1
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Wang J, Zhang Z, Lu Z, Mancuso N, Gazal S. Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions. Am J Hum Genet 2024; 111:2117-2128. [PMID: 39191255 PMCID: PMC11480800 DOI: 10.1016/j.ajhg.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/29/2024] Open
Abstract
Multi-ancestry genome-wide association studies (GWASs) have highlighted the existence of variants with ancestry-specific effect sizes. Understanding where and why these ancestry-specific effects occur is fundamental to understanding the genetic basis of human diseases and complex traits. Here, we characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-sequencing data in peripheral blood mononuclear cells for 21 individuals with East Asian (EAS) ancestry and 23 individuals with European (EUR) ancestry (172,385 cells); then, we tested whether variants surrounding those genes were enriched in disease variants with ancestry-specific effect sizes by leveraging ancestry-matched GWASs of 31 diseases and complex traits (average n ∼ 90,000 and ∼ 267,000 in EAS and EUR, respectively). We observed that ancDE genes tended to be cell-type specific and enriched in genes interacting with the environment and in variants with ancestry-specific disease effect sizes, which suggests cell-type-specific, gene-by-environment interactions shared between regulatory and disease architectures. Finally, we illustrated how different environments might have led to ancestry-specific myeloid cell leukemia 1 (MCL1) expression in B cells and ancestry-specific allele effect sizes in lymphocyte count GWASs for variants surrounding MCL1. Our results imply that large single-cell and GWAS datasets from diverse ancestries are required to improve our understanding of human diseases.
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Affiliation(s)
- Juehan Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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2
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Akamatsu K, Golzari S, Amariuta T. Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24314410. [PMID: 39399015 PMCID: PMC11469471 DOI: 10.1101/2024.09.25.24314410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.
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Affiliation(s)
- Kai Akamatsu
- School of Biological Sciences, UC San Diego, La Jolla, CA, USA
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
| | - Stephen Golzari
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Tiffany Amariuta
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
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3
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Chen Y, Liu S, Ren Z, Wang F, Liang Q, Jiang Y, Dai R, Duan F, Han C, Ning Z, Xia Y, Li M, Yuan K, Qiu W, Yan XX, Dai J, Kopp RF, Huang J, Xu S, Tang B, Wu L, Gamazon ER, Bigdeli T, Gershon E, Huang H, Ma C, Liu C, Chen C. Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies. Am J Hum Genet 2024:S0002-9297(24)00336-7. [PMID: 39362218 DOI: 10.1016/j.ajhg.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 09/04/2024] [Accepted: 09/06/2024] [Indexed: 10/05/2024] Open
Abstract
Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.
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Affiliation(s)
- Yu Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sihan Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Zongyao Ren
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Feiran Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Qiuman Liang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Yi Jiang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Fangyuan Duan
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Cong Han
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Zhilin Ning
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yan Xia
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Miao Li
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Kai Yuan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wenying Qiu
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences, Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xiao-Xin Yan
- Department of Human Anatomy and Neurobiology, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiapei Dai
- Wuhan Institute for Neuroscience and Engineering, South-Central University for Nationalities, Wuhan, China
| | - Richard F Kopp
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jufang Huang
- Department of Human Anatomy and Neurobiology, Xiangya School of Medicine, Central South University, Changsha, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lingqian Wu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Eric R Gamazon
- Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Tim Bigdeli
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Elliot Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | | | - Chao Ma
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences, Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, China.
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4
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Wen J, Sun Q, Huang L, Zhou L, Doyle MF, Ekunwe L, Durda P, Olson NC, Reiner AP, Li Y, Raffield LM. Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study. Genetics 2024; 228:iyae098. [PMID: 39056362 PMCID: PMC11373511 DOI: 10.1093/genetics/iyae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/05/2024] [Indexed: 07/28/2024] Open
Abstract
Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lingbo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Lynette Ekunwe
- Department of Medicine, University of MS Medical Center (UMMC), Jackson, MS 39213, USA
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Nels C Olson
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research, Seattle, WA 98109, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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5
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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning. Nat Commun 2024; 15:6646. [PMID: 39103319 DOI: 10.1038/s41467-024-50983-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/26/2024] [Indexed: 08/07/2024] Open
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.
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Affiliation(s)
- Randy L Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Denis Avey
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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6
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Taylor DJ, Chhetri SB, Tassia MG, Biddanda A, Yan SM, Wojcik GL, Battle A, McCoy RC. Sources of gene expression variation in a globally diverse human cohort. Nature 2024; 632:122-130. [PMID: 39020179 PMCID: PMC11291278 DOI: 10.1038/s41586-024-07708-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 06/12/2024] [Indexed: 07/19/2024]
Abstract
Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity1-5. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project6, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-expression quantitative trait loci (eQTLs) and cis-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent 'population-specific' effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.
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Affiliation(s)
- Dylan J Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Michael G Tassia
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Arjun Biddanda
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie M Yan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA.
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7
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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8
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import. Nat Genet 2024:10.1038/s41588-024-01827-2. [PMID: 38977856 DOI: 10.1038/s41588-024-01827-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 06/10/2024] [Indexed: 07/10/2024]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.
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Affiliation(s)
- Artem Khan
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Gokhan Unlu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuyang Liu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Ece Kilic
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Timothy C Kenny
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Kıvanç Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
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9
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Tan Q, Xu X, Zhou H, Jia J, Jia Y, Tu H, Zhou D, Wu X. A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors. Mol Psychiatry 2024:10.1038/s41380-024-02605-6. [PMID: 38816585 DOI: 10.1038/s41380-024-02605-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024]
Abstract
Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.
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Affiliation(s)
- Qilong Tan
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Xiaohang Xu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Hanyi Zhou
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Junlin Jia
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Yubing Jia
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Huakang Tu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Zhou
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China.
- School of Medicine and Health Science, George Washington University, Washington, DC, USA.
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10
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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.20.23291605. [PMID: 37425698 PMCID: PMC10327185 DOI: 10.1101/2023.06.20.23291605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.
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11
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Lee DJ, Moon JS, Song DK, Lee YS, Kim DS, Cho NJ, Gil HW, Lee EY, Park S. Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants. Kidney Res Clin Pract 2024; 43:299-312. [PMID: 37919891 PMCID: PMC11181046 DOI: 10.23876/j.krcp.23.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry. METHODS We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES). RESULTS More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues. CONCLUSION In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.
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Affiliation(s)
- Dong-Jin Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Jong-Seok Moon
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
| | - Dae Kwon Song
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Yong Seok Lee
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Dong-Sub Kim
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Nam-Jun Cho
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Hyo-Wook Gil
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Eun Young Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Institute of Tissue Regeneration, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea
| | - Samel Park
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
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12
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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13
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Wittich H, Ardlie K, Taylor KD, Durda P, Liu Y, Mikhaylova A, Gignoux CR, Cho MH, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits. Am J Hum Genet 2024; 111:445-455. [PMID: 38320554 PMCID: PMC10940016 DOI: 10.1016/j.ajhg.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.
