1
|
Benjamin KJM, Arora R, Feltrin AS, Pertea G, Giles HH, Stolz JM, D'Ignazio L, Collado-Torres L, Shin JH, Ulrich WS, Hyde TM, Kleinman JE, Weinberger DR, Paquola ACM, Erwin JA. Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus. Nat Commun 2024; 15:3980. [PMID: 38730231 PMCID: PMC11087501 DOI: 10.1038/s41467-024-48048-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.
Collapse
Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University Krieger School of Arts & Sciences, Baltimore, MD, USA
| | | | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Hunter H Giles
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Laura D'Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
2
|
Eulalio T, Sun MW, Gevaert O, Greicius MD, Montine TJ, Nachun D, Montgomery SB. regionalpcs: improved discovery of DNA methylation associations with complex traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.590171. [PMID: 38746367 PMCID: PMC11092597 DOI: 10.1101/2024.05.01.590171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.
Collapse
Affiliation(s)
- Tiffany Eulalio
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Michael D Greicius
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
| | | |
Collapse
|
3
|
Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
Collapse
Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| |
Collapse
|
4
|
Advani J, Mehta PA, Hamel AR, Mehrotra S, Kiel C, Strunz T, Corso-Díaz X, Kwicklis M, van Asten F, Ratnapriya R, Chew EY, Hernandez DG, Montezuma SR, Ferrington DA, Weber BHF, Segrè AV, Swaroop A. QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. Nat Commun 2024; 15:1972. [PMID: 38438351 PMCID: PMC10912779 DOI: 10.1038/s41467-024-46063-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
DNA methylation provides a crucial epigenetic mark linking genetic variations to environmental influence. We have analyzed array-based DNA methylation profiles of 160 human retinas with co-measured RNA-seq and >8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 methylation quantitative trait loci and 12,505 expression quantitative trait loci) and 13,747 DNA methylation loci affecting gene expression, with over one-third specific to the retina. Methylation and expression quantitative trait loci show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration. Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of macular degeneration pathology by genotype-environment interaction in retina.
Collapse
Affiliation(s)
- Jayshree Advani
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Puja A Mehta
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew R Hamel
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sudeep Mehrotra
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Tobias Strunz
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Ximena Corso-Díaz
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Madeline Kwicklis
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Freekje van Asten
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rinki Ratnapriya
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, Clinical Trials Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute of Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sandra R Montezuma
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA
| | - Deborah A Ferrington
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA
- Doheny Eye Institute, Pasadena, CA, USA
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Ayellet V Segrè
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Anand Swaroop
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
5
|
Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
Collapse
Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
6
|
Wang J, Cheng X, Liang Q, Owen LA, Lu J, Zheng Y, Wang M, Chen S, DeAngelis MM, Li Y, Chen R. Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation. Genome Biol 2023; 24:269. [PMID: 38012720 PMCID: PMC10680294 DOI: 10.1186/s13059-023-03111-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Systematic characterization of how genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expression and chromatin accessibility in cells from healthy retinae of 20 human donors through single-cell multiomics and genomic sequencing. RESULTS We map eQTL, caQTL, allelic-specific expression, and allelic-specific chromatin accessibility in major retinal cell types. By integrating these results, we identify and characterize regulatory elements and genetic variants effective on gene regulation in individual cell types. The majority of identified sc-eQTLs and sc-caQTLs display cell type-specific effects, while the cis-elements containing genetic variants with cell type-specific effects are often accessible in multiple cell types. Furthermore, the transcription factors whose binding sites are perturbed by genetic variants tend to have higher expression levels in the cell types where the variants exert their effects, compared to the cell types where the variants have no impact. We further validate our findings with high-throughput reporter assays. Lastly, we identify the enriched cell types, candidate causal variants and genes, and cell type-specific regulatory mechanism underlying GWAS loci. CONCLUSIONS Overall, genetic effects on gene regulation are highly context dependent. Our results suggest that cell type-dependent genetic effect is driven by precise modulation of both trans-factor expression and chromatin accessibility of cis-elements. Our findings indicate hierarchical collaboration among transcription factors plays a crucial role in mediating cell type-specific effects of genetic variants on gene regulation.
