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Stone K, Platig J, Quackenbush J, Fagny M. Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582063. [PMID: 38464142 PMCID: PMC10925220 DOI: 10.1101/2024.02.27.582063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.
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Affiliation(s)
- Katherine Stone
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - John Platig
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
- Department of Data Science and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Genetique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette 91190 France
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2
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Betti MJ, Aldrich MC, Gamazon ER. Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.11.544507. [PMID: 37398170 PMCID: PMC10312628 DOI: 10.1101/2023.06.11.544507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BED's interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability (~49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.
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Affiliation(s)
| | | | - Eric R Gamazon
- Vanderbilt University Medical Center, Nashville, TN
- Clare Hall, University of Cambridge, Cambridge, England
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3
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Lin C, Liu W, Jiang W, Zhao H. Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression. Biol Methods Protoc 2023; 8:bpad024. [PMID: 37901453 PMCID: PMC10599978 DOI: 10.1093/biomethods/bpad024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/08/2023] [Accepted: 10/16/2023] [Indexed: 10/31/2023] Open
Abstract
Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.
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Affiliation(s)
- Chen Lin
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
| | - Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
| | - Wei Jiang
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
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4
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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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5
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Huynh K, Smith BR, Macdonald SJ, Long AD. Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster. PLoS Genet 2023; 19:e1010439. [PMID: 37146087 PMCID: PMC10191298 DOI: 10.1371/journal.pgen.1010439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 05/17/2023] [Accepted: 04/20/2023] [Indexed: 05/07/2023] Open
Abstract
We use ATAC-seq to examine chromatin accessibility for four different tissues in Drosophila melanogaster: adult female brain, ovaries, and both wing and eye-antennal imaginal discs from males. Each tissue is assayed in eight different inbred strain genetic backgrounds, seven associated with a reference quality genome assembly. We develop a method for the quantile normalization of ATAC-seq fragments and test for differences in coverage among genotypes, tissues, and their interaction at 44099 peaks throughout the euchromatic genome. For the strains with reference quality genome assemblies, we correct ATAC-seq profiles for read mis-mapping due to nearby polymorphic structural variants (SVs). Comparing coverage among genotypes without accounting for SVs results in a highly elevated rate (55%) of identifying false positive differences in chromatin state between genotypes. After SV correction, we identify 1050, 30383, and 4508 regions whose peak heights are polymorphic among genotypes, among tissues, or exhibit genotype-by-tissue interactions, respectively. Finally, we identify 3988 candidate causative variants that explain at least 80% of the variance in chromatin state at nearby ATAC-seq peaks.
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Affiliation(s)
- Khoi Huynh
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, United States of America
| | - Brittny R. Smith
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, United States of America
| | - Stuart J. Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, United States of America
- Center for Computational Biology, University of Kansas, Lawrence, Kansas, United States of America
| | - Anthony D. Long
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, United States of America
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6
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Zhu Z, Chen X, Wang C, Zhang S, Yu R, Xie Y, Yuan S, Cheng L, Shi L, Zhang X. An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2. J Med Virol 2023; 95:e28585. [PMID: 36794676 DOI: 10.1002/jmv.28585] [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: 10/07/2022] [Revised: 01/15/2023] [Accepted: 01/29/2023] [Indexed: 02/17/2023]
Abstract
Genome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Chao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yubin Xie
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Shuofeng Yuan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- State Key Laboratory of Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lei Shi
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang, China
- 3McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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7
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Tay KY, Wu KX, Chioh FWJ, Autio MI, Pek NMQ, Narmada BC, Tan SH, Low AFH, Lian MM, Chew EGY, Lau HH, Kao SL, Teo AKK, Foo JN, Foo RSY, Heng CK, Chan MYY, Cheung C. Trans-interaction of risk loci 6p24.1 and 10q11.21 is associated with endothelial damage in coronary artery disease. Atherosclerosis 2022; 362:11-22. [PMID: 36435092 DOI: 10.1016/j.atherosclerosis.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIMS Single nucleotide polymorphism rs6903956 has been identified as one of the genetic risk factors for coronary artery disease (CAD). However, rs6903956 lies in a non-coding locus on chromosome 6p24.1. We aim to interrogate the molecular basis of 6p24.1 containing rs6903956 risk alleles in endothelial disease biology. METHODS AND RESULTS We generated induced pluripotent stem cells (iPSCs) from CAD patients (AA risk genotype at rs6903956) and non-CAD subjects (GG non-risk genotype at rs6903956). CRISPR-Cas9-based deletions (Δ63-89bp) on 6p24.1, including both rs6903956 and a short tandem repeat variant rs140361069 in linkage disequilibrium, were performed to generate isogenic iPSC-derived endothelial cells. Edited CAD endothelial cells, with removal of 'A' risk alleles, exhibited a global transcriptional downregulation of pathways relating to abnormal vascular physiology and activated endothelial processes. A CXC chemokine ligand on chromosome 10q11.21, CXCL12, was uncovered as a potential effector gene in CAD endothelial cells. Underlying this effect was the preferential inter-chromosomal interaction of 6p24.1 risk locus to a weak promoter of CXCL12, confirmed by chromatin conformation capture assays on our iPSC-derived endothelial cells. Functionally, risk genotypes AA/AG at rs6903956 were associated significantly with elevated levels of circulating damaged endothelial cells in CAD patients. Circulating endothelial cells isolated from patients with risk genotypes AA/AG were also found to have 10 folds higher CXCL12 transcript copies/cell than those with non-risk genotype GG. CONCLUSIONS Our study reveals the trans-acting impact of 6p24.1 with another CAD locus on 10q11.21 and is associated with intensified endothelial injury.
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Affiliation(s)
- Kai Yi Tay
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore
| | - Kan Xing Wu
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore
| | - Florence Wen Jing Chioh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore
| | - Matias Ilmari Autio
- Genome Institute of Singapore, 60 Biopolis Street, 138672, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Balakrishnan Chakrapani Narmada
- Genome Institute of Singapore, 60 Biopolis Street, 138672, Singapore; Experimental Drug Development Centre, A*STAR, 10 Biopolis Road, Singapore, 138670
| | - Sock-Hwee Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; National University Heart Centre, National University Health System, Singapore
| | - Adrian Fatt-Hoe Low
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; National University Heart Centre, National University Health System, Singapore
| | - Michelle Mulan Lian
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore
| | - Elaine Guo Yan Chew
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore
| | - Hwee Hui Lau
- Institute of Molecular and Cell Biology (IMCB), A*STAR, Proteos, 138673, Singapore; School of Biological Sciences, Nanyang Technological University, 637551, Singapore
| | - Shih Ling Kao
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore; Department of Medicine, National University Hospital and National University Health System, Singapore
| | - Adrian Kee Keong Teo
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Institute of Molecular and Cell Biology (IMCB), A*STAR, Proteos, 138673, Singapore
| | - Jia Nee Foo
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore; Genome Institute of Singapore, 60 Biopolis Street, 138672, Singapore
| | - Roger Sik Yin Foo
- Genome Institute of Singapore, 60 Biopolis Street, 138672, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; National University Heart Centre, National University Health System, Singapore
| | - Chew Kiat Heng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Khoo Teck Puat, National University Children's Medical Institute, National University Health System, Singapore
| | - Mark Yan Yee Chan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; National University Heart Centre, National University Health System, Singapore
| | - Christine Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, 636921, Singapore; Institute of Molecular and Cell Biology (IMCB), A*STAR, Proteos, 138673, Singapore.