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Affiliation(s)
- Henry Wittich
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Anna Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Heather E Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
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14
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Chen Y, Liu S, Ren Z, Wang F, Jiang Y, Dai R, Duan F, Han C, Ning Z, Xia Y, Li M, Yuan K, Qiu W, Yan XX, Dai J, Kopp RF, Huang J, Xu S, Tang B, Gamazon ER, Bigdeli T, Gershon E, Huang H, Ma C, Liu C, Chen C. Brain eQTLs of European, African American, and Asian ancestry improve interpretation of schizophrenia GWAS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.13.24301833. [PMID: 38405973 PMCID: PMC10888997 DOI: 10.1101/2024.02.13.24301833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet, the majority of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n=158), Europeans (EUR, n=408), and East Asians (EAS, n=217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs (representing ∼17% of all eQTLs pairs) linked to 1,276 genes (about 10% of all eGenes) and 198,769 SNPs (approximately 16% of all eSNPs) were identified only in the non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare (MAF < 0.05) in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified seven new risk genes ( SFXN2 , RP11-282018.3 , CYP17A1 , VPS37B , DENR , FTCDNL1 , and NT5DC2 ), and three potential novel regulatory variants in known risk genes ( CNNM2 , C12orf65 , and MPHOSPH9 ) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of novel risk genes in SCZ.
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15
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Pan C, Cheng B, Qin X, Cheng S, Liu L, Yang X, Meng P, Zhang N, He D, Cai Q, Wei W, Hui J, Wen Y, Jia Y, Liu H, Zhang F. Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function. Brief Bioinform 2024; 25:bbae070. [PMID: 38436562 DOI: 10.1093/bib/bbae070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857). CONCLUSIONS Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.
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Affiliation(s)
- Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Jingni Hui
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
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16
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Ehsan N, Kotis BM, Castel SE, Song EJ, Mancuso N, Mohammadi P. Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data. Nat Commun 2024; 15:522. [PMID: 38225224 PMCID: PMC10789818 DOI: 10.1038/s41467-024-44710-8] [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: 01/31/2022] [Accepted: 12/30/2023] [Indexed: 01/17/2024] Open
Abstract
Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.
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Affiliation(s)
- Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Bence M Kotis
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Stephane E Castel
- Department of Systems Biology, Columbia University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Eric J Song
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern, California, CA, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
- 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.
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17
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Mishra M, Nahlawi L, Zhong Y, De T, Yang G, Alarcon C, Perera MA. LA-GEM: imputation of gene expression with incorporation of Local Ancestry. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:341-358. [PMID: 38160291 PMCID: PMC10764069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Gene imputation and TWAS have become a staple in the genomics medicine discovery space; helping to identify genes whose regulation effects may contribute to disease susceptibility. However, the cohorts on which these methods are built are overwhelmingly of European Ancestry. This means that the unique regulatory variation that exist in non-European populations, specifically African Ancestry populations, may not be included in the current models. Moreover, African Americans are an admixed population, with a mix of European and African segments within their genome. No gene imputation model thus far has incorporated the effect of local ancestry (LA) on gene expression imputation. As such, we created LA-GEM which was trained and tested on a cohort of 60 African American hepatocyte primary cultures. Uniquely, LA-GEM include local ancestry inference in its prediction of gene expression. We compared the performance of LA-GEM to PrediXcan trained the same dataset (with no inclusion of local ancestry) We were able to reliably predict the expression of 2559 genes (1326 in LA-GEM and 1236 in PrediXcan). Of these, 546 genes were unique to LA-GEM, including the CYP3A5 gene which is critical to drug metabolism. We conducted TWAS analysis on two African American clinical cohorts with pharmacogenomics phenotypic information to identity novel gene associations. In our IWPC warfarin cohort, we identified 17 transcriptome-wide significant hits. No gene reached are prespecified significance level in the clopidogrel cohort. We did see suggestive association with RAS3A to P2RY12 Reactivity Units (PRU), a clinical measure of response to anti-platelet therapy. This method demonstrated the need for the incorporation of LA into study in admixed populations.
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Affiliation(s)
- Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA†Contributed equally to the work
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18
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Taylor DJ, Chhetri SB, Tassia MG, Biddanda A, Battle A, McCoy RC. Sources of gene expression variation in a globally diverse human cohort. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.565639. [PMID: 37965206 PMCID: PMC10635147 DOI: 10.1101/2023.11.04.565639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Genetic variation influencing gene expression and splicing is a key source of phenotypic diversity. Though invaluable, studies investigating these links in humans have been strongly biased toward participants of European ancestries, diminishing generalizability and hindering evolutionary research. To address these limitations, we developed MAGE, an open-access RNA-seq data set of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, mirroring variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-eQTLs and cis-sQTLs, respective), identifying >15,000 putatively causal eQTLs and >16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1310 eQTLs and 1657 sQTLs that are largely private to previously underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations and that apparent "population-specific" effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands understanding of gene expression diversity across human populations and provides an inclusive resource for studying the evolution and function of human genomes.
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Affiliation(s)
- Dylan J. Taylor
- Department of Biology, Johns Hopkins University, Baltimore MD, USA
| | - Surya B. Chhetri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
| | | | - Arjun Biddanda
- Department of Biology, Johns Hopkins University, Baltimore MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore MD, USA
| | - Rajiv C. McCoy
- Department of Biology, Johns Hopkins University, Baltimore MD, USA
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19
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Wang J, Gazal S. Ancestry-specific regulatory and disease architectures are likely due to cell-type-specific gene-by-environment interactions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.20.23297214. [PMID: 37905038 PMCID: PMC10615008 DOI: 10.1101/2023.10.20.23297214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Multi-ancestry genome-wide association studies (GWAS) have highlighted the existence of variants with ancestry-specific effect sizes. Understanding where and why these ancestry-specific effects occur is fundamental to understanding the genetic basis of human diseases and complex traits. Here, we characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-seq data in peripheral blood mononuclear cells for 21 individuals with East Asian (EAS) ancestry and 23 individuals with European (EUR) ancestry (172K cells); then, we tested if variants surrounding those genes were enriched in disease variants with ancestry-specific effect sizes by leveraging ancestry-matched GWAS of 31 diseases and complex traits (average N = 90K and 267K in EAS and EUR, respectively). We observed that ancDE genes tend to be cell-type-specific, to be enriched in genes interacting with the environment, and in variants with ancestry-specific disease effect sizes, suggesting the impact of shared cell-type-specific gene-by-environment (GxE) interactions between regulatory and disease architectures. Finally, we illustrated how GxE interactions might have led to ancestry-specific MCL1 expression in B cells, and ancestry-specific allele effect sizes in lymphocyte count GWAS for variants surrounding MCL1. Our results imply that large single-cell and GWAS datasets in diverse populations are required to improve our understanding on the effect of genetic variants on human diseases.
