duke Univ School of Medicine, Durham, NC
| | - Matthew C. Hyman
- Division of Cardiac Electrophysiology, Hospital of the Univ of Pennsylvania
| | - Renae L. Judy
- Dept of Surgery, Perelman School of Medicine, Univ of Pennsylvania, Philadelphia, PA
| | - Allan C. Skanes
- Section of Cardiac Electrophysiology, Division of Cardiology, Dept of Medicine, Western Univ, London, ON, Canada
| | - Kate M. Orland
- Dept of Medicine, Division of Cardiovascular Medicine, Univ of Wisconsin–Madison, Madison, WI
| | - Paloma Jordà
- Montreal Heart Inst Rsrch Ctr & Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | | | | | - Rajesh Subbiah
- Victor Chang Cardiac Rsrch Inst, Darlinghurst
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- St Vincent’s Hospital, Darlinghurst
| | - Jacob P. Hartmann
- Laboratory for Molecular Cardiology, Dept of Cardiology, Copenhagen Univ Hospital, Rigshospitalet
| | - Heidi T. May
- Intermountain Heart Inst, Intermountain Medical Ctr, Murray, UT
| | - John P. Kane
- Cardiovascular Rsrch Inst, Univ of California, San Francisco, CA
- Dept of Medicine, Univ of California, San Francisco, CA
- Dept of Biochemistry & Biophysics, Univ of California, San Francisco, CA
| | - Tariq Z. Issa
- Feinberg School of Medicine, Northwestern Univ, Chicago, IL
| | - Navid A. Nafissi
- Division of Cardiology, Dept of Medicine, Duke Univ School of Medicine, Durham, NC
| | - Peter Leong-Sit
- Section of Cardiac Electrophysiology, Division of Cardiology, Dept of Medicine, Western Univ, London, ON, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Inst Rsrch Ctr & Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
- Beaulieu-Saucier Pharmacogenomics Ctr, Montreal, Canada
| | - Carolina Roselli
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Dept of Cardiology, Univ of Groningen, University Medical Ctr Groningen, the Netherlands
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | | | | | | | - Jean-Claude Tardif
- Montreal Heart Inst Rsrch Ctr & Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Habib R. Khan
- Section of Cardiac Electrophysiology, Division of Cardiology, Dept of Medicine, Western Univ, London, ON, Canada
| | - Stacey Knight
- Intermountain Heart Inst, Intermountain Medical Ctr, Murray, UT
- Dept of Medicine, Univ of Utah, Salt Lake City, UT
| | - Jesper H. Svendsen
- Laboratory for Molecular Cardiology, Dept of Cardiology, Copenhagen Univ Hospital, Rigshospitalet
- Dept of Clinical Medicine, Univ of Copenhagen, Copenhagen, Denmark
| | - Bruce Walker
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- St Vincent’s Hospital, Darlinghurst
| | - Richard Karlsson Linnér
- Autism & Developmental Medicine Inst, Geisinger, Lewisburg, PA
- Dept of Economics, Leiden Law School, Leiden Univ, Leiden, the Netherlands
| | - J. Michael Gaziano
- VA Boston Healthcare System
- Cardiovascular Medicine Division, Brigham and Women’s Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Rafik Tadros
- Montreal Heart Inst Rsrch Ctr & Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Diane Fatkin
- Victor Chang Cardiac Rsrch Inst, Darlinghurst
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- St Vincent’s Hospital, Darlinghurst
| | - Daniel J. Rader
- Division of Cardiovascular Medicine, Dept of Medicine, Perelman School of Medicine, Univ of Pennsylvania, Philadelphia, PA
| | - Svati H. Shah
- Division of Cardiology, Dept of Medicine, Duke Univ School of Medicine, Durham, NC
- Duke Molecular Physiology Inst, Duke Univ School of Medicine, Durham, NC
| | | | | | - Ruth J.F. Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY & Novo Nordisk Foundation Ctr for Basic Metabolic Rsrch, Dept of Health & Medical Sciences, Univ of Copenhagen, Copenhagen, Denmark
| | - Scott M. Damrauer
- Dept of Surgery & Dept of Genetics, Perelman School of Medicine, Univ of Pennsylvania, Philadelphia, PA
- Corporal Michael Crescenz VA Medical Ctr, Philadelphia
| | - Christopher M. Haggerty
- Heart Inst, Geisinger, Danville, PA
- Dept of Translational Data Science & Informatics, Geisinger, Danville, PA
| | - Kelly Cho
- VA Boston Healthcare System
- Cardiovascular Medicine Division, Brigham and Women’s Hospital, Boston, MA
| | - Aarno Palotie
- Inst for Molecular Medicine Finland (FIMM), Helsinki Inst of Life Science (HiLIFE), Univ of Helsinki, Helsinki, Finland