Collapse
Affiliation(s)
- Jun Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Qingnan Liang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Leah A Owen
- Department of Ophthalmology and Visual Sciences, John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
| | - Jiaxiong Lu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Yiqiao Zheng
- Department of Ophthalmology and Visual Sciences, Washington University in St Louis, Saint Louis, MO, USA
| | - Meng Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Shiming Chen
- Department of Ophthalmology and Visual Sciences, Washington University in St Louis, Saint Louis, MO, USA
- Department of Developmental Biology, Washington University in St Louis, Saint Louis, MO, USA
| | - Margaret M DeAngelis
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA
| | - Yumei Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
7
|
Urbut SM, Koyama S, Hornsby W, Bhukar R, Kheterpal S, Truong B, Selvaraj MS, Neale B, O’Donnell CJ, Peloso GM, Natarajan P. Bayesian multivariate genetic analysis improves translational insights. iScience 2023; 26:107854. [PMID: 37766997 PMCID: PMC10520309 DOI: 10.1016/j.isci.2023.107854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/15/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.
Collapse
Affiliation(s)
- Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Whitney Hornsby
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Rohan Bhukar
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Sumeet Kheterpal
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Buu Truong
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Margaret S. Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- Analytic Translational and Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher J. O’Donnell
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- VA Boston Department of Veterans Affairs, Boston, MA 02130, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
8
|
Zhang S, Zhang H, Forrest MP, Zhou Y, Sun X, Bagchi VA, Kozlova A, Santos MD, Piguel NH, Dionisio LE, Sanders AR, Pang ZP, He X, Penzes P, Duan J. Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons. CELL GENOMICS 2023; 3:100399. [PMID: 37719141 PMCID: PMC10504676 DOI: 10.1016/j.xgen.2023.100399] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/22/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023]
Abstract
The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.
Collapse
Affiliation(s)
- Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc P. Forrest
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Yifan Zhou
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Xiaotong Sun
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Vikram A. Bagchi
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Marc Dos Santos
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Nicolas H. Piguel
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Leonardo E. Dionisio
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
| | - Alan R. Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Zhiping P. Pang
- Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Peter Penzes
- Department of Neuroscience, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Northwestern University, Chicago, IL 60611, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
9
|
Selewa A, Luo K, Wasney M, Smith L, Sun X, Tang C, Eckart H, Moskowitz IP, Basu A, He X, Pott S. Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation. Nat Commun 2023; 14:4999. [PMID: 37591828 PMCID: PMC10435551 DOI: 10.1038/s41467-023-40505-5] [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: 02/11/2022] [Accepted: 08/01/2023] [Indexed: 08/19/2023] Open
Abstract
Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
Collapse
Affiliation(s)
- Alan Selewa
- Biophysical Sciences Graduate Program, The University of Chicago, Chicago, IL, 60637, USA
| | - Kaixuan Luo
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA
| | - Michael Wasney
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Linsin Smith
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, 60637, USA
| | - Xiaotong Sun
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA
| | - Chenwei Tang
- The College, The University of Chicago, Chicago, IL, 60637, USA
| | - Heather Eckart
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Ivan P Moskowitz
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60637, USA
| | - Anindita Basu
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA.
| | - Xin He
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA.
| | - Sebastian Pott
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA.
| |
Collapse
|
10
|
Cabana-Domínguez J, Llonga N, Arribas L, Alemany S, Vilar-Ribó L, Demontis D, Fadeuilhe C, Corrales M, Richarte V, Børglum AD, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 2023; 28:3493-3502. [PMID: 37537283 PMCID: PMC10618083 DOI: 10.1038/s41380-023-02200-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.