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Affiliation(s)
- Juehan Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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20
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. HGG ADVANCES 2023; 4:100216. [PMID: 37869564 PMCID: PMC10589725 DOI: 10.1016/j.xhgg.2023.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/27/2023] [Indexed: 10/24/2023] Open
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - NHLBI TOPMed Consortium
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
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21
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Yang C, Veenstra J, Bartz TM, Pahl MC, Hallmark B, Chen YDI, Westra J, Steffen LM, Brown CD, Siscovick D, Tsai MY, Wood AC, Rich SS, Smith CE, O'Connor TD, Mozaffarian D, Grant SFA, Chilton FH, Tintle NL, Lemaitre RN, Manichaikul A. Genome-wide association studies and fine-mapping identify genomic loci for n-3 and n-6 polyunsaturated fatty acids in Hispanic American and African American cohorts. Commun Biol 2023; 6:852. [PMID: 37587153 PMCID: PMC10432561 DOI: 10.1038/s42003-023-05219-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: 02/21/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) play critical roles in human health. Prior genome-wide association studies (GWAS) of n-3 and n-6 PUFAs in European Americans from the CHARGE Consortium have documented strong genetic signals in/near the FADS locus on chromosome 11. We performed a GWAS of four n-3 and four n-6 PUFAs in Hispanic American (n = 1454) and African American (n = 2278) participants from three CHARGE cohorts. Applying a genome-wide significance threshold of P < 5 × 10-8, we confirmed association of the FADS signal and found evidence of two additional signals (in DAGLA and BEST1) within 200 kb of the originally reported FADS signal. Outside of the FADS region, we identified novel signals for arachidonic acid (AA) in Hispanic Americans located in/near genes including TMX2, SLC29A2, ANKRD13D and POLD4, and spanning a > 9 Mb region on chromosome 11 (57.5 Mb ~ 67.1 Mb). Among these novel signals, we found associations unique to Hispanic Americans, including rs28364240, a POLD4 missense variant for AA that is common in CHARGE Hispanic Americans but absent in other race/ancestry groups. Our study sheds light on the genetics of PUFAs and the value of investigating complex trait genetics across diverse ancestry populations.
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Affiliation(s)
- Chaojie Yang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Jenna Veenstra
- Departments of Biology and Statistics, Dordt University, Sioux Center, IA, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Brian Hallmark
- Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences and Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jason Westra
- Fatty Acid Research Institute, Sioux Falls, SD, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Alexis C Wood
- USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Caren E Smith
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Timothy D O'Connor
- Institute for Genome Sciences; Program in Personalized and Genomic Medicine; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science & Policy, Tufts University, Tufts School of Medicine and Division of Cardiology, Tufts Medical Center, Boston, MA, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Endocrinology and Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Floyd H Chilton
- School of Nutritional Sciences and Wellness and the BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | - Nathan L Tintle
- Fatty Acid Research Institute, Sioux Falls, SD, USA
- University of Illinois, Chicago, Chicago, IL, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
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22
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Gao Y, Sharma T, Cui Y. Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective. Annu Rev Biomed Data Sci 2023; 6:153-171. [PMID: 37104653 PMCID: PMC10529864 DOI: 10.1146/annurev-biodatasci-020722-020704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Teena Sharma
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Yan Cui
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
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23
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Rouskas K, Katsareli EA, Amerikanou C, Dimopoulos AC, Glentis S, Kalantzi A, Skoulakis A, Panousis N, Ongen H, Bielser D, Planchon A, Romano L, Harokopos V, Reczko M, Moulos P, Griniatsos I, Diamantis T, Dermitzakis ET, Ragoussis J, Dedoussis G, Dimas AS. Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population. BMC Genomics 2023; 24:442. [PMID: 37543566 PMCID: PMC10403965 DOI: 10.1186/s12864-023-09532-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: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.
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Affiliation(s)
- Konstantinos Rouskas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, Greece
| | - Efthymia A Katsareli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Charalampia Amerikanou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Alexandros C Dimopoulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Hellenic Naval Academy, Hatzikyriakou Avenue, Pireaus, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children's Hospital, Athens, Greece
| | - Alexandra Kalantzi
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Anargyros Skoulakis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | | | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Deborah Bielser
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Alexandra Planchon
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Vaggelis Harokopos
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Martin Reczko
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Center of New Biotechnologies & Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Griniatsos
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Theodoros Diamantis
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Bioengineering, McGill University, Montréal, QC, Canada
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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24
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Ghaffar A, Nyholt DR. Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci. Hum Genet 2023; 142:1113-1137. [PMID: 37245199 PMCID: PMC10449685 DOI: 10.1007/s00439-023-02568-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/02/2023] [Indexed: 05/29/2023]
Abstract
Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.
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Affiliation(s)
- Ammarah Ghaffar
- Statistical and Genomic Epidemiology Laboratory, School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
| | - Dale R Nyholt
- Statistical and Genomic Epidemiology Laboratory, School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
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25
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McLaren PJ, Porreca I, Iaconis G, Mok HP, Mukhopadhyay S, Karakoc E, Cristinelli S, Pomilla C, Bartha I, Thorball CW, Tough RH, Angelino P, Kiar CS, Carstensen T, Fatumo S, Porter T, Jarvis I, Skarnes WC, Bassett A, DeGorter MK, Sathya Moorthy MP, Tuff JF, Kim EY, Walter M, Simons LM, Bashirova A, Buchbinder S, Carrington M, Cossarizza A, De Luca A, Goedert JJ, Goldstein DB, Haas DW, Herbeck JT, Johnson EO, Kaleebu P, Kilembe W, Kirk GD, Kootstra NA, Kral AH, Lambotte O, Luo M, Mallal S, Martinez-Picado J, Meyer L, Miro JM, Moodley P, Motala AA, Mullins JI, Nam K, Obel N, Pirie F, Plummer FA, Poli G, Price MA, Rauch A, Theodorou I, Trkola A, Walker BD, Winkler CA, Zagury JF, Montgomery SB, Ciuffi A, Hultquist JF, Wolinsky SM, Dougan G, Lever AML, Gurdasani D, Groom H, Sandhu MS, Fellay J. Africa-specific human genetic variation near CHD1L associates with HIV-1 load. Nature 2023; 620:1025-1030. [PMID: 37532928 PMCID: PMC10848312 DOI: 10.1038/s41586-023-06370-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 06/26/2023] [Indexed: 08/04/2023]
Abstract
HIV-1 remains a global health crisis1, highlighting the need to identify new targets for therapies. Here, given the disproportionate HIV-1 burden and marked human genome diversity in Africa2, we assessed the genetic determinants of control of set-point viral load in 3,879 people of African ancestries living with HIV-1 participating in the international collaboration for the genomics of HIV3. We identify a previously undescribed association signal on chromosome 1 where the peak variant associates with an approximately 0.3 log10-transformed copies per ml lower set-point viral load per minor allele copy and is specific to populations of African descent. The top associated variant is intergenic and lies between a long intergenic non-coding RNA (LINC00624) and the coding gene CHD1L, which encodes a helicase that is involved in DNA repair4. Infection assays in iPS cell-derived macrophages and other immortalized cell lines showed increased HIV-1 replication in CHD1L-knockdown and CHD1L-knockout cells. We provide evidence from population genetic studies that Africa-specific genetic variation near CHD1L associates with HIV replication in vivo. Although experimental studies suggest that CHD1L is able to limit HIV infection in some cell types in vitro, further investigation is required to understand the mechanisms underlying our observations, including any potential indirect effects of CHD1L on HIV spread in vivo that our cell-based assays cannot recapitulate.