- The Stanley Center for Psychiatric Rsrch & Program in Medical & Population Genetics, The Broad Institute of MIT & Harvard, Cambridge
- Analytic & Translational Genetics Unit, Dept of Medicine, Dept of Neurology & Dept of Psychiatry, MGH, Boston
| | - Morten S. Olesen
- Laboratory for Molecular Cardiology, Dept of Cardiology, Copenhagen Univ Hospital, Rigshospitalet
- Dept of Biomedical Sciences, Univ of Copenhagen, Copenhagen, Denmark
| | - Lee L. Eckhardt
- Dept of Medicine, Division of Cardiovascular Medicine, Univ of Wisconsin–Madison, Madison, WI
| | - Jason D. Roberts
- Section of Cardiac Electrophysiology, Division of Cardiology, Dept of Medicine, Western Univ, London, ON, Canada
| | | | - M. Benjamin Shoemaker
- Dept of Medicine, Division of Cardiovascular Medicine, Vanderbilt Univ Medical Ctr, Nashville, TN
| | - Peter W.F. Wilson
- VA Atlanta Healthcare System, Decatur, GA
- Dept of Medicine, Emory Univ School of Medicine, Atlanta, GA
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Dept of Medicine, Dept of Neurology & Dept of Psychiatry, MGH, Boston
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Dept of Medicine, Dept of Neurology & Dept of Psychiatry, MGH, Boston
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38
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Tang T, Yu H, Xu S, Zhong Y, Ma J, Zhao T. Causal effects of endometriosis on cancer risk: A Mendelian randomization study. Int J Cancer 2024; 154:1948-1954. [PMID: 38323658 DOI: 10.1002/ijc.34876] [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: 08/24/2023] [Revised: 11/14/2023] [Accepted: 01/19/2024] [Indexed: 02/08/2024]
Abstract
Endometriosis has been reported in epidemiological studies to be associated with certain types of cancer. However, the presence of reverse causality and residual confounding due to common risk factors introduces uncertainty regarding the extent to which endometriosis itself contributes to the development of cancer. We performed the Mendelian randomization (MR) to investigate the causal associations between endometriosis and 34 different types of cancers. The results of the inverse-variance-weighted (IVW) model suggested that genetic predisposition to endometriosis was causally associated with an increased risk for ovarian cancer (OR = 3.2913; p-value = .0320). The genetic liabilities to endometriosis had causal associations with the decreased risk for skin cancer (OR = 0.9973; p-value = .0219), hematological cancer (OR = 0.9953; p-value = .0175) and ER- breast cancer (OR = 0.6960; p-value = .0381). The causal association of the above combinations were robust by test of heterogeneity and pleiotropy. Together, our study suggests that endometriosis had causal effect on cancer risk.
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Affiliation(s)
- Tianyou Tang
- The College of Pediatrics, Chongqing Medical University, Chongqing, China
| | - Huilin Yu
- The Second Medicine College, Chongqing Medical University, Chongqing, China
| | - Sipei Xu
- The First Medicine College, Chongqing Medical University, Chongqing, China
| | - Yi Zhong
- The College of Pediatrics, Chongqing Medical University, Chongqing, China
| | - Jie Ma
- Department of Pharmacology, Pharmaceutical Engineering College, Chongqing Chemical Industry Vocational College, Chongqing, China
| | - Tingting Zhao
- Laboratory of Human Function Experimental Teaching and Management Center of Chongqing Medical University, Chongqing, China
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39
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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; 56:1110-1120. [PMID: 38811844 DOI: 10.1038/s41588-024-01766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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40
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Patel A, Gill D, Shungin D, Mantzoros CS, Knudsen LB, Bowden J, Burgess S. Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region. Genet Epidemiol 2024; 48:151-163. [PMID: 38379245 PMCID: PMC7616158 DOI: 10.1002/gepi.22551] [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: 06/19/2023] [Revised: 12/08/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024]
Abstract
Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.