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8
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Brown KE, Kelly JK. Genome-wide association mapping of transcriptome variation in Mimulus guttatus indicates differing patterns of selection on cis- versus trans-acting mutations. Genetics 2021; 220:6427634. [PMID: 34791192 DOI: 10.1093/genetics/iyab189] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/28/2021] [Indexed: 11/14/2022] Open
Abstract
We measured the floral bud transcriptome of 151 fully sequenced lines of Mimulus guttatus from one natural population. Thousands of single nucleotide polymorphisms (SNPs) are implicated as transcription regulators, but there is a striking difference in the Allele Frequency Spectrum (AFS) of cis-acting and trans-acting mutations. Cis-SNPs have intermediate frequencies (consistent with balancing selection) while trans-SNPs exhibit a rare-alleles model (consistent with purifying selection). This pattern only becomes clear when transcript variation is normalized on a gene-to-gene basis. If a global normalization is applied, as is typically in RNAseq experiments, asymmetric transcript distributions combined with "rarity disequilibrium" produce a super-abundance of false positives for trans-acting SNPs. To explore the cause of purifying selection on trans-acting mutations, we identified gene expression modules as sets of co-expressed genes. The extent to which trans-acting mutations influence modules is a strong predictor of allele frequency. Mutations altering expression of genes with high "connectedness" (those that are highly predictive of the representative module expression value) have the lowest allele frequency. The expression modules can also predict whole-plant traits such as flower size. We find that a substantial portion of the genetic (co)variance among traits can be described as an emergent property of genetic effects on expression modules.
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Affiliation(s)
- Keely E Brown
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas 66045, USA.,Department of Botany and Plant Sciences, University of California Riverside, Riverside, California 92521, USA
| | - John K Kelly
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas 66045, USA
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Majumdar A, Giambartolomei C, Cai N, Haldar T, Schwarz T, Gandal M, Flint J, Pasaniuc B. Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. PLoS Comput Biol 2021; 17:e1008915. [PMID: 34019542 PMCID: PMC8174686 DOI: 10.1371/journal.pcbi.1008915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 06/03/2021] [Accepted: 03/26/2021] [Indexed: 12/26/2022] Open
Abstract
Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.
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Affiliation(s)
- Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
- Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, Telangana, India
| | - Claudia Giambartolomei
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Na Cai
- Wellcome Sanger Institute, Wellcome genome campus, Hinxton, United Kingdom
- European Bioinformatics Institute (EMBL-EBI), Wellcome genome campus, Hinxton, United Kingdom
| | - Tanushree Haldar
- Institute for Human Genetics, University of California, San Francisco, California, United States of America
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
| | - Michael Gandal
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
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10
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Fan Y, Zhu H, Song Y, Peng Q, Zhou X. Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses. Bioinformatics 2021; 37:296-302. [PMID: 32790868 PMCID: PMC8058772 DOI: 10.1093/bioinformatics/btaa715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 07/09/2020] [Accepted: 08/06/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Identifying cis-acting genetic variants associated with gene expression levels-an analysis commonly referred to as expression quantitative trait loci (eQTLs) mapping-is an important first step toward understanding the genetic determinant of gene expression variation. Successful eQTL mapping requires effective control of confounding factors. A common method for confounding effects control in eQTL mapping studies is the probabilistic estimation of expression residual (PEER) analysis. PEER analysis extracts PEER factors to serve as surrogates for confounding factors, which is further included in the subsequent eQTL mapping analysis. However, it is computationally challenging to determine the optimal number of PEER factors used for eQTL mapping. In particular, the standard approach to determine the optimal number of PEER factors examines one number at a time and chooses a number that optimizes eQTLs discovery. Unfortunately, this standard approach involves multiple repetitive eQTL mapping procedures that are computationally expensive, restricting its use in large-scale eQTL mapping studies that being collected today. RESULTS Here, we present a simple and computationally scalable alternative, Effect size Correlation for COnfounding determination (ECCO), to determine the optimal number of PEER factors used for eQTL mapping studies. Instead of performing repetitive eQTL mapping, ECCO jointly applies differential expression analysis and Mendelian randomization analysis, leading to substantial computational savings. In simulations and real data applications, we show that ECCO identifies a similar number of PEER factors required for eQTL mapping analysis as the standard approach but is two orders of magnitude faster. The computational scalability of ECCO allows for optimized eQTL discovery across 48 GTEx tissues for the first time, yielding an overall 5.89% power gain on the number of eQTL harboring genes (eGenes) discovered as compared to the previous GTEx recommendation that does not attempt to determine tissue-specific optimal number of PEER factors. AVAILABILITYAND IMPLEMENTATION Our method is implemented in the ECCO software, which, along with its GTEx mapping results, is freely available at www.xzlab.org/software.html. All R scripts used in this study are also available at this site. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yue Fan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.,Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huanhuan Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yanyi Song
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Qinke Peng
- Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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11
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Wang X, Su YR, Petersen PS, Bien S, Schmit SL, Drew DA, Albanes D, Berndt SI, Brenner H, Campbell PT, Casey G, Chang-Claude J, Gallinger SJ, Gruber SB, Haile RW, Harrison TA, Hoffmeister M, Jacobs EJ, Jenkins MA, Joshi AD, Li L, Lin Y, Lindor NM, Marchand LL, Martin V, Milne R, Maclnnis R, Moreno V, Nan H, Newcomb PA, Potter JD, Rennert G, Rennert H, Slattery ML, Thibodeau SN, Weinstein SJ, Woods MO, Chan AT, White E, Hsu L, Peters U. Exploratory Genome-Wide Interaction Analysis of Nonsteroidal Anti-inflammatory Drugs and Predicted Gene Expression on Colorectal Cancer Risk. Cancer Epidemiol Biomarkers Prev 2020; 29:1800-1808. [PMID: 32651213 PMCID: PMC7556991 DOI: 10.1158/1055-9965.epi-19-1018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/13/2019] [Accepted: 06/24/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Regular use of nonsteroidal anti-inflammatory drugs (NSAID) is associated with lower risk of colorectal cancer. Genome-wide interaction analysis on single variants (G × E) has identified several SNPs that may interact with NSAIDs to confer colorectal cancer risk, but variations in gene expression levels may also modify the effect of NSAID use. Therefore, we tested interactions between NSAID use and predicted gene expression levels in relation to colorectal cancer risk. METHODS Genetically predicted gene expressions were tested for interaction with NSAID use on colorectal cancer risk among 19,258 colorectal cancer cases and 18,597 controls from 21 observational studies. A Mixed Score Test for Interactions (MiSTi) approach was used to jointly assess G × E effects which are modeled via fixed interaction effects of the weighted burden within each gene set (burden) and residual G × E effects (variance). A false discovery rate (FDR) at 0.2 was applied to correct for multiple testing. RESULTS Among the 4,840 genes tested, genetically predicted expression levels of four genes modified the effect of any NSAID use on colorectal cancer risk, including DPP10 (PG×E = 1.96 × 10-4), KRT16 (PG×E = 2.3 × 10-4), CD14 (PG×E = 9.38 × 10-4), and CYP27A1 (PG×E = 1.44 × 10-3). There was a significant interaction between expression level of RP11-89N17 and regular use of aspirin only on colorectal cancer risk (PG×E = 3.23 × 10-5). No interactions were observed between predicted gene expression and nonaspirin NSAID use at FDR < 0.2. CONCLUSIONS By incorporating functional information, we discovered several novel genes that interacted with NSAID use. IMPACT These findings provide preliminary support that could help understand the chemopreventive mechanisms of NSAIDs on colorectal cancer.