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Affiliation(s)
- Paul J McLaren
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada.
| | | | - Gennaro Iaconis
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Hoi Ping Mok
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Subhankar Mukhopadhyay
- Peter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King's College London, London, UK
| | | | - Sara Cristinelli
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - István Bartha
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christian W Thorball
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Riley H Tough
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Paolo Angelino
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Cher S Kiar
- Peter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Tommy Carstensen
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
- Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Isobel Jarvis
- Department of Medicine, University of Cambridge, Cambridge, UK
| | | | | | - Marianne K DeGorter
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mohana Prasad Sathya Moorthy
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey F Tuff
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Eun-Young Kim
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Miriam Walter
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lacy M Simons
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Arman Bashirova
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Susan Buchbinder
- Bridge HIV, San Francisco Department of Public Health, San Francisco, CA, USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Andrea De Luca
- University Division of Infectious Diseases, Siena University Hospital, Siena, Italy
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - James J Goedert
- Epidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University, New York, NY, USA
| | - David W Haas
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Joshua T Herbeck
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center and Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Pontiano Kaleebu
- Medical Research Council/Uganda Virus Research Institute & London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Gregory D Kirk
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Neeltje A Kootstra
- Department of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Alex H Kral
- Community Health Research Division, RTI International, Berkeley, CA, USA
| | - Olivier Lambotte
- Université Paris Saclay, Inserm UMR1184, CEA, Le Kremlin-Bicêtre, France
- APHP, Department of Clinical Immunology, Bicêtre Hospital, Le Kremlin-Bicêtre, France
| | - Ma Luo
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
- Vaccine and Therapeutics Laboratory, Medical and Scientific Affairs, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Simon Mallal
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Institute for Immunology & Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Javier Martinez-Picado
- University of Vic-Central University of Catalonia, Vic, Spain
- IrsiCaixa AIDS Research Institute, Badalona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Laurence Meyer
- INSERM U1018, Université Paris-Saclay, Le Kremlin Bicêtre, France
- AP-HP, Hôpital de Bicêtre, Département d'Épidémiologie, Le Kremlin Bicêtre, France
| | - José M Miro
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
- Infectious Diseases Service, Hospital Clinic-Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Pravi Moodley
- National Health Laboratory Service, South Africa and University of KwaZulu-Natal, Durban, South Africa
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - James I Mullins
- Department of Microbiology, University of Washington, Seattle, WA, USA
| | - Kireem Nam
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Niels Obel
- Department of Infectious Diseases, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Francis A Plummer
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Guido Poli
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Matthew A Price
- International AIDS Vaccine Initiative, New York, NY, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Andri Rauch
- Department of Infectious Diseases, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ioannis Theodorou
- Laboratoire d'Immunologie, Hôpital Robert Debré Paris, Paris, France
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Bruce D Walker
- Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Cheryl A Winkler
- Basic Research Laboratory, Molecular Genetic Epidemiology Section, Frederick National Laboratory for Cancer Research and Cancer Innovative Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Jean-François Zagury
- Laboratoire Génomique, Bioinformatique et Chimie Moléculaire, EA7528, Conservatoire National des Arts et Métiers, HESAM Université, Paris, France
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Angela Ciuffi
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Judd F Hultquist
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Steven M Wolinsky
- Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gordon Dougan
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Andrew M L Lever
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Medicine, National University of Singapore, Singapore, Singapore
| | - Deepti Gurdasani
- Queen Mary University of London, London, UK
- Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Harriet Groom
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Manjinder S Sandhu
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
- Omnigen Biodata, Cambridge, UK.
| | - Jacques Fellay
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
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26
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Kachuri L, Mak ACY, Hu D, Eng C, Huntsman S, Elhawary JR, Gupta N, Gabriel S, Xiao S, Keys KL, Oni-Orisan A, Rodríguez-Santana JR, LeNoir MA, Borrell LN, Zaitlen NA, Williams LK, Gignoux CR, Burchard EG, Ziv E. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat Genet 2023; 55:952-963. [PMID: 37231098 PMCID: PMC10260401 DOI: 10.1038/s41588-023-01377-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/21/2023] [Indexed: 05/27/2023]
Abstract
We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Akinyemi Oni-Orisan
- Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Esteban González Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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27
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527747. [PMID: 36798214 PMCID: PMC9934635 DOI: 10.1101/2023.02.09.527747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT, 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD, 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
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28
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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.
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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.
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29
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Wen J, Sun Q, Huang L, Zhou L, Doyle MF, Ekunwe L, Olson NC, Reiner AP, Li Y, Raffield LM. Gene Expression and Splicing QTL Analysis of Blood Cells in African American Participants from the Jackson Heart Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.26.538455. [PMID: 37163084 PMCID: PMC10168308 DOI: 10.1101/2023.04.26.538455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analysis using TOPMed whole genome sequencing-derived genotype data and RNA sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate (FDR) of 5%, we identified 4,798,604 significant eQTL-gene pairs, covering 16,538 unique genes; and 5,921,368 sQTL-gene-cluster pairs, covering 9,605 unique genes. About 31% of detected eQTL and sQTL variants with a minor allele frequency (MAF) > 1% in JHS were rare (MAF < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. We also generated 17,630 eQTL credible sets and 24,525 sQTL credible sets for genes (gene-clusters) with lead QTL p < 5e-8. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.
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30
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Knutson KA, Pan W. MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits. Hum Mol Genet 2023; 32:1237-1251. [PMID: 36179104 PMCID: PMC10077507 DOI: 10.1093/hmg/ddac247] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/16/2022] [Accepted: 09/28/2022] [Indexed: 01/16/2023] Open
Abstract
The Transcriptome-Wide Association Study (TWAS) is a widely used approach which integrates gene expression and Genome Wide Association Study (GWAS) data to study the role of cis-regulated gene expression (GEx) in complex traits. However, the genetic architecture of GEx varies across populations, and recent findings point to possible ancestral heterogeneity in the effects of GEx on complex traits, which may be amplified in TWAS by modeling GEx as a function of cis-eQTLs. Here, we present a novel extension to TWAS to account for heterogeneity in the effects of cis-regulated GEx which are correlated with ancestry. Our proposed Multi-Ancestry TwaS (MATS) framework jointly analyzes samples from multiple populations and distinguishes between shared, ancestry-specific and/or subject-specific expression-trait associations. As such, MATS amplifies power to detect shared GEx associations over ancestry-stratified TWAS through increased sample sizes, and facilitates the detection of genes with subgroup-specific associations which may be masked by standard TWAS. Our simulations highlight the improved Type-I error conservation and power of MATS compared with competing approaches. Our real data applications to Alzheimer's disease (AD) case-control genotypes from the Alzheimer's Disease Sequencing Project (ADSP) and continuous phenotypes from the UK Biobank (UKBB) identify a number of unique gene-trait associations which were not discovered through standard and/or ancestry-stratified TWAS. Ultimately, these findings promote MATS as a powerful method for detecting and estimating significant gene expression effects on complex traits within multi-ancestry cohorts and corroborates the mounting evidence for inter-population heterogeneity in gene-trait associations.