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Affiliation(s)
- Ashish Patel
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Dmitry Shungin
- Human Genetics Centre of Excellence, AI and Digital Research, Novo Nordisk, Denmark
| | - Christos S. Mantzoros
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
- Department of Internal Medicine, Boston VA Healthcare System, Harvard Medical School, USA
| | - Lotte Bjerre Knudsen
- Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Denmark
| | - Jack Bowden
- Department of Clinical and Biomedical Sciences, University of Exeter, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, U.K
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, UK
- Cardiovascular Epidemiology Unit, University of Cambridge, UK
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41
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Rahman MS, Harrison E, Biggs H, Seikus C, Elliott P, Breen G, Kingston N, Bradley JR, Hill SM, Tom BDM, Chinnery PF. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med 2024; 30:1739-1748. [PMID: 38745010 PMCID: PMC11186791 DOI: 10.1038/s41591-024-02960-5] [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: 11/21/2023] [Accepted: 03/28/2024] [Indexed: 05/16/2024]
Abstract
A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.
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Affiliation(s)
- Md Shafiqur Rahman
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Emma Harrison
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Heather Biggs
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Chloe Seikus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Nathalie Kingston
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Dept of Haematology, Cambridge University, Cambridge, UK
| | - John R Bradley
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research BioResource, Cambridge, UK.
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK.
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42
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Schormair B, Zhao C, Bell S, Didriksen M, Nawaz MS, Schandra N, Stefani A, Högl B, Dauvilliers Y, Bachmann CG, Kemlink D, Sonka K, Paulus W, Trenkwalder C, Oertel WH, Hornyak M, Teder-Laving M, Metspalu A, Hadjigeorgiou GM, Polo O, Fietze I, Ross OA, Wszolek ZK, Ibrahim A, Bergmann M, Kittke V, Harrer P, Dowsett J, Chenini S, Ostrowski SR, Sørensen E, Erikstrup C, Pedersen OB, Topholm Bruun M, Nielsen KR, Butterworth AS, Soranzo N, Ouwehand WH, Roberts DJ, Danesh J, Burchell B, Furlotte NA, Nandakumar P, Earley CJ, Ondo WG, Xiong L, Desautels A, Perola M, Vodicka P, Dina C, Stoll M, Franke A, Lieb W, Stewart AFR, Shah SH, Gieger C, Peters A, Rye DB, Rouleau GA, Berger K, Stefansson H, Ullum H, Stefansson K, Hinds DA, Di Angelantonio E, Oexle K, Winkelmann J. Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction. Nat Genet 2024; 56:1090-1099. [PMID: 38839884 PMCID: PMC11176086 DOI: 10.1038/s41588-024-01763-1] [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: 03/09/2023] [Accepted: 04/19/2024] [Indexed: 06/07/2024]
Abstract
Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.
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Affiliation(s)
- Barbara Schormair
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Steven Bell
- Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Maria Didriksen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | | | - Nathalie Schandra
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Ambra Stefani
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yves Dauvilliers
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Cornelius G Bachmann
- SomnoDiagnostics, Osnabrück, Germany
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - David Kemlink
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Karel Sonka
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Walter Paulus
- Department of Neurology, Ludwig Maximilians University Munich, Munich, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, Kassel, Germany
- Department of Neurosurgery, University Medical Center Göttingen, Göttingen, Germany
| | - Wolfgang H Oertel
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | | | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Georgios M Hadjigeorgiou
- Department of Neurology, Nicosia General Hospital Medical School, University of Cyprus, Nicosia, Cyprus
| | - Olli Polo
- Bragée ME/CFS Center, Stockholm, Sweden
| | - Ingo Fietze
- Department of Pulmonology, Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA
| | | | - Abubaker Ibrahim
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Melanie Bergmann
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Volker Kittke
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Philip Harrer
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Joseph Dowsett
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sofiene Chenini
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Kaspar R Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Adam S Butterworth
- 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
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Nicole Soranzo
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- Department of Haematology, University College London Hospitals, London, UK
| | - David J Roberts