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Affiliation(s)
- Xiaoliang Wang
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Yu-Ru Su
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Paneen S Petersen
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Stephanie Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephanie L Schmit
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - David A Drew
- Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Graham Casey
- Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Center Hamburg, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Steven J Gallinger
- Department of Pathology and Laboratory Medicine, Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Division of General Surgery, Toronto General Hospital, Toronto, Ontario, Canada
| | - Stephen B Gruber
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Robert W Haile
- Department of Health Research and Policy (Epidemiology), Stanford University School of Medicine, Palo Alto, California
- Department of Medicine (Oncology), Stanford Cancer Institute, Palo Alto, California
| | - Tabitha A Harrison
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eric J Jacobs
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Yi Lin
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Noralane M Lindor
- Department of Health Sciences Research, Mayo Clinic, Scottsdale, Arizona
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Vicente Martin
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Biomedicine Institute (IBIOMED), University of León, León, Spain
| | - Roger Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Robert Maclnnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Victor Moreno
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana
- Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana
| | - Polly A Newcomb
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - John D Potter
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
- Centre for Public Health Research, Massey University, Wellington, New Zealand
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Hedy Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Steve N Thibodeau
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, Minnesota
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Michael O Woods
- Discipline of Genetics, Memorial University of Newfoundland, St. John's, Canada
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Emily White
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Li Hsu
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
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12
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Yao DW, O'Connor LJ, Price AL, Gusev A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet 2020; 52:626-633. [PMID: 32424349 PMCID: PMC7276299 DOI: 10.1038/s41588-020-0625-2] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 04/08/2020] [Indexed: 12/23/2022]
Abstract
Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels mediating genetic effects on disease. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of gene expression levels. We applied MESC to GWAS summary statistics for 42 traits (average N = 323K) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11±2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk-tissue eQTLs, though disease relevant, cannot explain the majority of disease heritability.
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Affiliation(s)
- Douglas W Yao
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Luke J O'Connor
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alexander Gusev
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. .,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
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13
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Xia Z, Su Y, Petersen P, Qi L, Kim AE, Figueiredo JC, Lin Y, Nan H, Sakoda LC, Albanes D, Berndt SI, Bézieau S, Bien S, Buchanan DD, Casey G, Chan AT, Conti DV, Drew DA, Gallinger SJ, Gauderman WJ, Giles GG, Gruber SB, Gunter MJ, Hoffmeister M, Jenkins MA, Joshi AD, Le Marchand L, Lewinger JP, Li L, Lindor NM, Moreno V, Murphy N, Nassir R, Newcomb PA, Ogino S, Rennert G, Song M, Wang X, Wolk A, Woods MO, Brenner H, White E, Slattery ML, Giovannucci EL, Chang‐Claude J, Pharoah PDP, Hsu L, Campbell PT, Peters U. Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk. Cancer Med 2020; 9:3563-3573. [PMID: 32207560 PMCID: PMC7221445 DOI: 10.1002/cam4.2971] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/28/2020] [Accepted: 02/21/2020] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. METHODS To improve statistical power and interpretation for gene-environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2 ) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype-Tissue Expression Project for all genes with heritability ≥1%. We used a mixed-effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI-CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2. RESULTS Among 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10-5 ), PSMC5 (P = 4.51 × 10-4 ) and CD33 (P = 2.71 × 10-4 ) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10-5 ) and SCN1B (P = 2.76 × 10-4 ) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10-5 ). CONCLUSIONS Aggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.
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14
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Shang L, Smith JA, Zhao W, Kho M, Turner ST, Mosley TH, Kardia SL, Zhou X. Genetic Architecture of Gene Expression in European and African Americans: An eQTL Mapping Study in GENOA. Am J Hum Genet 2020; 106:496-512. [PMID: 32220292 DOI: 10.1016/j.ajhg.2020.03.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 03/06/2020] [Indexed: 12/20/2022] Open
Abstract
Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. Lack of large-scale well-powered eQTL mapping studies in populations with African ancestry can both impede the dissemination of eQTL mapping results that would otherwise benefit individuals with African ancestry and hinder the comparable analysis for understanding how gene regulation is shaped through evolution. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort. We identified a total of 354,931 eSNPs in AA and 371,309 eSNPs in EA, with 112,316 eSNPs overlapped between the two. We found that eQTL harboring genes (eGenes) are enriched in metabolic pathways and tend to have higher SNP heritability compared to non-eGenes. We found that eGenes that are common in the two populations tend to be less conserved than eGenes that are unique to one population, which are less conserved than non-eGenes. Through conditional analysis, we found that eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse genetic architecture underlying expression variation in the two populations. Finally, the large sample sizes in GENOA allow us to construct accurate expression prediction models in both AA and EA, facilitating powerful transcriptome-wide association studies. Overall, our results represent an important step toward revealing the genetic architecture underlying expression variation in African Americans.
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15
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Ndungu A, Payne A, Torres JM, van de Bunt M, McCarthy MI. A Multi-tissue Transcriptome Analysis of Human Metabolites Guides Interpretability of Associations Based on Multi-SNP Models for Gene Expression. Am J Hum Genet 2020; 106:188-201. [PMID: 31978332 PMCID: PMC7010967 DOI: 10.1016/j.ajhg.2020.01.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 01/06/2020] [Indexed: 12/16/2022] Open
Abstract
There is particular interest in transcriptome-wide association studies (TWAS) gene-level tests based on multi-SNP predictive models of gene expression—for identifying causal genes at loci associated with complex traits. However, interpretation of TWAS associations may be complicated by divergent effects of model SNPs on phenotype and gene expression. We developed an iterative modeling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissue Expression (GTEx) Project. We characterized the performance of single- and multi-SNP models for identifying causal genes in GWAS data for 46 circulating metabolites. We show that: (A) multi-SNP models captured more variation in expression than did the top cis-eQTL (median 2-fold improvement); (B) predicted expression based on multi-SNP models was associated (false discovery rate < 0.01) with metabolite levels for 826 unique gene-metabolite pairs, but, after stepwise conditional analyses, 90% were dominated by a single eQTL SNP; (C) among the 35% of associations where a SNP in the expression model was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between these signals, but interpretation was often complicated by incomplete overlap of QTLs in multi-SNP models; and (D) using a “truth” set of causal genes at 61 met-QTLs, the sensitivity was high (67%), but the positive predictive value was low, as only 8% of TWAS associations (19% when restricted to colocalized associations at met-QTLs) involved true causal genes. These results guide the interpretation of TWAS and highlight the need for corroborative data to provide confident assignment of causality.
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Affiliation(s)
- Anne Ndungu
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Anthony Payne
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Jason M Torres
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Martijn van de Bunt
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK; Department of Bioinformatics and Data Mining, Novo Nordisk A/S, Måløv, 2760, DK
| | - Mark I McCarthy
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK.
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16
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Koutrouli M, Karatzas E, Paez-Espino D, Pavlopoulos GA. A Guide to Conquer the Biological Network Era Using Graph Theory. Front Bioeng Biotechnol 2020; 8:34. [PMID: 32083072 PMCID: PMC7004966 DOI: 10.3389/fbioe.2020.00034] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/15/2020] [Indexed: 12/24/2022] Open
Abstract
Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.