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Affiliation(s)
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
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31
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Yang C, Veenstra J, Bartz T, Pahl M, Hallmark B, Chen YDI, Westra J, Steffen L, Brown C, Siscovick D, Tsai M, Wood A, Rich S, Smith C, O'Connor T, Mozaffarian D, Grant S, Chilton F, Tintle N, Lemaitre R, Manichaikul A. Genome-Wide Association Studies and fine-mapping of genomic loci for n-3 and n-6 Polyunsaturated Fatty Acids in Hispanic American and African American Cohorts. RESEARCH SQUARE 2023:rs.3.rs-2073736. [PMID: 36865120 PMCID: PMC9980229 DOI: 10.21203/rs.3.rs-2073736/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) play critical roles in human health. Prior genome-wide association studies (GWAS) of n-3 and n-6 PUFAs in European Americans from the CHARGE Consortium have documented strong genetic signals in/near the FADS locus on chromosome 11. We performed a GWAS of four n-3 and four n-6 PUFAs in Hispanic American (n = 1454) and African American (n = 2278) participants from three CHARGE cohorts. Applying a genome-wide significance threshold of P < 5 x 10 - 8 , we confirmed association of the FADS signal and found evidence of two additional signals (in DAGLA and BEST1 ) within 200 kb of the originally reported FADS signal. Outside of the FADS region, we identified novel signals for arachidonic acid (AA) in Hispanic Americans located in/near genes including TMX2 , SLC29A2 , ANKRD13D and POLD4, and spanning a > 9 Mb region on chromosome 11 (57.5Mb ~ 67.1Mb). Among these novel signals, we found associations unique to Hispanic Americans, including rs28364240, a POLD4 missense variant for AA that is common in CHARGE Hispanic Americans but absent in other race/ancestry groups. Our study sheds light on the genetics of PUFAs and the value of investigating complex trait genetics across diverse ancestry populations.
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Affiliation(s)
| | | | | | | | | | - Yii-Der Ida Chen
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
| | | | | | | | | | | | | | | | | | | | | | - Struan Grant
- Children's Hospital of Philadelphia Research Institute
| | | | | | - Rozenn Lemaitre
- Cardiovascular Health Research Unit, University of Washington
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Xue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, Liu M, Zhang Y, Liu F. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology 2023; 48:518-528. [PMID: 36253546 PMCID: PMC9852427 DOI: 10.1038/s41386-022-01474-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023]
Abstract
Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.
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Affiliation(s)
- Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Sixiang Liang
- Tianjin Anding Hospital, Tianjin, 300222, China
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100088, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yong Zhang
- Tianjin Anding Hospital, Tianjin, 300222, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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Kurniansyah N, Wallace DA, Zhang Y, Yu B, Cade B, Wang H, Ochs-Balcom HM, Reiner AP, Ramos AR, Smith JD, Cai J, Daviglus M, Zee PC, Kaplan R, Kooperberg C, Rich SS, Rotter JI, Gharib SA, Redline S, Sofer T. An integrated multi-omics analysis of sleep-disordered breathing traits implicates P2XR4 purinergic signaling. Commun Biol 2023; 6:125. [PMID: 36721044 PMCID: PMC9889381 DOI: 10.1038/s42003-023-04520-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023] Open
Abstract
Sleep Disordered Breathing (SDB) is a common disease associated with increased risk for cardiometabolic, cardiovascular, and cognitive diseases. How SDB affects the molecular environment is still poorly understood. We study the association of three SDB measures with gene expression measured using RNA-seq in multiple blood tissues from the Multi-Ethnic Study of Atherosclerosis. We develop genetic instrumental variables for the associated transcripts as polygenic risk scores (tPRS), then generalize and validate the tPRS in the Women's Health Initiative. We measure the associations of the validated tPRS with SDB and serum metabolites in Hispanic Community Health Study/Study of Latinos. Here we find differential gene expression by blood cell type in relation to SDB traits and link P2XR4 expression to average oxyhemoglobin saturation during sleep and butyrylcarnitine (C4) levels. These findings can be used to develop interventions to alleviate the effect of SDB on the human molecular environment.
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Affiliation(s)
- Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Danielle A Wallace
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Heather M Ochs-Balcom
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Alexander P Reiner
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Joshua D Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, at Chapel Hill, NC, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Phyllis C Zee
- Division of Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL, USA
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, UW Medicine Sleep Center, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
- Departments of Medicine and of Biostatistics, Harvard University, Boston, MA, USA.
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Gedik H, Peterson RE, Riley BP, Vladimirov VI, Bacanu SA. Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals. Complex Psychiatry 2023; 9:130-144. [PMID: 37588130 PMCID: PMC10425719 DOI: 10.1159/000530223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/09/2023] [Indexed: 08/18/2023] Open
Abstract
Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P. Riley
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Silviu-Alin Bacanu
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
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Akter S, Roy AS, Tonmoy MIQ, Islam MS. Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach. J Biomol Struct Dyn 2022; 40:11173-11189. [PMID: 34355676 DOI: 10.1080/07391102.2021.1957714] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shamima Akter
- Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, USA
| | - Arpita Singha Roy
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | | | - Md Sajedul Islam
- Department of Biochemistry & Biotechnology, University of Barishal, Barishal, Bangladesh
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36
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Elgart M, Goodman MO, Isasi C, Chen H, Morrison AC, de Vries PS, Xu H, Manichaikul AW, Guo X, Franceschini N, Psaty BM, Rich SS, Rotter JI, Lloyd-Jones DM, Fornage M, Correa A, Heard-Costa NL, Vasan RS, Hernandez R, Kaplan RC, Redline S, Sofer T. Correlations between complex human phenotypes vary by genetic background, gender, and environment. Cell Rep Med 2022; 3:100844. [PMID: 36513073 PMCID: PMC9797952 DOI: 10.1016/j.xcrm.2022.100844] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/11/2022] [Accepted: 11/09/2022] [Indexed: 12/15/2022]
Abstract
We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carmen Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Huichun Xu
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA; Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nancy L Heard-Costa
- Boston University and National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Ramachandran S Vasan
- Boston University and National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; Preventive Medicine & Epidemiology, and Cardiovascular Medicine, Medicine, Boston University School of Medicine, and Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Ryan Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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A comparison of the genes and genesets identified by GWAS and EWAS of fifteen complex traits. Nat Commun 2022; 13:7816. [PMID: 36535946 PMCID: PMC9763500 DOI: 10.1038/s41467-022-35037-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 11/16/2022] [Indexed: 12/23/2022] Open
Abstract
Identifying genomic regions pertinent to complex traits is a common goal of genome-wide and epigenome-wide association studies (GWAS and EWAS). GWAS identify causal genetic variants, directly or via linkage disequilibrium, and EWAS identify variation in DNA methylation associated with a trait. While GWAS in principle will only detect variants due to causal genes, EWAS can also identify genes via confounding, or reverse causation. We systematically compare GWAS (N > 50,000) and EWAS (N > 4500) results of 15 complex traits. We evaluate if the genes or gene ontology terms flagged by GWAS and EWAS overlap, and find substantial overlap for diastolic blood pressure, (gene overlap P = 5.2 × 10-6; term overlap P = 0.001). We superimpose our empirical findings against simulated models of varying genetic and epigenetic architectures and observe that in most cases GWAS and EWAS are likely capturing distinct genesets. Our results indicate that GWAS and EWAS are capturing different aspects of the biology of complex traits.