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Radcliffe Department of Medicine and National Health Service Blood and Transplant, Oxford, UK
- Department of Haematology and BRC Haematology Theme, Churchill Hospital, Headington, Oxford, UK
| | - John Danesh
- 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
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | | | | | | | - Christopher J Earley
- Center for Restless Legs Syndrome, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - William G Ondo
- Department of Neurology, Methodist Neurological Institute, Weill Cornell Medical School, Houston, TX, USA
| | - Lan Xiong
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Alex Desautels
- Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec, Canada
- Department of Neurosciences, Université de Montréal, Montreal, Quebec, Canada
| | - Markus Perola
- Clinical and Molecular Metabolism Research Program (CAMM), Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health and Welfare, National Institute for Health and Welfare, Helsinki, Finland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Academy of Science of Czech Republic, Prague, Czech Republic
- First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Christian Dina
- L'institut du thorax, CNRS, INSERM, Nantes Université, Nantes, France
| | - Monika Stoll
- Department of Genetic Epidemiology, Institute for Human Genetics, University of Münster, Münster, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Wolfgang Lieb
- PopGen Biobank and Institute of Epidemiology, Christian Albrechts University Kiel, Kiel, Germany
| | - Alexandre F R Stewart
- John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Svati H Shah
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - David B Rye
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Guy A Rouleau
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | | | | | | | | | - Emanuele Di Angelantonio
- 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
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Fondazione Human Technopole, Milan, Italy
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich-Augsburg, Munich-Augsburg, Germany
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Xu X, Xu X, Zakeri MA, Wang SY, Yan M, Wang YH, Li L, Sun ZL, Wang RY, Miao LZ. Assessment of causal relationships between omega-3 and omega-6 polyunsaturated fatty acids in autoimmune rheumatic diseases: a brief research report from a Mendelian randomization study. Front Nutr 2024; 11:1356207. [PMID: 38863588 PMCID: PMC11165037 DOI: 10.3389/fnut.2024.1356207] [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: 12/15/2023] [Accepted: 05/08/2024] [Indexed: 06/13/2024] Open
Abstract
Background Currently, the association between the consumption of polyunsaturated fatty acids (PUFAs) and the susceptibility to autoimmune rheumatic diseases (ARDs) remains conflict and lacks substantial evidence in various clinical studies. To address this issue, we employed Mendelian randomization (MR) to establish causal links between six types of PUFAs and their connection to the risk of ARDs. Methods We retrieved summary-level data on six types of PUFAs, and five different types of ARDs from publicly accessible GWAS statistics. Causal relationships were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the reliability of our research findings, we used four complementary approaches and conducted multivariable MR analysis (MVMR). Additionally, we investigated reverse causality through a reverse MR analysis. Results Our results indicate that a heightened genetic predisposition for elevated levels of EPA (ORIVW: 0.924, 95% CI: 0.666-1.283, P IVW = 0.025) was linked to a decreased susceptibility to psoriatic arthritis (PsA). Importantly, the genetically predicted higher levels of EPA remain significantly associated with an reduced risk of PsA, even after adjusting for multiple testing using the FDR method (P IVW-FDR-corrected = 0.033) and multivariable MR analysis (P MV-IVW < 0.05), indicating that EPA may be considered as the risk-protecting PUFAs for PsA. Additionally, high levels of LA showed a positive causal relationship with a higher risk of PsA (ORIVW: 1.248, 95% CI: 1.013-1.538, P IVW = 0.037). It is interesting to note, however, that the effects of these associations were weakened in our MVMR analyses, which incorporated adjustment for lipid profiles (P MV-IVW > 0.05) and multiple testing using the FDR method (P IVW-FDR-corrected = 0.062). Moreover, effects of total omega-3 PUFAs, DHA, EPA, and LA on PsA, were massively driven by SNP effects in the FADS gene region. Furthermore, no causal association was identified between the concentrations of other circulating PUFAs and the risk of other ARDs. Further analysis revealed no significant horizontal pleiotropy and heterogeneity or reverse causality. Conclusion Our comprehensive MR analysis indicated that EPA is a key omega-3 PUFA that may protect against PsA but not other ARDs. The FADS2 gene appears to play a central role in mediating the effects of omega-3 PUFAs on PsA risk. These findings suggest that EPA supplementation may be a promising strategy for preventing PsA onset. Further well-powered epidemiological studies and clinical trials are warranted to explore the potential mechanisms underlying the protective effects of EPA in PsA.