Collapse
Affiliation(s)
- Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Fadeuilhe
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montse Corrales
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vanesa Richarte
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anders D Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| |
Collapse
|
11
|
Little P, Liu S, Zhabotynsky V, Li Y, Lin DY, Sun W. A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data. Nat Commun 2023; 14:3030. [PMID: 37231002 PMCID: PMC10212972 DOI: 10.1038/s41467-023-38795-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.
Collapse
Affiliation(s)
- Paul Little
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Si Liu
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Vasyl Zhabotynsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wei Sun
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| |
Collapse
|
12
|
Ursini G, Di Carlo P, Mukherjee S, Chen Q, Han S, Kim J, Deyssenroth M, Marsit CJ, Chen J, Hao K, Punzi G, Weinberger DR. Prioritization of potential causative genes for schizophrenia in placenta. Nat Commun 2023; 14:2613. [PMID: 37188697 PMCID: PMC10185564 DOI: 10.1038/s41467-023-38140-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone.
Collapse
Affiliation(s)
- Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Pasquale Di Carlo
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Sreya Mukherjee
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Jiyoung Kim
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Maya Deyssenroth
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Carmen J Marsit
- Departments of Environmental Health and Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jia Chen
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Giovanna Punzi
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
13
|
Shang L, Zhao W, Wang YZ, Li Z, Choi JJ, Kho M, Mosley TH, Kardia SLR, Smith JA, Zhou X. meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans. Nat Commun 2023; 14:2711. [PMID: 37169753 PMCID: PMC10175543 DOI: 10.1038/s41467-023-37961-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 04/07/2023] [Indexed: 05/13/2023] Open
Abstract
Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.
Collapse
Affiliation(s)
- Lulu Shang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yi Zhe Wang
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Zheng Li
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jerome J Choi
- Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53726, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, 39126, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
14
|
Cai W, Zhang Y, Chang T, Wang Z, Zhu B, Chen Y, Gao X, Xu L, Zhang L, Gao H, Song J, Li J. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle. J Anim Sci Biotechnol 2023; 14:78. [PMID: 37165455 PMCID: PMC10173583 DOI: 10.1186/s40104-023-00876-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. RESULTS We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. CONCLUSIONS The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.
Collapse
Affiliation(s)
- Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yapeng Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Tianpeng Chang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zezhao Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Bo Zhu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yan Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiuzhou Song
- Department of Animal and Avian Science, University of Maryland, College Park, MD, 20742, USA.
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| |
Collapse
|
15
|
Han SK, McNulty MT, Benway CJ, Wen P, Greenberg A, Onuchic-Whitford AC, Jang D, Flannick J, Burtt NP, Wilson PC, Humphreys BD, Wen X, Han Z, Lee D, Sampson MG. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat Commun 2023; 14:2229. [PMID: 37076491 PMCID: PMC10115815 DOI: 10.1038/s41467-023-37691-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 03/27/2023] [Indexed: 04/21/2023] Open
Abstract
Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.
Collapse
Affiliation(s)
- Seong Kyu Han
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Michelle T McNulty
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Christopher J Benway
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Pei Wen
- Center for Precision Disease Modeling, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Anya Greenberg
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Ana C Onuchic-Whitford
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jason Flannick
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Noël P Burtt
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Parker C Wilson
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xiaoquan Wen
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhe Han
- Center for Precision Disease Modeling, University of Maryland, School of Medicine, Baltimore, MD, USA.
| | - Dongwon Lee
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA.
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA, USA.
| | - Matthew G Sampson
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Kidney Disease Initiative, Broad Institute, Cambridge, MA, USA.
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| |
Collapse
|
16
|
Oliva M, Demanelis K, Lu Y, Chernoff M, Jasmine F, Ahsan H, Kibriya MG, Chen LS, Pierce BL. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 2023; 55:112-122. [PMID: 36510025 PMCID: PMC10249665 DOI: 10.1038/s41588-022-01248-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/26/2022] [Indexed: 12/14/2022]
Abstract
Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.