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38
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Fryett JJ, Morris AP, Cordell HJ. Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. Genet Epidemiol 2022; 46:629-643. [PMID: 35930604 PMCID: PMC9804820 DOI: 10.1002/gepi.22496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/27/2022] [Accepted: 07/19/2022] [Indexed: 01/09/2023]
Abstract
As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.
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Affiliation(s)
- James J. Fryett
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal ResearchUniversity of ManchesterManchesterUK
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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39
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Caliebe A, Tekola‐Ayele F, Darst BF, Wang X, Song YE, Gui J, Sebro RA, Balding DJ, Saad M, Dubé M. Including diverse and admixed populations in genetic epidemiology research. Genet Epidemiol 2022; 46:347-371. [PMID: 35842778 PMCID: PMC9452464 DOI: 10.1002/gepi.22492] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
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Affiliation(s)
- Amke Caliebe
- Institute of Medical Informatics and StatisticsKiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Fasil Tekola‐Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMarylandUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Xuexia Wang
- Department of MathematicsUniversity of North TexasDentonTexasUSA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth CollegeOne Medical Center Dr.LebanonNew HampshireUSA
| | | | - David J. Balding
- Melbourne Integrative Genomics, Schools of BioSciences and of Mathematics & StatisticsUniversity of MelbourneMelbourneAustralia
| | - Mohamad Saad
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Neuroscience Research Center, Faculty of Medical SciencesLebanese UniversityBeirutLebanon
| | - Marie‐Pierre Dubé
- Department of Medicine, and Social and Preventive MedicineUniversité de MontréalMontréalQuébecCanada
- Beaulieu‐Saucier Pharmacogenomcis CentreMontreal Heart InstituteMontrealCanada
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40
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Lu Z, Gopalan S, Yuan D, Conti DV, Pasaniuc B, Gusev A, Mancuso N. Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Am J Hum Genet 2022; 109:1388-1404. [PMID: 35931050 PMCID: PMC9388396 DOI: 10.1016/j.ajhg.2022.07.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average nEA = 511 k, nAA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.
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Affiliation(s)
- Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Shyamalika Gopalan
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Dong Yuan
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - David V Conti
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA; Division of Genetics, Brigham & Women's Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA
| | - Nicholas Mancuso
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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Xue K, Liang S, Yang B, Zhu D, Xie Y, Qin W, Liu F, Zhang Y, Yu C. Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles. Psychol Med 2022; 52:2052-2061. [PMID: 33121546 DOI: 10.1017/s0033291720003876] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a common debilitating disorder characterized by impaired spontaneous brain activity, yet little is known about its alterations in dynamic properties and the molecular mechanisms associated with these changes. METHODS Based on the resting-state functional MRI data of 65 first-episode, treatment-naïve patients with MDD and 66 healthy controls, we compared dynamic regional homogeneity (dReHo) of spontaneous brain activity between the two groups, and we investigated gene expression profiles associated with dReHo alterations in MDD by leveraging transcriptional data from the Allen Human Brain Atlas and weighted gene co-expression network analysis. RESULTS Compared with healthy controls, patients with MDD consistently showed reduced dReHo in both fusiform gyri and in the right temporal pole and hippocampus. The expression profiles of 16 gene modules were correlated with dReHo alterations in MDD. These gene modules were enriched for various biological process terms, including immune, synaptic signalling, ion channels, mitochondrial function and protein metabolism, and were preferentially expressed in different cell types. CONCLUSIONS Patients with MDD have reduced dReHo in brain areas associated with emotional and cognitive regulation, and these changes may be related to complex polygenetic and polypathway mechanisms.
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Affiliation(s)
- Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Sixiang Liang
- Tianjin Anding Hospital, Tianjin 300222, China
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100088, China
| | - Bingbing Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yong Zhang
- Tianjin Anding Hospital, Tianjin 300222, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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42
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Joo J, Mak ACY, Xiao S, Sleiman PM, Hu D, Huntsman S, Eng C, Kan M, Diwakar AR, Lasky-Su JA, Weiss ST, Sordillo JE, Wu AC, Cloutier M, Canino G, Forno E, Celedón JC, Seibold MA, Hakonarson H, Williams LK, Burchard EG, Himes BE. Genome-wide association study in minority children with asthma implicates DNAH5 in bronchodilator responsiveness. Sci Rep 2022; 12:12514. [PMID: 35869121 PMCID: PMC9307508 DOI: 10.1038/s41598-022-16488-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/11/2022] [Indexed: 12/25/2022] Open
Abstract
Variability in response to short-acting β2-agonists (e.g., albuterol) among patients with asthma from diverse racial/ethnic groups may contribute to asthma disparities. We sought to identify genetic variants associated with bronchodilator response (BDR) to identify potential mechanisms of drug response and risk factors for worse asthma outcomes. Genome-wide association studies of bronchodilator response (BDR) were performed using TOPMed Whole Genome Sequencing data of the Asthma Translational Genomic Collaboration (ATGC), which corresponded to 1136 Puerto Rican, 656 Mexican and 4337 African American patients with asthma. With the population-specific GWAS results, a trans-ethnic meta-analysis was performed to identify BDR-associated variants shared across the three populations. Replication analysis was carried out in three pediatric asthma cohorts, including CAMP (Childhood Asthma Management Program; n = 560), GACRS (Genetics of Asthma in Costa Rica Study; n = 967) and HPR (Hartford-Puerto Rico; n = 417). A genome-wide significant locus (rs35661809; P = 3.61 × 10-8) in LINC02220, a non-coding RNA gene, was identified in Puerto Ricans. While this region was devoid of protein-coding genes, capture Hi-C data showed a distal interaction with the promoter of the DNAH5 gene in lung tissue. In replication analysis, the GACRS cohort yielded a nominal association (1-tailed P < 0.05). No genetic variant was associated with BDR at the genome-wide significant threshold in Mexicans and African Americans. Our findings help inform genetic underpinnings of BDR for understudied minority patients with asthma, but the limited availability of genetic data for racial/ethnic minority children with asthma remains a paramount challenge.
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Affiliation(s)
- Jaehyun Joo
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 402 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, UCSF, 1550 4th Street, Bldg 19B, San Francisco, CA, 94158, USA
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, UCSF, 1550 4th Street, Bldg 19B, San Francisco, CA, 94158, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, UCSF, 1550 4th Street, Bldg 19B, San Francisco, CA, 94158, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, UCSF, 1550 4th Street, Bldg 19B, San Francisco, CA, 94158, USA
| | - Mengyuan Kan
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 402 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Avantika R Diwakar
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 402 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Jessica A Lasky-Su
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Scott T Weiss
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Joanne E Sordillo
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Ann C Wu
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Michelle Cloutier
- Department of Pediatrics, University of Connecticut, Farmington, CT, USA
| | - Glorisa Canino
- Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, PR, USA
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, UMPC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UMPC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Max A Seibold
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, UCSF, 1550 4th Street, Bldg 19B, San Francisco, CA, 94158, USA.