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Affiliation(s)
- Xiao Xu
- School of Nursing, Nantong Health College of Jiangsu Province, Nantong, China
| | - Xu Xu
- Department of Geriatrics, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mohammad Ali Zakeri
- Non-Communicable Diseases Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Shu-Yun Wang
- Department of Postgraduate, St. Paul University Philippines, Tuggegarau, Philippines
| | - Min Yan
- Department of Epidemiology, School of Public Health, Changzhou University, Changzhou, China
- Faculty of Health and Welfare, Satakunta University of Applied Sciences, Pori, Finland
| | - Yuan-Hong Wang
- Department of Rheumatology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Li Li
- Department of Rheumatology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhi-ling Sun
- Department of Epidemiology, School of Public Health, Nanjing University of Chinese Medicine, Nanjing, China
| | - Rong-Yun Wang
- Department of Rheumatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lin-Zhong Miao
- Department of Nursing, Children’s Hospital of Soochow University, Soochow University, Suzhou, China
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Hernandez-Cordero A, Thomas L, Smail A, Lim ZQ, Saklatvala JR, Chung R, Curtis CJ, Baum P, Visvanathan S, Burden AD, Cooper HL, Dunnill G, Griffiths CEM, Levell NJ, Parslew R, Reynolds NJ, Wahie S, Warren RB, Wright A, Simpson M, Hveem K, Barker JN, Dand N, Løset M, Smith CH, Capon F. A genome-wide meta-analysis of palmoplantar pustulosis implicates T H2 responses and cigarette smoking in disease pathogenesis. J Allergy Clin Immunol 2024:S0091-6749(24)00553-0. [PMID: 38815935 DOI: 10.1016/j.jaci.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/22/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Palmoplantar pustulosis (PPP) is an inflammatory skin disorder that mostly affects smokers and manifests with painful pustular eruptions on the palms and soles. Although the disease can present with concurrent plaque psoriasis, TNF and IL-17/IL-23 inhibitors show limited efficacy. There is therefore a pressing need to uncover PPP disease drivers and therapeutic targets. OBJECTIVES We sought to identify genetic determinants of PPP and investigate whether cigarette smoking contributes to disease pathogenesis. METHODS We performed a genome-wide association meta-analysis of 3 North-European cohorts (n = 1,456 PPP cases and 402,050 controls). We then used the scGWAS program to investigate the cell-type specificity of the association signals. We also undertook genetic correlation analyses to examine the similarities between PPP and other immune-mediated diseases. Finally, we applied Mendelian randomization to analyze the causal relationship between cigarette smoking and PPP. RESULTS We found that PPP is not associated with the main genetic determinants of plaque psoriasis. Conversely, we identified genome-wide significant associations with the FCGR3A/FCGR3B and CCHCR1 loci. We also observed 13 suggestive (P < 5 × 10-6) susceptibility regions, including the IL4/IL13 interval. Accordingly, we demonstrated a significant genetic correlation between PPP and TH2-mediated diseases such as atopic dermatitis and ulcerative colitis. We also found that genes mapping to PPP-associated intervals were preferentially expressed in dendritic cells and often implicated in T-cell activation pathways. Finally, we undertook a Mendelian randomization analysis, which supported a causal role of cigarette smoking in PPP. CONCLUSIONS The first genome-wide association study of PPP points to a pathogenic role for deregulated TH2 responses and cigarette smoking.
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Affiliation(s)
- Ariana Hernandez-Cordero
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Laurent Thomas
- Department of Clinical and Molecular Medicine, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, 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. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Alice Smail
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Zhao Qin Lim
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom; Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Jake R Saklatvala
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Raymond Chung
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Charles J Curtis
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Patrick Baum
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | | | - A David Burden
- School of Infection and Immunity, University of Glasgow, Glasgow, United Kingdom
| | - Hywel L Cooper
- Portsmouth Dermatology Unit, Portsmouth Hospitals Trust, Portsmouth, United Kingdom
| | | | - Christopher E M Griffiths
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; Department of Dermatology, King's College Hospital, King's College London, London, United Kingdom
| | - Nick J Levell
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Richard Parslew
- Department of Dermatology, Royal Liverpool Hospitals, Liverpool, United Kingdom
| | - Nick J Reynolds
- Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle NIHR Biomedical Research Centre and the Department of Dermatology, Royal Victoria Infirmary, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Shyamal Wahie
- University Hospital of North Durham, Durham, United Kingdom; Darlington Memorial Hospital, Darlington, United Kingdom
| | - Richard B Warren
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; Dermatology Centre, Northern Care Alliance NHS Foundation Trust, Manchester, United Kingdom
| | - Andrew Wright
- St Lukes Hospital, Bradford, United Kingdom; Centre for Skin Science, University of Bradford, Bradford, United Kingdom
| | - Michael Simpson
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Innovation and Research, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonathan N Barker
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Nick Dand
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Mari Løset
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Dermatology, Clinic of Orthopedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Catherine H Smith
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Francesca Capon
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom.
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Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, Kemper KE, Zeng J, Zheng Z, Zhu Z, Hannon E, Vellame DS, Franklin A, Caggiano C, Wamsley B, Geschwind DH, Zaitlen N, Gusev A, Pasaniuc B, Mill J, Luo C, Gandal MJ. Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. SCIENCE ADVANCES 2024; 10:eadn7655. [PMID: 38781333 PMCID: PMC11114225 DOI: 10.1126/sciadv.adn7655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.