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Affiliation(s)
- Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.,Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, Department of Energy, Joint Genome Institute, Walnut Creek, CA, United States
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17
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Zou J, Hormozdiari F, Jew B, Castel SE, Lappalainen T, Ernst J, Sul JH, Eskin E. Leveraging allelic imbalance to refine fine-mapping for eQTL studies. PLoS Genet 2019; 15:e1008481. [PMID: 31834882 PMCID: PMC6952111 DOI: 10.1371/journal.pgen.1008481] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 01/09/2020] [Accepted: 10/15/2019] [Indexed: 11/18/2022] Open
Abstract
Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity. In recent years, many studies have identified genetic variants that are associated with the expression of genes (eQTLs). While thousands of eQTLs have been identified, not all associated variants cause changes in gene expression. This is in part due to the complex patterns of genetic correlation in the human genome. If a region of the genome contains many genetic variants that are highly correlated with each other, non-causal genetic variants close to a causal variant are also correlated with gene expression. Statistical fine-mapping is the process of identifying true causal variants from a set of candidate variants. In regions with high genetic correlation, previous fine-mapping methods may not be able to differentiate causal variants from nearby variants. We propose a method that utilizes a complementary source of information called allelic imbalance (AIM). We show that by combining eQTL and AIM data, we can identify the true causal variants more efficiently and substantially decrease the number of putative causal variants for downstream analysis.
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Affiliation(s)
- Jennifer Zou
- Computer Science Department, University of California Los Angeles, Los Angeles, California, United States of America
| | - Farhad Hormozdiari
- Genetic Epidemiology and Statistical Genetics Program, Harvard University, Cambridge, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Brandon Jew
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America
| | - Stephane E. Castel
- New York Genome Center, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Tuuli Lappalainen
- New York Genome Center, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Jason Ernst
- Computer Science Department, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jae Hoon Sul
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (JHS); (EE)
| | - Eleazar Eskin
- Computer Science Department, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (JHS); (EE)
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18
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Xu T, Jin P, Qin ZS. Regulatory annotation of genomic intervals based on tissue-specific expression QTLs. Bioinformatics 2019; 36:690-697. [PMID: 31504167 PMCID: PMC8215915 DOI: 10.1093/bioinformatics/btz669] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 05/14/2019] [Accepted: 08/23/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Annotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, the same idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections. RESULTS In this study, we describe a novel computational strategy named loci2path that takes advantage of the newly emerged, genome-wide and tissue-specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic intervals in terms of transcription regulation. By checking the presence or the absence of millions of eQTLs in a set of input genomic intervals, combined with grouping eQTLs by the pathways or gene sets that their target genes belong to, loci2path build a bridge connecting genomic intervals to functional pathways and pre-defined biological-meaningful gene sets, revealing potential for regulatory connection. Our method enjoys two key advantages over existing methods: first, we no longer rely on proximity to link a locus to a gene which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation under the context of specific tissue types. To demonstrate its utilities, we apply loci2path on sets of genomic intervals harboring disease-associated variants as query. Using 1 702 612 eQTLs discovered by the Genotype-Tissue Expression (GTEx) project across 44 tissues and 6320 pathways or gene sets cataloged in MSigDB as annotation resource, our method successfully identifies highly relevant biological pathways and revealed disease mechanisms for psoriasis and other immune-related diseases. Tissue specificity analysis of associated eQTLs provide additional evidence of the distinct roles of different tissues played in the disease mechanisms. AVAILABILITY AND IMPLEMENTATION loci2path is published as an open source Bioconductor package, and it is available at http://bioconductor.org/packages/release/bioc/html/loci2path.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tianlei Xu
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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19
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Petty LE, Highland HM, Gamazon ER, Hu H, Karhade M, Chen HH, de Vries PS, Grove ML, Aguilar D, Bell GI, Huff CD, Hanis CL, Doddapaneni H, Munzy DM, Gibbs RA, Ma J, Parra EJ, Cruz M, Valladares-Salgado A, Arking DE, Barbeira A, Im HK, Morrison AC, Boerwinkle E, Below JE. Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample. Hum Mol Genet 2019; 28:1212-1224. [PMID: 30624610 PMCID: PMC6423424 DOI: 10.1093/hmg/ddy435] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 11/13/2018] [Accepted: 11/20/2018] [Indexed: 01/02/2023] Open
Abstract
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
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Affiliation(s)
- Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Heather M Highland
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Clare Hall, University of Cambridge, Cambridge, UK
| | - Hao Hu
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Mandar Karhade
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Megan L Grove
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - David Aguilar
- Department of Cardiology, Baylor College of Medicine Houston, TX, USA
| | - Graeme I Bell
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Chad D Huff
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Craig L Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Donna M Munzy
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jianzhong Ma
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alvaro Barbeira
- Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, IL, USA
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
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20
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Xiang D, Zhao SD, Tony Cai T. Signal classification for the integrative analysis of multiple sequences of large-scale multiple tests. J R Stat Soc Series B Stat Methodol 2019. [DOI: 10.1111/rssb.12323] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Dongdong Xiang
- East China Normal University; Shanghai People's Republic of China
| | | | - T. Tony Cai
- University of Pennsylvania; Philadelphia USA
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21
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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22
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Mikhaylova AV, Thornton TA. Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations. Front Genet 2019; 10:261. [PMID: 31001318 PMCID: PMC6456650 DOI: 10.3389/fgene.2019.00261] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 03/08/2019] [Indexed: 01/08/2023] Open
Abstract
Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10-16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10-16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases.
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Affiliation(s)
- Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, United States
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23
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Bien SA, Su YR, Conti DV, Harrison TA, Qu C, Guo X, Lu Y, Albanes D, Auer PL, Banbury BL, Berndt SI, Bézieau S, Brenner H, Buchanan DD, Caan BJ, Campbell PT, Carlson CS, Chan AT, Chang-Claude J, Chen S, Connolly CM, Easton DF, Feskens EJM, Gallinger S, Giles GG, Gunter MJ, Hampe J, Huyghe JR, Hoffmeister M, Hudson TJ, Jacobs EJ, Jenkins MA, Kampman E, Kang HM, Kühn T, Küry S, Lejbkowicz F, Le Marchand L, Milne RL, Li L, Li CI, Lindblom A, Lindor NM, Martín V, McNeil CE, Melas M, Moreno V, Newcomb PA, Offit K, Pharaoh PDP, Potter JD, Qu C, Riboli E, Rennert G, Sala N, Schafmayer C, Scacheri PC, Schmit SL, Severi G, Slattery ML, Smith JD, Trichopoulou A, Tumino R, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Weinstein SJ, White E, Wolk A, Woods MO, Wu AH, Abecasis GR, Casey G, Nickerson DA, Gruber SB, Hsu L, Zheng W, Peters U. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet 2019; 138:307-326. [PMID: 30820706 PMCID: PMC6483948 DOI: 10.1007/s00439-019-01989-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/20/2019] [Indexed: 02/02/2023]
Abstract
Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.