Collapse
Affiliation(s)
- Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Kathryn Demanelis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yihao Lu
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Meytal Chernoff
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Farzana Jasmine
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA
- Department of Medicine, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
| |
Collapse
|
17
|
Benjamin KJM, Chen Q, Jaffe AE, Stolz JM, Collado-Torres L, Huuki-Myers LA, Burke EE, Arora R, Feltrin AS, Barbosa AR, Radulescu E, Pergola G, Shin JH, Ulrich WS, Deep-Soboslay A, Tao R, Hyde TM, Kleinman JE, Erwin JA, Weinberger DR, Paquola ACM. Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes. Nat Neurosci 2022; 25:1559-1568. [PMID: 36319771 PMCID: PMC10599288 DOI: 10.1038/s41593-022-01182-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 09/13/2022] [Indexed: 11/06/2022]
Abstract
Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum's dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.
Collapse
Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Emily E Burke
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Arthur S Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Mathematics, Computation and Cognition, Federal University of ABC, Santo André, Brazil
| | - André Rocha Barbosa
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Inter-Institutional Graduate Program on Bioinformatics, University of São Paulo, São Paulo, Brazil
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | | | - Giulio Pergola
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Ran Tao
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer A Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
18
|
Pérez-Granado J, Piñero J, Furlong LI. Benchmarking post-GWAS analysis tools in major depression: Challenges and implications. Front Genet 2022; 13:1006903. [PMID: 36276939 PMCID: PMC9579284 DOI: 10.3389/fgene.2022.1006903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/20/2022] [Indexed: 12/05/2022] Open
Abstract
Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.
Collapse
Affiliation(s)
- Judith Pérez-Granado
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
- *Correspondence: Laura I. Furlong,
| |
Collapse
|
19
|
Hukku A, Sampson MG, Luca F, Pique-Regi R, Wen X. Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility. Am J Hum Genet 2022; 109:825-837. [PMID: 35523146 PMCID: PMC9118134 DOI: 10.1016/j.ajhg.2022.04.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/04/2022] [Indexed: 11/29/2022] Open
Abstract
Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.
Collapse
Affiliation(s)
- Abhay Hukku
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Matthew G Sampson
- Division of Nephrology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
20
|
Zhang J, Dutta D, Köttgen A, Tin A, Schlosser P, Grams ME, Harvey B, Yu B, Boerwinkle E, Coresh J, Chatterjee N. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat Genet 2022; 54:593-602. [PMID: 35501419 PMCID: PMC9236177 DOI: 10.1038/s41588-022-01051-w] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 03/10/2022] [Indexed: 01/02/2023]
Abstract
Improved understanding of genetic regulation of the proteome can facilitate identification of the causal mechanisms for complex traits. We analyzed data on 4,657 plasma proteins from 7,213 European American (EA) and 1,871 African American (AA) individuals from the Atherosclerosis Risk in Communities study, and further replicated findings on 467 AA individuals from the African American Study of Kidney Disease and Hypertension study. Here, we identified 2,004 proteins in EA and 1,618 in AA, with most overlapping, which showed associations with common variants in cis-regions. Availability of AA samples led to smaller credible sets and notable number of population-specific cis-protein quantitative trait loci. Elastic Net produced powerful models for protein prediction in both populations. An application of proteome-wide association studies to serum urate and gout implicated several proteins, including IL1RN, revealing the promise of the drug anakinra to treat acute gout flares. Our study demonstrates the value of large and diverse ancestry study to investigate the genetic mechanisms of molecular phenotypes and their relationship with complex traits.
Collapse
Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Diptavo Dutta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Harvey
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Bing Yu
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Josef Coresh
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
21
|
Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Fernandes Silva L, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, Boehnke M. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun 2022; 13:1644. [PMID: 35347128 PMCID: PMC8960770 DOI: 10.1038/s41467-022-29143-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/23/2022] [Indexed: 01/13/2023] Open
Abstract
Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.