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Affiliation(s)
- Chloe X. Yap
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel D. Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cindy Wen
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuanhao Yang
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Dorothea Seiler Vellame
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Brie Wamsley
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Kraft J, Braun A, Awasthi S, Panagiotaropoulou G, Schipper M, Bell N, Posthuma D, Pardiñas AF, Ripke S, Heilbron K. Identifying drug targets for schizophrenia through gene prioritization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.15.24307423. [PMID: 38798390 PMCID: PMC11118622 DOI: 10.1101/2024.05.15.24307423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. Methods To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool. Results We prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition to DRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2A and GRM3), calcium channels (CACNA1C and CACNB2), and GABAB receptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4B and VRK2). Conclusions We curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.
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Affiliation(s)
- Julia Kraft
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Alice Braun
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Swapnil Awasthi
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Georgia Panagiotaropoulou
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | | | - Nathaniel Bell
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Antonio F. Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | | | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Karl Heilbron
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
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Brandenburg JT, Chen WC, Boua PR, Govender MA, Agongo G, Micklesfield LK, Sorgho H, Tollman S, Asiki G, Mashinya F, Hazelhurst S, Morris AP, Fabian J, Ramsay M. Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry. Front Genet 2024; 15:1372042. [PMID: 38812969 PMCID: PMC11134365 DOI: 10.3389/fgene.2024.1372042] [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: 01/18/2024] [Accepted: 04/12/2024] [Indexed: 05/31/2024] Open
Abstract
Background Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. Methods Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. Results Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. Conclusion This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.
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Affiliation(s)
- Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Wenlong Carl Chen
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
| | - Palwende Romuald Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | | | - Godfred Agongo
- Navrongo Health Research Centre, Navrongo, Ghana
- Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Lisa K. Micklesfield
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | - Stephen Tollman
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
- Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden
| | - Felistas Mashinya
- Department of Pathology and Medical Sciences, School of Healthcare Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, United Kingdom
| | - June Fabian
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Ojewunmi OO, Adeyemo TA, Oyetunji AI, Inyang B, Akinrindoye A, Mkumbe BS, Gardner K, Rooks H, Brewin J, Patel H, Lee SH, Chung R, Rashkin S, Kang G, Chianumba R, Sangeda R, Mwita L, Isa H, Agumadu UN, Ekong R, Faruk JA, Jamoh BY, Adebiyi NM, Umar IA, Hassan A, Grace C, Goel A, Inusa BPD, Falchi M, Nkya S, Makani J, Ahmad HR, Nnodu O, Strouboulis J, Menzel S. The genetic dissection of fetal haemoglobin persistence in sickle cell disease in Nigeria. Hum Mol Genet 2024; 33:919-929. [PMID: 38339995 PMCID: PMC11070134 DOI: 10.1093/hmg/ddae014] [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: 05/20/2023] [Revised: 12/20/2023] [Accepted: 01/08/2024] [Indexed: 02/12/2024] Open
Abstract
The clinical severity of sickle cell disease (SCD) is strongly influenced by the level of fetal haemoglobin (HbF) persistent in each patient. Three major HbF loci (BCL11A, HBS1L-MYB, and Xmn1-HBG2) have been reported, but a considerable hidden heritability remains. We conducted a genome-wide association study for HbF levels in 1006 Nigerian patients with SCD (HbSS/HbSβ0), followed by a replication and meta-analysis exercise in four independent SCD cohorts (3,582 patients). To dissect association signals at the major loci, we performed stepwise conditional and haplotype association analyses and included public functional annotation datasets. Association signals were detected for BCL11A (lead SNP rs6706648, β = -0.39, P = 4.96 × 10-34) and HBS1L-MYB (lead SNP rs61028892, β = 0.73, P = 1.18 × 10-9), whereas the variant allele for Xmn1-HBG2 was found to be very rare. In addition, we detected three putative new trait-associated regions. Genetically, dissecting the two major loci BCL11A and HBS1L-MYB, we defined trait-increasing haplotypes (P < 0.0001) containing so far unidentified causal variants. At BCL11A, in addition to a haplotype harbouring the putative functional variant rs1427407-'T', we identified a second haplotype, tagged by the rs7565301-'A' allele, where a yet-to-be-discovered causal DNA variant may reside. Similarly, at HBS1L-MYB, one HbF-increasing haplotype contains the likely functional small indel rs66650371, and a second tagged by rs61028892-'C' is likely to harbour a presently unknown functional allele. Together, variants at BCL11A and HBS1L-MYB SNPs explained 24.1% of the trait variance. Our findings provide a path for further investigation of the causes of variable fetal haemoglobin persistence in sickle cell disease.