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Affiliation(s)
- Stephanie A Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
| | - Yu-Ru Su
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - David V Conti
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Tabitha A Harrison
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Conghui Qu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Xingyi Guo
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Yingchang Lu
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Paul L Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, 53205, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Barbara L Banbury
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stéphane Bézieau
- Centre Hospitalier Universitaire Hotel-Dieu, 44093, Nantes, France
- Service de Génétique Médiczle, Centre Hospitalier Universitaire (CHU), 44093, Nantes, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120, Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Daniel D Buchanan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Colorectal Oncogenomics Group, Department of Pathology, University of Melbourne, Melbourne, VIC, 3010, Australia
- Genetic Medicine and Familial Cancer Centre, The Royal Melbourne Hospital, Parkville, VIC, 3010, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Bette J Caan
- Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA, 94612, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Peter T Campbell
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, 30329-4251, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Christopher S Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Genetic Tumour Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, 20246, Hamburg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sai Chen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Charles M Connolly
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Douglas F Easton
- Department of Public Health and Primary Care School of Clinical Medicine, University of Cambridge, Cambridge, England, 01223, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Edith J M Feskens
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, 1X5, Canada
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, 3004, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Marc J Gunter
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, TU Dresden, 01307, Dresden, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jeroen R Huyghe
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Thomas J Hudson
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- AbbVie Inc, 1500 Seaport Blvd, Redwood City, CA, 94063, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Eric J Jacobs
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, 30329-4251, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Ellen Kampman
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sébastien Küry
- Centre Hospitalier Universitaire Hotel-Dieu, 44093, Nantes, France
- Service de Génétique Médiczle, Centre Hospitalier Universitaire (CHU), 44093, Nantes, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Flavio Lejbkowicz
- Clalit Health Services National Israeli Cancer Control Center, 34361, Haifa, Israel
- Department of Community Medicine and Epidemiology, Carmel Medical Center, 34361, Haifa, Israel
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Loic Le Marchand
- University of Hawai'i Cancer Center, Honolulu, Hawaii, 96813, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, 3004, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Li Li
- Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital Solna, 171 77, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet Solna, 171 77, Stockholm, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Noralane M Lindor
- Department of Health Science Research, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Vicente Martín
- Biomedicine Institute (IBIOMED), University of León, León, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Caroline E McNeil
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Marilena Melas
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Victor Moreno
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08028, Barcelona, Spain
- University of Barcelona, 08007, Barcelona, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Polly A Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Kenneth Offit
- Department of Medicine, Clinical Genetics Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Paul D P Pharaoh
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, CB2 1TN, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - John D Potter
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Chenxu Qu
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Elio Riboli
- School of Public Health, Imperial College London, London, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Gad Rennert
- Clalit Health Services National Israeli Cancer Control Center, 34361, Haifa, Israel
- Department of Community Medicine and Epidemiology, Carmel Medical Center, 34361, Haifa, Israel
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Núria Sala
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Molecular Epidemiology Group, Translational Research Laboratory, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Clemens Schafmayer
- Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, 24118, Kiel, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephanie L Schmit
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Inc, Tampa, FL, 33612, USA
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Inc, Tampa, FL, 33612, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Gianluca Severi
- Centre for Research in Epidemiology and Population Health, Institut de Cancérologie Gustave Roussy, Villejuif, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Joshua D Smith
- Department Genome Sciences, University of Washington, 98195, Seattle, WA, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Antonia Trichopoulou
- Hellenic Health Foundation, 13 Kaisareias & Alexandroupoleos, 115 27, Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, 115 27, Athens, Greece
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Rosario Tumino
- Affiliation Cancer Registry, Department of Prevention, Azienda Sanitaria Provinciale di Ragusa, Ragusa, Italy
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Cornelia M Ulrich
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, 84112, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Fränzel J B van Duijnhoven
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Bethany Van Guelpen
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet Solna, 17177, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 75121, Uppsala, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Michael O Woods
- Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, Saint John's, NL, A1B 3V6, Canada
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Anna H Wu
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Goncalo R Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Graham Casey
- Centre for Research in Epidemiology and Population Health, Institut de Cancérologie Gustave Roussy, Villejuif, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Deborah A Nickerson
- Department Genome Sciences, University of Washington, 98195, Seattle, WA, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephen B Gruber
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37232, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
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24
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Lamontagne M, Bérubé JC, Obeidat M, Cho MH, Hobbs BD, Sakornsakolpat P, de Jong K, Boezen HM, Nickle D, Hao K, Timens W, van den Berge M, Joubert P, Laviolette M, Sin DD, Paré PD, Bossé Y. Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations. Hum Mol Genet 2019; 27:1819-1829. [PMID: 29547942 DOI: 10.1093/hmg/ddy091] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 03/09/2018] [Indexed: 12/20/2022] Open
Abstract
Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.
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Affiliation(s)
- Maxime Lamontagne
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada
| | - Jean-Christophe Bérubé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada
| | - Ma'en Obeidat
- The University of British Columbia Centre for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Phuwanat Sakornsakolpat
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Kim de Jong
- Department of Epidemiology, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| | - H Marike Boezen
- Department of Epidemiology, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| | | | - David Nickle
- Merck Research Laboratories (MRL), Seattle, WA, USA
| | - Ke Hao
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wim Timens
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Maarten van den Berge
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada.,Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC, Canada
| | - Michel Laviolette
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada
| | - Don D Sin
- The University of British Columbia Centre for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada.,Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Peter D Paré
- The University of British Columbia Centre for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada.,Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada.,Department of Molecular Medicine, Laval University, Quebec City, QC, Canada
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25
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Ip HF, Jansen R, Abdellaoui A, Bartels M, Boomsma DI, Nivard MG. Characterizing the Relation Between Expression QTLs and Complex Traits: Exploring the Role of Tissue Specificity. Behav Genet 2018; 48:374-385. [PMID: 30030655 PMCID: PMC6097736 DOI: 10.1007/s10519-018-9914-2] [Citation(s) in RCA: 12] [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: 02/22/2017] [Accepted: 07/04/2018] [Indexed: 01/14/2023]
Abstract
Measurement of gene expression levels and detection of eQTLs (expression quantitative trait loci) are difficult in tissues with limited sample availability, such as the brain. However, eQTL overlap between tissues might be high, which would allow for inference of eQTL functioning in the brain via eQTLs detected in readily accessible tissues, e.g. whole blood. Applying Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantified the enrichment in polygenic signal of blood and brain eQTLs in genome-wide association studies (GWAS) of 11 complex traits. We looked at eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium and two other large representative studies, and found no tissue-specific eQTL effects. Next, we integrated the GTEx eQTLs with regions associated with tissue-specific histone modifiers, and interrogated their effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects of eQTLs located inside regions bearing modification H3K4me1 on schizophrenia, but not rheumatoid arthritis, and not tissue-specific. Finally, we extracted eQTLs associated with tissue-specific differentially expressed genes and determined their effects on rheumatoid arthritis and schizophrenia, these analysis revealed limited enrichment of eQTLs associated with gene specifically expressed in specific tissues. Our results pointed to strong enrichment of eQTLs in their effect on complex traits, without evidence for tissue-specific effects. Lack of tissue-specificity can be either due to a lack of statistical power or due to the true absence of tissue-specific effects. We conclude that eQTLs are strongly enriched in GWAS signal and that the enrichment is not specific to the eQTL discovery tissue. Until sample sizes for eQTL discovery grow sufficiently large, working with relatively accessible tissues as proxy for eQTL discovery is sensible and restricting lookups for GWAS hits to a specific tissue for which limited samples are available might not be advisable.
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Affiliation(s)
- Hill F Ip
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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26
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Liu J, Chen J, Perrone-Bizzozero N, Calhoun VD. A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia. Front Genet 2018; 9:343. [PMID: 30190726 PMCID: PMC6115489 DOI: 10.3389/fgene.2018.00343] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 08/09/2018] [Indexed: 12/11/2022] Open
Abstract
Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.
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Affiliation(s)
- Jingyu Liu
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, United States
| | - Jiayu Chen
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
| | - Nora Perrone-Bizzozero
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Vince D. Calhoun
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, United States
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27
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Mogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, Johnson WC, Im HK, Liu Y, Wheeler HE. Genetic architecture of gene expression traits across diverse populations. PLoS Genet 2018; 14:e1007586. [PMID: 30096133 PMCID: PMC6105030 DOI: 10.1371/journal.pgen.1007586] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 08/22/2018] [Accepted: 07/24/2018] [Indexed: 01/14/2023] Open
Abstract
For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.
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Affiliation(s)
- Lauren S. Mogil
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Angela Andaleon
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Alexa Badalamenti
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Scott P. Dickinson
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Yongmei Liu
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
- Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America
- Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
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28
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Dziewulska A, Dobosz AM, Dobrzyn A. High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes. Genes (Basel) 2018; 9:E374. [PMID: 30050001 PMCID: PMC6115814 DOI: 10.3390/genes9080374] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 07/20/2018] [Accepted: 07/23/2018] [Indexed: 12/20/2022] Open
Abstract
Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.