Collapse
Affiliation(s)
- Xianyong Yin
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lap Sum Chan
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Debraj Bose
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Anne U. Jackson
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Peter VandeHaar
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Adam E. Locke
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Christian Fuchsberger
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.511439.bInstitute for Biomedicine, Eurac Research, Bolzano, 39100 Italy
| | - Heather M. Stringham
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ryan Welch
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ketian Yu
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lilian Fernandes Silva
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland
| | - Susan K. Service
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Daiwei Zhang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
| | - Emily C. Hector
- grid.40803.3f0000 0001 2173 6074Department of Statistics, North Carolina State University, Raleigh, NC 27695 USA
| | - Erica Young
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Liron Ganel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Indraniel Das
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Haley Abel
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Michael R. Erdos
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Lori L. Bonnycastle
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Johanna Kuusisto
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland ,grid.410705.70000 0004 0628 207XCenter for Medicine and Clinical Research, Kuopio University Hospital, Kuopio, 70210 Finland
| | - Nathan O. Stitziel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Ira M. Hall
- grid.47100.320000000419368710Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510 USA
| | - Gregory R. Wagner
- grid.429438.00000 0004 0402 1933Metabolon, Inc., Morrisville, NC 27560 USA
| | | | - Jian Kang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Jean Morrison
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Charles F. Burant
- grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109 USA
| | - Francis S. Collins
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Samuli Ripatti
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.66859.340000 0004 0546 1623Broad Institute of MIT & Harvard, Cambridge, MA 02142 USA
| | - Aarno Palotie
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Nelson B. Freimer
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Karen L. Mohlke
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Laura J. Scott
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Xiaoquan Wen
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Eric B. Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139 USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland.
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
22
|
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power. Nat Commun 2022; 13:241. [PMID: 35017556 PMCID: PMC8752755 DOI: 10.1038/s41467-021-27930-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average). GWAS often assume a linear phenotype-covariate relationship which may not hold in practice. Here the authors present DeepNull, in which they apply deep learning to identify and adjust for complex non-linear relationships, improving phenotypic prediction and GWAS power.
Collapse
|
23
|
Zhou H, Zhang X, Chen J. Covariate adaptive familywise error rate control for genome-wide association studies. Biometrika 2021; 108:915-931. [PMID: 34803516 DOI: 10.1093/biomet/asaa098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Indexed: 11/12/2022] Open
Abstract
The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.
Collapse
Affiliation(s)
- Huijuan Zhou
- Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China
| | - Xianyang Zhang
- Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Mayo Clinic, 200 First St. SW, Rochester, Minnesota 55905, U.S.A
| |
Collapse
|
24
|
Hormozdiari F, Jung J, Eskin E, J. Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing. Genome Biol 2021; 22:128. [PMID: 33931127 PMCID: PMC8086090 DOI: 10.1186/s13059-021-02353-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/15/2021] [Indexed: 11/10/2022] Open
Abstract
In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.
Collapse
Affiliation(s)
- Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115 MA USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Junghyun Jung
- Department of Life Science, Dongguk University-Seoul, Seoul, 04620 South Korea
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, 90095 CA USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, 90095 CA USA
| | - Jong Wha J. Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620 South Korea
| |
Collapse
|
25
|
Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol 2021; 22:127. [PMID: 33931130 PMCID: PMC8086086 DOI: 10.1186/s13059-021-02347-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.
Collapse
Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| |
Collapse
|
26
|
Díez-Obrero V, Dampier CH, Moratalla-Navarro F, Devall M, Plummer SJ, Díez-Villanueva A, Peters U, Bien S, Huyghe JR, Kundaje A, Ibáñez-Sanz G, Guinó E, Obón-Santacana M, Carreras-Torres R, Casey G, Moreno V. Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci. Cell Mol Gastroenterol Hepatol 2021; 12:181-197. [PMID: 33601062 PMCID: PMC8102177 DOI: 10.1016/j.jcmgh.2021.02.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND & AIMS The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. METHODS We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. RESULTS We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. CONCLUSIONS We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.