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Affiliation(s)
- Oyesola O Ojewunmi
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom
| | - Titilope A Adeyemo
- Department of Haematology and Blood Transfusion, College of Medicine, University of Lagos, P.M.B 12003, Lagos, Nigeria
| | - Ajoke I Oyetunji
- Sickle Cell Foundation Nigeria, Ishaga Road, Idi-Araba, P.O. Box 3463, Lagos, Nigeria
| | - Bassey Inyang
- Department of Medical Biochemistry, College of Health Sciences, University of Abuja, Mohammed Maccido Road, Airport Road, P.M.B 117, Abuja, Nigeria
| | - Afolashade Akinrindoye
- Sickle Cell Foundation Nigeria, Ishaga Road, Idi-Araba, P.O. Box 3463, Lagos, Nigeria
- School of Science, University of Greenwich, Central Avenue, Chatham Maritime, Kent ME4 4TB, United Kingdom
| | - Baraka S Mkumbe
- Department of Biochemistry and Molecular Biology, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, United Nations Rd, Dar es Salaam, Tanzania
- Department of Artificial Intelligence and Innovative Medicine, Tohoku University Graduate School of Medicine, 980-8573, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Kate Gardner
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
- Clinical Haematology, Haematology and Oncology Directorate, Guy’s Hospital, Great Maze Pond, London SE1 9RT, United Kingdom
| | - Helen Rooks
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
| | - John Brewin
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
- Department of Haematological Medicine, King's College Hospital, London SE5 9RS, United Kingdom
| | - Hamel Patel
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, 16 De Crespigny Park, London SE5 8AB, United Kingdom
| | - Sang Hyuck Lee
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, 16 De Crespigny Park, London SE5 8AB, United Kingdom
| | - Raymond Chung
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, 16 De Crespigny Park, London SE5 8AB, United Kingdom
| | - Sara Rashkin
- St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, United States
| | - Guolian Kang
- St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, United States
| | - Reuben Chianumba
- Centre of Excellence for Sickle Cell Disease Research and Training (CESRTA), University of Abuja, Mohammed Maccido Road, Airport Road, P.M.B 117, Abuja, Nigeria
| | - Raphael Sangeda
- Department of Pharmaceutical Microbiology, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, Dar es Salaam, Tanzania
| | - Liberata Mwita
- Department of Pharmaceutical Microbiology, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, Dar es Salaam, Tanzania
| | - Hezekiah Isa
- Centre of Excellence for Sickle Cell Disease Research and Training (CESRTA), University of Abuja, Mohammed Maccido Road, Airport Road, P.M.B 117, Abuja, Nigeria
- Department of Haematology and Blood Transfusion, University of Abuja Teaching Hospital, Gwagwalada, P.M.B. 228, Gwagwalada, FCT Abuja, Nigeria
| | - Uche-Nnebe Agumadu
- Department of Paediatrics, College of Health Sciences, University of Abuja, Mohammed Maccido Road, Airport Road, P.M.B 117, Abuja, Nigeria
| | - Rosemary Ekong
- Research Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Jamilu A Faruk
- Department of Paediatrics, Ahmadu Bello University/Ahmadu Bello University Teaching Hospital, P.M.B 006, Zaria, Nigeria
| | - Bello Y Jamoh
- Department of Internal Medicine, Ahmadu Bello University/Ahmadu Bello University Teaching Hospital, P.M.B 006, Zaria, Nigeria
| | - Niyi M Adebiyi
- Department of Paediatrics, Ahmadu Bello University/Ahmadu Bello University Teaching Hospital, P.M.B 006, Zaria, Nigeria
| | - Ismail A Umar
- Department of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Sokoto Road, Samaru, P.M.B 006, Zaria, Nigeria
| | - Abdulaziz Hassan
- Department of Haematology and Blood Transfusion, Ahmadu Bello University, Sokoto Road, Samaru, P.M.B 006, Zaria, Nigeria
| | - Christopher Grace
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Centre for Human Genetics, Roosevelt Drive, Oxford OX37BN, United Kingdom
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Centre for Human Genetics, Roosevelt Drive, Oxford OX37BN, United Kingdom
| | - Baba P D Inusa
- Evelina London Children’s Hospital, Guy’s and St. Thomas’ NHS Foundation Trust, Westminster Bridge Rd, London SE1 7EH, United Kingdom
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, United Kingdom
| | - Siana Nkya
- Department of Biochemistry and Molecular Biology, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, United Nations Rd, Dar es Salaam, Tanzania
- Tanzania Human Genetics Organisation, Sickle Cell Centre, 1 Kipalapala Street, Dar es Salaam, Tanzania
- Sickle Cell Program, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, United Nations Rd, Dar es Salaam, Tanzania
- Department of Haematology and Blood Transfusion, Muhimbili University of Health and Allied Science, P.O. Box 65001, Dar es Salaam, Tanzania
| | - Julie Makani
- Sickle Cell Program, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, United Nations Rd, Dar es Salaam, Tanzania