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Affiliation(s)
- Anna Dziewulska
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
| | - Aneta M Dobosz
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
| | - Agnieszka Dobrzyn
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
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29
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Hormozdiari F, Gazal S, van de Geijn B, Finucane HK, Ju CJT, Loh PR, Schoech A, Reshef Y, Liu X, O'Connor L, Gusev A, Eskin E, Price AL. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 2018; 50:1041-1047. [PMID: 29942083 PMCID: PMC6030458 DOI: 10.1038/s41588-018-0148-2] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 04/27/2018] [Indexed: 12/20/2022]
Abstract
There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.
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Affiliation(s)
- Farhad Hormozdiari
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hilary K Finucane
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chelsea J-T Ju
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Armin Schoech
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xuanyao Liu
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luke O'Connor
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
| | - Alexander Gusev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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30
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Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, Zhu Z, Kemper K, Yengo L, Zheng Z, Marioni RE, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun 2018; 9:2282. [PMID: 29891976 PMCID: PMC5995828 DOI: 10.1038/s41467-018-04558-1] [Citation(s) in RCA: 212] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 05/10/2018] [Indexed: 01/01/2023] Open
Abstract
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ([Formula: see text] for cis-eQTLs and [Formula: see text] for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.
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Affiliation(s)
- Ting Qi
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Kathryn Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China
| | | | - Riccardo E Marioni
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian J Deary
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia. .,The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China.
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31
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Ying D, Li MJ, Sham PC, Li M. A powerful approach reveals numerous expression quantitative trait haplotypes in multiple tissues. Bioinformatics 2018; 34:3145-3150. [DOI: 10.1093/bioinformatics/bty318] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 04/25/2018] [Indexed: 12/21/2022] Open
Affiliation(s)
- Dingge Ying
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Mulin Jun Li
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Pak Chung Sham
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Miaoxin Li
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
- Zhongshan School of Medicine, Center for Disease Genomics, Sun Yat-Sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China
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32
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Yang F, Wang J, Pierce BL, Chen LS. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res 2017; 27:1859-1871. [PMID: 29021290 PMCID: PMC5668943 DOI: 10.1101/gr.216754.116] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 05/01/2017] [Indexed: 11/25/2022]
Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.
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Affiliation(s)
- Fan Yang
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jiebiao Wang
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
- Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois 60637, USA
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, USA
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33
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Fagny M, Paulson JN, Kuijjer ML, Sonawane AR, Chen CY, Lopes-Ramos CM, Glass K, Quackenbush J, Platig J. Exploring regulation in tissues with eQTL networks. Proc Natl Acad Sci U S A 2017; 114:E7841-E7850. [PMID: 28851834 PMCID: PMC5604022 DOI: 10.1073/pnas.1707375114] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.
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Affiliation(s)
- Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Joseph N Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Marieke L Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School Boston, MA 02115
| | - Cho-Yi Chen
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Camila M Lopes-Ramos
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School Boston, MA 02115
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115;
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
| | - John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115;
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115
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34
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Highfill CA, Tran JH, Nguyen SKT, Moldenhauer TR, Wang X, Macdonald SJ. Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster. Genetics 2017; 207:311-325. [PMID: 28743761 PMCID: PMC5586381 DOI: 10.1534/genetics.117.300058] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 07/24/2017] [Indexed: 12/16/2022] Open
Abstract
Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.
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Affiliation(s)
- Chad A Highfill
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Jonathan H Tran
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Samantha K T Nguyen
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Taylor R Moldenhauer
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Xiaofei Wang
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
- Center for Computational Biology, University of Kansas, Lawrence, Kansas 66047
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35
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Song X, Li G, Zhou Z, Wang X, Ionita-Laza I, Wei Y. QRank: a novel quantile regression tool for eQTL discovery. Bioinformatics 2017; 33:2123-2130. [PMID: 28334222 PMCID: PMC5870877 DOI: 10.1093/bioinformatics/btx119] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 02/14/2017] [Accepted: 03/09/2017] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION Over the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Motivated by the potential higher order associations, several studies investigated variance eQTLs. RESULTS In this paper, we develop a Quantile Rank-score based test (QRank), which provides an easy way to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRank to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRank complements the existing methods, and identifies new eQTLs with heterogeneous effects across different quantile levels. Notably, we show that the eQTLs identified by QRank but missed by linear regression are associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog, and are also more likely to be tissue specific than eQTLs identified by linear regression. AVAILABILITY AND IMPLEMENTATION An R package is available on R CRAN at https://cran.r-project.org/web/packages/QRank . CONTACT xs2148@cumc.columbia.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaoyu Song
- Heilbrunn Department of Population & Family Health, Columbia University, New York, NY, USA
| | - Gen Li
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Zhenwei Zhou
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Xianling Wang
- Department of Biostatistics, Columbia University, New York, NY, USA
| | | | - Ying Wei
- Department of Biostatistics, Columbia University, New York, NY, USA
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36
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Saleheen D, Zhao W, Young R, Nelson CP, Ho W, Ferguson JF, Rasheed A, Ou K, Nurnberg ST, Bauer RC, Goel A, Do R, Stewart AF, Hartiala J, Zhang W, Thorleifsson G, Strawbridge RJ, Sinisalo J, Kanoni S, Sedaghat S, Marouli E, Kristiansson K, Zhao JH, Scott R, Gauguier D, Shah SH, Smith AV, van Zuydam N, Cox AJ, Willenborg C, Kessler T, Zeng L, Province MA, Ganna A, Lind L, Pedersen NL, White CC, Joensuu A, Kleber ME, Hall AS, März W, Salomaa V, O’Donnell C, Ingelsson E, Feitosa MF, Erdmann J, Bowden DW, Palmer CN, Gudnason V, De Faire U, Zalloua P, Wareham N, Thompson JR, Kuulasmaa K, Dedoussis G, Perola M, Dehghan A, Chambers JC, Kooner J, Allayee H, Deloukas P, McPherson R, Stefansson K, Schunkert H, Kathiresan S, Farrall M, Frossard PM, Rader DJ, Samani NJ, Reilly MP. Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions. Circulation 2017; 135:2336-2353. [PMID: 28461624 PMCID: PMC5612779 DOI: 10.1161/circulationaha.116.022069] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 03/21/2017] [Indexed: 01/14/2023]
Abstract
BACKGROUND Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0×10-3 (Bonferroni correction for 50 tests). RESULTS We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P=1.3×10-16) in comparison with 5% in ever-smokers (P=2.5×10-4), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value=8.7×10-5). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.