- Department of Bioengineering and Therapeutic Sciences, University of Californica, San Francisco, CA, USA.
| | - Blanca E Himes
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 402 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
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43
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Rowland B, Venkatesh S, Tardaguila M, Wen J, Rosen JD, Tapia AL, Sun Q, Graff M, Vuckovic D, Lettre G, Sankaran VG, Voloudakis G, Roussos P, Huffman JE, Reiner AP, Soranzo N, Raffield LM, Li Y. Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits. Hum Mol Genet 2022; 31:2333-2347. [PMID: 35138379 PMCID: PMC9307312 DOI: 10.1093/hmg/ddac011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.
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Affiliation(s)
- Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sanan Venkatesh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Manuel Tardaguila
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jonathan D Rosen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Amanda L Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dragana Vuckovic
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Guillaume Lettre
- Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Georgios Voloudakis
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Panos Roussos
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Jennifer E Huffman
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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44
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Long E, García-Closas M, Chanock SJ, Camargo MC, Banovich NE, Choi J. The case for increasing diversity in tissue-based functional genomics datasets to understand human disease susceptibility. Nat Commun 2022; 13:2907. [PMID: 35614099 PMCID: PMC9133089 DOI: 10.1038/s41467-022-30650-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - M Constanza Camargo
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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45
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Zhang C, Verma A, Feng Y, Melo MCR, McQuillan M, Hansen M, Lucas A, Park J, Ranciaro A, Thompson S, Rubel MA, Campbell MC, Beggs W, Hirbo J, Wata Mpoloka S, George Mokone G, Nyambo T, Wolde Meskel D, Belay G, Fokunang C, Njamnshi AK, Omar SA, Williams SM, Rader DJ, Ritchie MD, de la Fuente-Nunez C, Sirugo G, Tishkoff SA. Impact of natural selection on global patterns of genetic variation and association with clinical phenotypes at genes involved in SARS-CoV-2 infection. Proc Natl Acad Sci U S A 2022; 119:e2123000119. [PMID: 35580180 PMCID: PMC9173769 DOI: 10.1073/pnas.2123000119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/29/2022] [Indexed: 01/09/2023] Open
Abstract
Human genomic diversity has been shaped by both ancient and ongoing challenges from viruses. The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on population health. However, genetic diversity and evolutionary forces impacting host genes related to SARS-CoV-2 infection are not well understood. We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (angiotensin converting enzyme 2 [ACE2], transmembrane protease serine 2 [TMPRSS2], dipeptidyl peptidase 4 [DPP4], and lymphocyte antigen 6 complex locus E [LY6E]). We analyzed data from 2,012 ethnically diverse Africans and 15,977 individuals of European and African ancestry with electronic health records and integrated with global data from the 1000 Genomes Project. At ACE2, we identified 41 nonsynonymous variants that were rare in most populations, several of which impact protein function. However, three nonsynonymous variants (rs138390800, rs147311723, and rs145437639) were common among central African hunter-gatherers from Cameroon (minor allele frequency 0.083 to 0.164) and are on haplotypes that exhibit signatures of positive selection. We identify signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage compared with the chimpanzee genome. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19. Our study provides insights into global variation at host genes related to SARS-CoV-2 infection, which have been shaped by natural selection in some populations, possibly due to prior viral infections.
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Affiliation(s)
- Chao Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Yuanqing Feng
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Marcelo C. R. Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael McQuillan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Matthew Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alessia Ranciaro
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Simon Thompson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Meagan A. Rubel
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael C. Campbell
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089
| | - William Beggs
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Jibril Hirbo
- Department of Medicine, Vanderbilt University, Nashville, TN 37232
| | | | | | | | - Thomas Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar es Salaam, Tanzania
| | - Dawit Wolde Meskel
- Department of Microbial Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Department of Microbial Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Charles Fokunang
- Department of Pharmacotoxicology and Pharmacokinetics, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
| | - Alfred K. Njamnshi
- Department of Neurology, Central Hospital Yaoundé, Yaoundé, Cameroon
- Brain Research Africa Initiative, Neuroscience Laboratory, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
| | - Sabah A. Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Scott M. Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106
| | - Daniel J. Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104
| | - Giorgio Sirugo
- Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sarah A. Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA 19104
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46
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Hu X, Qiao D, Kim W, Moll M, Balte PP, Lange LA, Bartz TM, Kumar R, Li X, Yu B, Cade BE, Laurie CA, Sofer T, Ruczinski I, Nickerson DA, Muzny DM, Metcalf GA, Doddapaneni H, Gabriel S, Gupta N, Dugan-Perez S, Cupples LA, Loehr LR, Jain D, Rotter JI, Wilson JG, Psaty BM, Fornage M, Morrison AC, Vasan RS, Washko G, Rich SS, O'Connor GT, Bleecker E, Kaplan RC, Kalhan R, Redline S, Gharib SA, Meyers D, Ortega V, Dupuis J, London SJ, Lappalainen T, Oelsner EC, Silverman EK, Barr RG, Thornton TA, Wheeler HE, Cho MH, Im HK, Manichaikul A. Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program. Am J Hum Genet 2022; 109:857-870. [PMID: 35385699 PMCID: PMC9118106 DOI: 10.1016/j.ajhg.2022.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/04/2022] [Indexed: 12/17/2022] Open
Abstract
While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.
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Affiliation(s)
- Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Pallavi P Balte
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Rajesh Kumar
- Division of Allergy and Clinical Immunology, Ann and Robert H. Lurie Children's Hospital, Chicago, IL 60611, USA; Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xingnan Li
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Donna M Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger A Metcalf
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Stacy Gabriel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shannon Dugan-Perez
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura R Loehr
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA 98101, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ramachandran S Vasan
- Boston University and the National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA; Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA
| | - George Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - George T O'Connor
- Pulmonary Center, Boston University, School of Medicine, Boston, MA 02118, USA
| | - Eugene Bleecker
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Ravi Kalhan
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sina A Gharib
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA 98109, USA
| | - Deborah Meyers
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Victor Ortega
- Pulmonary and Critical Care, School of Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Durham, NC 27709, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Elizabeth C Oelsner
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | | | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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47
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Li Z, Zhao W, Shang L, Mosley TH, Kardia SLR, Smith JA, Zhou X. METRO: Multi-ancestry transcriptome-wide association studies for powerful gene-trait association detection. Am J Hum Genet 2022; 109:783-801. [PMID: 35334221 PMCID: PMC9118130 DOI: 10.1016/j.ajhg.2022.03.003] [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: 10/11/2021] [Accepted: 03/01/2022] [Indexed: 12/23/2022] Open
Abstract
Integrative analysis of genome-wide association studies (GWASs) and gene expression studies in the form of a transcriptome-wide association study (TWAS) has the potential to better elucidate the molecular mechanisms underlying disease etiology. Here we present a method, METRO, that can leverage gene expression data collected from multiple genetic ancestries to enhance TWASs. METRO incorporates expression prediction models constructed in different genetic ancestries through a likelihood-based inference framework, producing calibrated p values with substantially improved TWAS power. We illustrate the benefits of METRO in both simulations and applications to seven complex traits and diseases obtained from four GWASs. These GWASs include two of primarily European ancestry (n = 188,577 and 339,226) and two of primarily African ancestry (n = 42,752 and 23,827). In the real data applications, we leverage gene expression data measured on 1,032 African Americans and 801 European Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study to identify a substantially larger number of gene-trait associations as compared to existing TWAS approaches. The benefits of METRO are most prominent in applications to GWASs of African ancestry where the sample size is much smaller than GWASs of European ancestry and where a more powerful TWAS method is crucial. Among the identified associations are high-density lipoprotein-associated genes including PLTP and PPARG that are critical for maintaining lipid homeostasis and the type II diabetes-associated gene MAPT that supports microtubule-associated protein tau as a key component underlying impaired insulin secretion.