- Department of Haematology and Blood Transfusion, Muhimbili University of Health and Allied Science, P.O. Box 65001, Dar es Salaam, Tanzania
- Centre for Haematology, Department of Immunology & Inflammation, Imperial College London, Commonwealth Building, Hammersmith Campus, Du Cane Rd, London W12 0NN, United Kingdom
| | - Hafsat R Ahmad
- Department of Paediatrics, Ahmadu Bello University/Ahmadu Bello University Teaching Hospital, P.M.B 006, Zaria, Nigeria
| | - Obiageli Nnodu
- Centre of Excellence for Sickle Cell Disease Research and Training (CESRTA), University of Abuja, Mohammed Maccido Road, Airport Road, P.M.B 117, Abuja, Nigeria
- Department of Haematology and Blood Transfusion, University of Abuja Teaching Hospital, Gwagwalada, P.M.B. 228, Gwagwalada, FCT Abuja, Nigeria
| | - John Strouboulis
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
| | - Stephan Menzel
- School of Cancer and Pharmaceutical Sciences, King’s College London, 123 Coldharbour Lane, London SE5 9NU, United Kingdom
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Ping J, Jia G, Cai Q, Guo X, Tao R, Ambrosone C, Huo D, Ambs S, Barnard ME, Chen Y, Garcia-Closas M, Gu J, Hu JJ, John EM, Li CI, Nathanson K, Nemesure B, Olopade OI, Pal T, Press MF, Sanderson M, Sandler DP, Yoshimatsu T, Adejumo PO, Ahearn T, Brewster AM, Hennis AJM, Makumbi T, Ndom P, O'Brien KM, Olshan AF, Oluwasanu MM, Reid S, Yao S, Butler EN, Huang M, Ntekim A, Li B, Troester MA, Palmer JR, Haiman CA, Long J, Zheng W. Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes. Nat Commun 2024; 15:3718. [PMID: 38697998 PMCID: PMC11065893 DOI: 10.1038/s41467-024-47650-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 04/08/2024] [Indexed: 05/05/2024] Open
Abstract
African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P < 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations.
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Affiliation(s)
- Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christine Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center of Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Yu Chen
- Division of Epidemiology, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Jian Gu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, University of Miami School of Medicine, Miami, FL, USA
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Katherine Nathanson
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Tuya Pal
- Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael F Press
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Toshio Yoshimatsu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Prisca O Adejumo
- Department of Nursing, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Abenaa M Brewster
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anselm J M Hennis
- George Alleyne Chronic Disease Research Centre, University of the West Indies, Bridgetown, Barbados
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | | | - Paul Ndom
- Yaounde General Hospital, Yaounde, Cameroon
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Andrew F Olshan
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mojisola M Oluwasanu
- Department of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Sonya Reid
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Ebonee N Butler
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Maosheng Huang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Atara Ntekim
- Department of Radiation Oncology, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Melissa A Troester
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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Zhang W, Lu T, Sladek R, Li Y, Najafabadi H, Dupuis J. SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals. Bioinformatics 2024; 40:btae295. [PMID: 38688586 PMCID: PMC11105950 DOI: 10.1093/bioinformatics/btae295] [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: 07/24/2023] [Revised: 04/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. RESULTS We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. AVAILABILITY AND IMPLEMENTATION SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.
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Affiliation(s)
- Wenmin Zhang
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Montreal Heart Institute, Université de Montréal, Montreal, Quebec H1T 1C8, Canada
| | - Tianyuan Lu
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Robert Sladek
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada
- Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec H3A 0G1, Canada
| | - Yue Li
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada
| | - Hamed Najafabadi
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada
- Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec H3A 0G1, Canada
| | - Josée Dupuis
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, McGill College, QC H3A 1Y7, Canada
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