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Affiliation(s)
- Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Wei Zhao
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA
| | - Robin Young
- Department of Public Health and Primary Care, University of Cambridge, United Kingdom
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - WeangKee Ho
- Department of Public Health and Primary Care, University of Cambridge, United Kingdom
| | - Jane F. Ferguson
- Cardiology Division, Department of Medicine, Vanderbilt University, Nashville, TN
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Kristy Ou
- Cardiology Division, Department of Medicine, Vanderbilt University, Nashville, TN
| | - Sylvia T. Nurnberg
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Robert C. Bauer
- Cardiology Division, Department of Medicine and the Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine & Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexandre F.R. Stewart
- Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Jaana Hartiala
- Institute for Genetic Medicine and Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital NHS Trust, Middlesex, United Kingdom
| | - Gudmar Thorleifsson
- deCODE Genetics, Sturlugata 8, IS-101 Reykjavik, Iceland
- University of Iceland, School of Medicine, Reykjavik, Iceland
| | - Rona J Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Eirini Marouli
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Department of Dietetics-Nutrition, Harokopio University, 70 El. VenizelouStr, Athens, Greece
| | | | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Robert Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | | | - Svati H. Shah
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC
| | - Albert Vernon Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Natalie van Zuydam
- Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
| | - Amanda J. Cox
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
| | - Christina Willenborg
- Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, Lübeck, Germany
- DZHK (German Research Center for Cardiovascular Research) partner site Hamburg–Lübeck–Kiel, Lübeck, Germany
| | - Thorsten Kessler
- Deutsches Herzzentrum München, Technische Universität München, München, Germany
- Klinikum rechts der Isar, München, Germany
| | - Lingyao Zeng
- Deutsches Herzzentrum München, Technische Universität München, München, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, München, Germany
| | - Michael A. Province
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Andrea Ganna
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Charles C. White
- Department of Biostatistics Boston University School of Public Health Framingham Heart Study, Boston, MA
| | - Anni Joensuu
- National Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki, Institute for Molecular Medicine, Finland (FIMM)
| | - Marcus Edi Kleber
- Department of Medicine, Mannheim Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Alistair S. Hall
- Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, United Kingdom
| | - Winfried März
- Synlab Academy, Synlab Services GmbH, Mannheim, Germany and Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Christopher O’Donnell
- National Heart, Lung, and Blood Institute and the Framingham Heart Study, National Institutes of Health, Bethesda, MD
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
| | - Mary F. Feitosa
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Jeanette Erdmann
- Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, Lübeck, Germany
- DZHK (German Research Center for Cardiovascular Research) partner site Hamburg–Lübeck–Kiel, Lübeck, Germany
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
| | - Colin N.A. Palmer
- Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ulf De Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Pierre Zalloua
- Lebanese American University, School of Medicine, Beirut, Lebanon
| | - Nicholas Wareham
- INSERM, UMRS1138, Centre de Recherche des Cordeliers, Paris, France
| | - John R. Thompson
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
| | - Kari Kuulasmaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - George Dedoussis
- Department of Dietetics-Nutrition, Harokopio University, 70 El. VenizelouStr, Athens, Greece
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki, Institute for Molecular Medicine, Finland (FIMM)
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital NHS Trust, Middlesex, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Jaspal Kooner
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Cardiovascular Science, National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Hooman Allayee
- Institute for Genetic Medicine and Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ruth McPherson
- Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Kari Stefansson
- deCODE Genetics, Sturlugata 8, IS-101 Reykjavik, Iceland
- University of Iceland, School of Medicine, Reykjavik, Iceland
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Technische Universität München, München, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, München, Germany
| | - Sekar Kathiresan
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine & Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - EPIC-CVD
- Department of Public Health and Primary Care, University of Cambridge, United Kingdom
| | | | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - PROMIS
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | | | - Muredach P. Reilly
- Cardiology Division, Department of Medicine and the Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
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Liu X, Finucane HK, Gusev A, Bhatia G, Gazal S, O’Connor L, Bulik-Sullivan B, Wright FA, Sullivan PF, Neale BM, Price AL. Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues. Am J Hum Genet 2017; 100:605-616. [PMID: 28343628 DOI: 10.1016/j.ajhg.2017.03.002] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 02/24/2017] [Indexed: 12/12/2022] Open
Abstract
Genetic variants that modulate gene expression levels play an important role in the etiology of human diseases and complex traits. Although large-scale eQTL mapping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variants regulate expression remain unclear, particularly for distal eQTLs, which these studies are not well powered to detect. Here, we leveraged all variants (not just those that pass stringent significance thresholds) to analyze the functional architecture of local and distal regulation of gene expression in 15 human tissues by employing an extension of stratified LD-score regression that produces robust results in simulations. The top enriched functional categories in local regulation of peripheral-blood gene expression included coding regions (11.41×), conserved regions (4.67×), and four histone marks (p < 5 × 10-5 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral-blood gene expression: coding regions (4.47×), conserved regions (4.51×), and two histone marks (p < 3 × 10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues confirmed that local regulation of gene expression is largely shared across tissues but that distal regulation is highly tissue specific. Our results elucidate the functional components of the genetic architecture of local and distal regulation of gene expression.
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38
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Zeng Y, Navarro P, Fernandez-Pujals AM, Hall LS, Clarke TK, Thomson PA, Smith BH, Hocking LJ, Padmanabhan S, Hayward C, MacIntyre DJ, Wray NR, Deary IJ, Porteous DJ, Haley CS, McIntosh AM. A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway Is Associated With Major Depressive Disorder. Biol Psychiatry 2017; 81:336-346. [PMID: 27422368 PMCID: PMC5262437 DOI: 10.1016/j.biopsych.2016.04.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Revised: 04/20/2016] [Accepted: 04/21/2016] [Indexed: 01/14/2023]
Abstract
BACKGROUND Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants, in particular biological pathways, may be more powerful. Regional heritability analysis can be used to detect genomic regions that contribute to disease risk. METHODS We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. RESULTS In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model. CONCLUSIONS These post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies.
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Affiliation(s)
- Yanni Zeng
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
| | - Pau Navarro
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Lynsey S Hall
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Pippa A Thomson
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Medical Genetics Section, University of Edinburgh, Edinburgh, United Kingdom
| | - Blair H Smith
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom; Division of Population Health Sciences, University of Dundee, Dundee, United Kingdom
| | - Lynne J Hocking
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom; Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Sandosh Padmanabhan
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom; Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Donald J MacIntyre
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Naomi R Wray
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Generation Scotland, University of Edinburgh, Edinburgh, United Kingdom; Institute of Genetics and Molecular Medicine, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Medical Genetics Section, University of Edinburgh, Edinburgh, United Kingdom; Generation Scotland, University of Edinburgh, Edinburgh, United Kingdom
| | - Chris S Haley
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom; The Roslin Institute and Royal (Dick) School of Veterinary Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Generation Scotland, University of Edinburgh, Edinburgh, United Kingdom
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Abstract
Genome-wide association studies (GWAS) of asthma have yielded exciting results and identified novel risk alleles and loci. But, like other common complex diseases, asthma-associated alleles have small effect sizes and account for little of the prevalence of asthma. In this review, I discuss the limitations of GWAS approaches and the major challenges facing geneticists in the post-GWAS era and propose alternative strategies to address these challenges. In particular, I propose that focusing on genetic variations that influences gene expression and using cell models of gene-environment interactions in cell types that are relevant to asthma will allow us to more completely characterize the genetic architecture of asthma.
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40
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Deplancke B, Alpern D, Gardeux V. The Genetics of Transcription Factor DNA Binding Variation. Cell 2016; 166:538-554. [PMID: 27471964 DOI: 10.1016/j.cell.2016.07.012] [Citation(s) in RCA: 244] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/23/2022]
Abstract
Most complex trait-associated variants are located in non-coding regulatory regions of the genome, where they have been shown to disrupt transcription factor (TF)-DNA binding motifs. Variable TF-DNA interactions are therefore increasingly considered as key drivers of phenotypic variation. However, recent genome-wide studies revealed that the majority of variable TF-DNA binding events are not driven by sequence alterations in the motif of the studied TF. This observation implies that the molecular mechanisms underlying TF-DNA binding variation and, by extrapolation, inter-individual phenotypic variation are more complex than originally anticipated. Here, we summarize the findings that led to this important paradigm shift and review proposed mechanisms for local, proximal, or distal genetic variation-driven variable TF-DNA binding. In addition, we discuss the biomedical implications of these findings for our ability to dissect the molecular role(s) of non-coding genetic variants in complex traits, including disease susceptibility.