Collapse
Affiliation(s)
- Virginia Díez-Obrero
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Christopher H Dampier
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia; Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - Ferran Moratalla-Navarro
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Matthew Devall
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Sarah J Plummer
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Anna Díez-Villanueva
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Ulrike Peters
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephanie Bien
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jeroen R Huyghe
- Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, California
| | - Gemma Ibáñez-Sanz
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Gastroenterology Department, Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Elisabeth Guinó
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Mireia Obón-Santacana
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Robert Carreras-Torres
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.
| | - Víctor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
| |
Collapse
|
27
|
Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y, Kim-Hellmuth S, Wang G, Jiang Z, Zhou D, Hormozdiari F, Liu B, Rao A, Hamel AR, Pividori MD, Aguet F, Bastarache L, Jordan DM, Verbanck M, Do R, Stephens M, Ardlie K, McCarthy M, Montgomery SB, Segrè AV, Brown CD, Lappalainen T, Wen X, Im HK. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol 2021; 22:49. [PMID: 33499903 PMCID: PMC7836161 DOI: 10.1186/s13059-020-02252-4] [Citation(s) in RCA: 128] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 12/18/2020] [Indexed: 12/12/2022] Open
Abstract
The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.
Collapse
Affiliation(s)
- Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Data Science Institute, Vanderbilt University, Nashville, TN, USA
- Clare Hall, University of Cambridge, Cambridge, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Yanyu Liang
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - YoSon Park
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Sarah Kim-Hellmuth
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Gao Wang
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Zhuoxun Jiang
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Farhad Hormozdiari
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Boxiang Liu
- Department of Biology, Stanford University, Stanford, 94305, CA, USA
| | - Abhiram Rao
- Department of Biology, Stanford University, Stanford, 94305, CA, USA
| | - Andrew R Hamel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Milton D Pividori
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University, Nashville, TN, USA
- Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Daniel M Jordan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marie Verbanck
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Université de Paris - EA 7537 BIOSTM, Paris, France
| | - Ron Do
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Kristin Ardlie
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Stephen B Montgomery
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Ayellet V Segrè
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Christopher D Brown
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
| |
Collapse
|
28
|
The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369:1318-1330. [PMID: 32913098 PMCID: PMC7737656 DOI: 10.1126/science.aaz1776] [Citation(s) in RCA: 1887] [Impact Index Per Article: 471.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 07/30/2020] [Indexed: 02/06/2023]
Abstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.
Collapse
|
29
|
Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, Shi H, Zou Y, Wang G, Morrison J, Zhu AC, Qiao M, Li Z, Stephens M, He X, He C. Genetic analyses support the contribution of mRNA N 6-methyladenosine (m 6A) modification to human disease heritability. Nat Genet 2020; 52:939-949. [PMID: 32601472 PMCID: PMC7483307 DOI: 10.1038/s41588-020-0644-z] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/11/2020] [Indexed: 12/18/2022]
Abstract
N6-methyladenosine (m6A) plays important roles in regulating messenger RNA processing. Despite rapid progress in this field, little is known about the genetic determinants of m6A modification and their role in common diseases. In this study, we mapped the quantitative trait loci (QTLs) of m6A peaks in 60 Yoruba (YRI) lymphoblastoid cell lines. We found that m6A QTLs are largely independent of expression and splicing QTLs and are enriched with binding sites of RNA-binding proteins, RNA structure-changing variants and transcriptional features. Joint analysis of the QTLs of m6A and related molecular traits suggests that the downstream effects of m6A are heterogeneous and context dependent. We identified proteins that mediate m6A effects on translation. Through integration with data from genome-wide association studies, we show that m6A QTLs contribute to the heritability of various immune and blood-related traits at levels comparable to splicing QTLs and roughly half of expression QTLs. By leveraging m6A QTLs in a transcriptome-wide association study framework, we identified putative risk genes of these traits.
Collapse
Affiliation(s)
- Zijie Zhang
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Zhongyu Zou
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Maguanyun Qiu
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Jiakun Tian
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Laura Sieh
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Hailing Shi
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Yuxin Zou
- Department of Statistics, The University of Chicago, Chicago, IL, USA
| | - Gao Wang
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Jean Morrison
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Allen C Zhu
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
- Medical Scientist Training Program/Committee on Cancer Biology, The University of Chicago, Chicago, IL, USA
| | - Min Qiao
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zhongshan Li
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
- Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China
| | - Matthew Stephens
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
- Department of Statistics, The University of Chicago, Chicago, IL, USA.
| | - Xin He
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
| | - Chuan He
- Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA.