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Affiliation(s)
- Zheng Li
- 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
| | - Lulu Shang
- Department of Biostatistics, 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 39216, 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.
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48
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Barfield R, Huyghe JR, Lemire M, Dong X, Su YR, Brezina S, Buchanan DD, Figueiredo JC, Gallinger S, Giannakis M, Gsur A, Gunter MJ, Hampel H, Harrison TA, Hopper JL, Hudson TJ, Li CI, Moreno V, Newcomb PA, Pai RK, Pharoah PDP, Phipps AI, Qu C, Steinfelder RS, Sun W, Win AK, Zaidi SH, Campbell PT, Peters U, Hsu L. Genetic Regulation of DNA Methylation Yields Novel Discoveries in GWAS of Colorectal Cancer. Cancer Epidemiol Biomarkers Prev 2022; 31:1068-1076. [PMID: 35247911 PMCID: PMC9081265 DOI: 10.1158/1055-9965.epi-21-0724] [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: 06/15/2021] [Revised: 10/05/2021] [Accepted: 02/23/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Colorectal cancer has a strong epigenetic component that is accompanied by frequent DNA methylation (DNAm) alterations in addition to heritable genetic risk. It is of interest to understand the interrelationship of germline genetics, DNAm, and colorectal cancer risk. METHODS We performed a genome-wide methylation quantitative trait locus (meQTL) analysis in 1,355 people, assessing the pairwise associations between genetic variants and lymphocytes methylation data. In addition, we used penalized regression with cis-genetic variants ± 1 Mb of methylation to identify genome-wide heritable DNAm. We evaluated the association of genetically predicted methylation with colorectal cancer risk based on genome-wide association studies (GWAS) of over 125,000 cases and controls using the multivariate sMiST as well as univariately via examination of marginal association with colorectal cancer risk. RESULTS Of the 142 known colorectal cancer GWAS loci, 47 were identified as meQTLs. We identified four novel colorectal cancer-associated loci (NID2, ATXN10, KLHDC10, and CEP41) that reside over 1 Mb outside of known colorectal cancer loci and 10 secondary signals within 1 Mb of known loci. CONCLUSIONS Leveraging information of DNAm regulation into genetic association of colorectal cancer risk reveals novel pathways in colorectal cancer tumorigenesis. Our summary statistics-based framework sMiST provides a powerful approach by combining information from the effect through methylation and residual direct effects of the meQTLs on disease risk. Further validation and functional follow-up of these novel pathways are needed. IMPACT Using genotype, DNAm, and GWAS, we identified four new colorectal cancer risk loci. We studied the landscape of genetic regulation of DNAm via single-SNP and multi-SNP meQTL analyses.
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Affiliation(s)
- Richard Barfield
- Department of Biostatistics and Bioinformatics, Duke University, Durham NC USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mathieu Lemire
- Neurosciences & Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
| | - Xinyuan Dong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Yu-Ru Su
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010 Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria 3010 Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Marios Giannakis
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, Lyon, France
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Thomas J Hudson
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- School of Public Health, University of Washington, Seattle, Washington, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Aung Ko Win
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Syed H Zaidi
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter T Campbell
- Department of Population Science, American Cancer Society, Atlanta, Georgia, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
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49
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Adebamowo CA, Adeyemo A, Ashaye A, Akpa OM, Chikowore T, Choudhury A, Fakim YJ, Fatumo S, Hanchard N, Hauser M, Mitchell B, Mulder N, Ofori-Acquah SF, Owolabi M, Ramsay M, Tayo B, VasanthKumar AB, Zhang Y, Adebamowo SN. Polygenic risk scores for CARDINAL study. Nat Genet 2022; 54:527-530. [PMID: 35513726 PMCID: PMC9907721 DOI: 10.1038/s41588-022-01074-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The Cardiometabolic Disorders in African-Ancestry Populations (CARDINAL) study site is a well-powered, first-of-its-kind resource for developing, refining and validating methods for research into polygenic risk scores that accounts for local ancestry, to improve risk prediction in diverse populations.
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Affiliation(s)
- Clement A Adebamowo
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
- University of Maryland Marlene and Stewart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Yasmina J Fakim
- Department of Biotechnology, University of Mauritius, Reduit, MU, Mauritius
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM (Uganda Research Unit), Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
| | - Neil Hanchard
- National Human Genome Research Institute, Rockville, MD, USA
| | - Michael Hauser
- Department of Medicine, Duke University, Durham, NC, USA
| | - Braxton Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicola Mulder
- Computational Biology Division, IDM, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Solomon F Ofori-Acquah
- West African Genetic Medicine Centre (WAGMC), University of Ghana, Accra, Ghana
- Center for Translational and International Hematology, Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mayowa Owolabi
- Department of Medicine and Center for Genomic and Precision Medicine, University of Ibadan, Ibadan, Nigeria
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Bamidele Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | | | - Yuji Zhang
- University of Maryland Marlene and Stewart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Sally N Adebamowo
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
- University of Maryland Marlene and Stewart Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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50
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Highland HM, Wojcik GL, Graff M, Nishimura KK, Hodonsky CJ, Baldassari AR, Cote AC, Cheng I, Gignoux CR, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff LA, Hu Y, Justice AE, Lin BM, Lin D, Stram DO, Haiman CA, Kooperberg C, Le Marchand L, Matise TC, Kenny EE, Carlson CS, Stahl EA, Avery CL, North KE, Ambite JL, Buyske S, Loos RJ, Peters U, Young KL, Bien SA, Huckins LM. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Am J Hum Genet 2022; 109:669-679. [PMID: 35263625 PMCID: PMC9069067 DOI: 10.1016/j.ajhg.2022.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/15/2022] [Indexed: 02/06/2023] Open
Abstract
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
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Affiliation(s)
- Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Katherine K Nishimura
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Chani J Hodonsky
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Antoine R Baldassari
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Myriam Fornage
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; Brown Foundation Institute for Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Jeffrey Haessler
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lucia A Hindorff
- Division of Genomic Medicine, NIH National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Yao Hu
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA 17822, USA
| | - Bridget M Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Danyu Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Daniel O Stram
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christopher A Haiman
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charles Kooperberg
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | | | - Tara C Matise
- Genetics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christopher S Carlson
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eli A Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Steven Buyske
- Statistics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Ruth J Loos
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Stephanie A Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Laura M Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 14068, USA.
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