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA.
- Medical Scientist Training Program/Committee on Cancer Biology, The University of Chicago, Chicago, IL, USA.
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA.
| |
Collapse
|
30
|
Pividori M, Rajagopal PS, Barbeira A, Liang Y, Melia O, Bastarache L, Park Y, Consortium GTE, Wen X, Im HK. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. SCIENCE ADVANCES 2020; 6:eaba2083. [PMID: 32917697 PMCID: PMC11206444 DOI: 10.1126/sciadv.aba2083] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 07/29/2020] [Indexed: 05/02/2023]
Abstract
Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.
Collapse
Affiliation(s)
- Milton Pividori
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Padma S Rajagopal
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Alvaro Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Yanyu Liang
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Owen Melia
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University, Nashville, TN, USA
- Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - YoSon Park
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
| | - Hae K Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
| |
Collapse
|
31
|
Zhang S, Zhang H, Zhou Y, Qiao M, Zhao S, Kozlova A, Shi J, Sanders AR, Wang G, Luo K, Sengupta S, West S, Qian S, Streit M, Avramopoulos D, Cowan CA, Chen M, Pang ZP, Gejman PV, He X, Duan J. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science 2020; 369:561-565. [PMID: 32732423 PMCID: PMC7773145 DOI: 10.1126/science.aay3983] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 09/16/2019] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Abstract
Most neuropsychiatric disease risk variants are in noncoding sequences and lack functional interpretation. Because regulatory sequences often reside in open chromatin, we reasoned that neuropsychiatric disease risk variants may affect chromatin accessibility during neurodevelopment. Using human induced pluripotent stem cell (iPSC)-derived neurons that model developing brains, we identified thousands of genetic variants exhibiting allele-specific open chromatin (ASoC). These neuronal ASoCs were partially driven by altered transcription factor binding, overrepresented in brain gene enhancers and expression quantitative trait loci, and frequently associated with distal genes through chromatin contacts. ASoCs were enriched for genetic variants associated with brain disorders, enabling identification of functional schizophrenia risk variants and their cis-target genes. This study highlights ASoC as a functional mechanism of noncoding neuropsychiatric risk variants, providing a powerful framework for identifying disease causal variants and genes.
Collapse
Affiliation(s)
- Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Yifan Zhou
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
- The Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Min Qiao
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Siming Zhao
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Alan R Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Gao Wang
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Subhajit Sengupta
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Siobhan West
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Sheng Qian
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Michael Streit
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Dimitrios Avramopoulos
- Department of Genetic Medicine and Psychiatry, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chad A Cowan
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Mengjie Chen
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhiping P Pang
- Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Pablo V Gejman
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
- Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA.
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
32
|
A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data. PLoS Comput Biol 2020; 16:e1007770. [PMID: 32516306 PMCID: PMC7332077 DOI: 10.1371/journal.pcbi.1007770] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 07/02/2020] [Accepted: 03/03/2020] [Indexed: 11/19/2022] Open
Abstract
A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. We used BAGEA to integrate directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10−7. For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology. Many geneticists wish to map changes in DNA sequences to changes in human traits and to understand these processes mechanistically. Here we present BAGEA, a framework to study this question for gene expression. Specifically, BAGEA models a genome variant’s impact on gene expression based on established genome annotations. BAGEA predicts not only whether a variant has an impact on gene expression, but also the sign of the effect. We applied BAGEA to datasets from different tissues and cell types and found that annotations most predictive of gene expression in a given tissue were typically derived from similar tissues. Based on our results, we recommend the use of BAGEA to reveal annotations relevant to expression biology and to build predictive models of gene expression.
Collapse
|
33
|
Liu B, Montgomery SB. Identifying causal variants and genes using functional genomics in specialized cell types and contexts. Hum Genet 2020; 139:95-102. [PMID: 31317254 PMCID: PMC6942616 DOI: 10.1007/s00439-019-02044-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 07/03/2019] [Indexed: 02/06/2023]
Abstract
A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.
Collapse
Affiliation(s)
- Boxiang Liu
- Department of Biology, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University, Stanford, USA.
- Department of Genetics, Stanford University, Stanford, USA.
| |
Collapse
|
34
|
Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. Nat Commun 2019; 10:3433. [PMID: 31366926 PMCID: PMC6668431 DOI: 10.1038/s41467-019-11247-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 07/03/2019] [Indexed: 12/31/2022] Open
Abstract
Multiple hypothesis testing is an essential component of modern data science. In many settings, in addition to the p-value, additional covariates for each hypothesis are available, e.g., functional annotation of variants in genome-wide association studies. Such information is ignored by popular multiple testing approaches such as the Benjamini-Hochberg procedure (BH). Here we introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% more associations than BH at the same false discovery rate. We prove that AdaFDR controls false discovery proportion and show that it makes substantially more discoveries while controlling false discovery rate (FDR) in extensive experiments. AdaFDR is computationally efficient and allows multi-dimensional covariates with both numeric and categorical values, making it broadly useful across many applications.
Collapse
|
35
|
Gillies CE, Putler R, Menon R, Otto E, Yasutake K, Nair V, Hoover P, Lieb D, Li S, Eddy S, Fermin D, McNulty MT, Hacohen N, Kiryluk K, Kretzler M, Wen X, Sampson MG. An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome. Am J Hum Genet 2018; 103:232-244. [PMID: 30057032 PMCID: PMC6081280 DOI: 10.1016/j.ajhg.2018.07.004] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 06/29/2018] [Indexed: 01/14/2023] Open
Abstract
Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."
Collapse
Affiliation(s)
- Christopher E Gillies
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Rosemary Putler
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Edgar Otto
- Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Kalyn Yasutake
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Viji Nair
- Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Paul Hoover
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA 02114, USA; Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | - David Lieb
- Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | - Shuqiang Li
- Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | - Sean Eddy
- Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Damian Fermin
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Michelle T McNulty
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Nir Hacohen
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA 02114, USA; Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Matthias Kretzler
- Department of Computational Medicine and Bioinformatics, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA; Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Matthew G Sampson
- Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA.
| |
Collapse
|
36
|
Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet 2018; 50:956-967. [PMID: 29955180 PMCID: PMC6248311 DOI: 10.1038/s41588-018-0154-4] [Citation(s) in RCA: 273] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 05/08/2018] [Indexed: 12/27/2022]
Abstract
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
Collapse
Affiliation(s)
- Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Clare Hall, University of Cambridge, Cambridge, UK.
| | - Ayellet V Segrè
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Ophthalmology and Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
| | - Martijn van de Bunt
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hualin S Xi
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Farhad Hormozdiari
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Anuar Konkashbaev
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eske M Derks
- Translational Neurogenomics Group, QIMR Berghofer, Brisbane, Queensland, Australia
| | - François Aguet
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jie Quan
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Dan L Nicolae
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Statistics, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Manolis Kellis
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gad Getz
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kristin G Ardlie
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| |
Collapse
|
37
|
Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature 2017; 550:204-213. [PMID: 29022597 PMCID: PMC5776756 DOI: 10.1038/nature24277] [Citation(s) in RCA: 2464] [Impact Index Per Article: 352.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 09/15/2017] [Indexed: 12/12/2022]
Abstract
Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
Collapse
Affiliation(s)
- Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Christopher D Brown
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Barbara E Engelhardt
- Department of Computer Science and Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08540, USA
| | - Stephen B Montgomery
- Department of Genetics, Stanford University, Stanford, California 94305, USA
- Department of Pathology, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
38
|
Wen X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann Appl Stat 2016. [DOI: 10.1214/16-aoas952] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|