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Affiliation(s)
- Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Daniel Alpern
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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41
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Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, Cox NJ, Nicolae DL, Im HK. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genet 2016; 12:e1006423. [PMID: 27835642 PMCID: PMC5106030 DOI: 10.1371/journal.pgen.1006423] [Citation(s) in RCA: 127] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 10/12/2016] [Indexed: 11/19/2022] Open
Abstract
Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).
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Affiliation(s)
- Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
- Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Kaanan P. Shah
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Jonathon Brenner
- Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Tzintzuni Garcia
- Center for Research Informatics, University of Chicago, Chicago, Illinois, United States of America
| | - Keston Aquino-Michaels
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | | | - Nancy J. Cox
- Division of Genetic Medicine, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Dan L. Nicolae
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
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42
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Bønnelykke K, Ober C. Leveraging gene-environment interactions and endotypes for asthma gene discovery. J Allergy Clin Immunol 2016; 137:667-79. [PMID: 26947980 DOI: 10.1016/j.jaci.2016.01.006] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 12/30/2015] [Accepted: 01/08/2016] [Indexed: 12/20/2022]
Abstract
Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs and the advantages of studying responses to asthma-associated exposures in clinical birth cohorts, as well as in cell models of GEIs, to dissect the context-specific nature of genotypic risks, to prioritize variants in genome-wide association studies, and to identify pathways involved in pathogenesis in subgroups of patients. We propose that such approaches, in spite of their many challenges, present great opportunities for better understanding of asthma pathogenesis and heterogeneity and, ultimately, for improving prevention and treatment of disease.
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Affiliation(s)
- Klaus Bønnelykke
- COPSAC (Copenhagen Prospective Studies on Asthma in Childhood), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Ill.
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43
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Highfill CA, Reeves GA, Macdonald SJ. Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population. BMC Genet 2016; 17:113. [PMID: 27485207 PMCID: PMC4970266 DOI: 10.1186/s12863-016-0419-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 07/21/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Considerable natural variation for lifespan exists within human and animal populations. Genetically dissecting this variation can elucidate the pathways and genes involved in aging, and help uncover the genetic mechanisms underlying risk for age-related diseases. Studying aging in model systems is attractive due to their relatively short lifespan, and the ability to carry out programmed crosses under environmentally-controlled conditions. Here we investigate the genetic architecture of lifespan using the Drosophila Synthetic Population Resource (DSPR), a multiparental advanced intercross mapping population. RESULTS We measured lifespan in females from 805 DSPR lines, mapping five QTL (Quantitative Trait Loci) that each contribute 4-5 % to among-line lifespan variation in the DSPR. Each of these QTL co-localizes with the position of at least one QTL mapped in 13 previous studies of lifespan variation in flies. However, given that these studies implicate >90 % of the genome in the control of lifespan, this level of overlap is unsurprising. DSPR QTL intervals harbor 11-155 protein-coding genes, and we used RNAseq on samples of young and old flies to help resolve pathways affecting lifespan, and identify potentially causative loci present within mapped QTL intervals. Broad age-related patterns of expression revealed by these data recapitulate results from previous work. For example, we see an increase in antimicrobial defense gene expression with age, and a decrease in expression of genes involved in the electron transport chain. Several genes within QTL intervals are highlighted by our RNAseq data, such as Relish, a critical immune response gene, that shows increased expression with age, and UQCR-14, a gene involved in mitochondrial electron transport, that has reduced expression in older flies. CONCLUSIONS The five QTL we isolate collectively explain a considerable fraction of the genetic variation for female lifespan in the DSPR, and implicate modest numbers of genes. In several cases the candidate loci we highlight reside in biological pathways already implicated in the control of lifespan variation. Thus, our results provide further evidence that functional genetics tests targeting these genes will be fruitful, lead to the identification of natural sequence variants contributing to lifespan variation, and help uncover the mechanisms of aging.
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Affiliation(s)
- Chad A Highfill
- Department of Molecular Biosciences, University of Kansas, 1200 Sunnyside Avenue, Lawrence, KS, 66045, USA
| | - G Adam Reeves
- Department of Molecular Biosciences, University of Kansas, 1200 Sunnyside Avenue, Lawrence, KS, 66045, USA
| | - Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, 1200 Sunnyside Avenue, Lawrence, KS, 66045, USA. .,Center for Computational Biology, University of Kansas, 2030 Becker Drive, Lawrence, KS, 66047, USA.
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44
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Abstract
There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.
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Affiliation(s)
- William L Lowe
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Timothy E Reddy
- Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27708, USA; Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27708, USA
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45
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Peterson CB, Service SK, Jasinska AJ, Gao F, Zelaya I, Teshiba TM, Bearden CE, Cantor RM, Reus VI, Macaya G, López-Jaramillo C, Bogomolov M, Benjamini Y, Eskin E, Coppola G, Freimer NB, Sabatti C. Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder. PLoS Genet 2016; 12:e1006046. [PMID: 27176483 PMCID: PMC4866754 DOI: 10.1371/journal.pgen.1006046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 04/20/2016] [Indexed: 01/22/2023] Open
Abstract
The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information.
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Affiliation(s)
- Christine B. Peterson
- Department of Health Research and Policy, Stanford University, Stanford, California, United States of America
| | - Susan K. Service
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Anna J. Jasinska
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Fuying Gao
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Ivette Zelaya
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Terri M. Teshiba
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Rita M. Cantor
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Victor I. Reus
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
| | - Gabriel Macaya
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Costa Rica
| | - Carlos López-Jaramillo
- Grupo de Investigación en Psiquiatría (Research Group in Psychiatry (GIPSI)), Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia; Mood Disorders Program, Hospital San Vicente Fundacion, Medellín, Colombia
| | - Marina Bogomolov
- Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
| | - Yoav Benjamini
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Eleazar Eskin
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Giovanni Coppola
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Nelson B. Freimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Chiara Sabatti
- Department of Biomedical Data Science and Department of Statistics, Stanford University, Stanford, California, United States of America
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46
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Abstract
Although disproportionately affected by increasing rates of type 2 diabetes and dyslipidemias, Hispanic populations are underrepresented in efforts to understand genetic susceptibility to these disorders. Where research has been undertaken, these populations have provided substantial insight into identification of novel risk-associated genes and have aided in the ability to fine map previously described risk loci. Genome-wide analyses in Hispanic and trans-ethnic populations have resulted in identification of more than 40 replicated or novel genes with significant effects for type 2 diabetes or lipid traits. Initial investigations into rare variant effects have identified new risk-associated variants private to Hispanic populations, and preliminary results suggest metagenomic approaches in Hispanic populations, such as characterizing the gut microbiome, will enable the development of new predictive tools and therapeutic targets for type 2 diabetes. Future genome-wide studies in expanded cohorts of Hispanics are likely to result in new insights into the genetic etiology of metabolic health.
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Affiliation(s)
- Jennifer E Below
- The Human Genetics Center, University of Texas School of Public Health, Houston, TX, USA.
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, ON, Canada
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47
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Wang J, Gamazon ER, Pierce BL, Stranger BE, Im HK, Gibbons RD, Cox NJ, Nicolae DL, Chen LS. Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. Am J Hum Genet 2016; 98:697-708. [PMID: 27040689 PMCID: PMC4833292 DOI: 10.1016/j.ajhg.2016.02.020] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 02/22/2016] [Indexed: 01/14/2023] Open
Abstract
Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.
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Affiliation(s)
- Jiebiao Wang
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University and Vanderbilt Genetics Institute, Nashville, TN 37232, USA; Academic Medical Center, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Barbara E Stranger
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Robert D Gibbons
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University and Vanderbilt Genetics Institute, Nashville, TN 37232, USA
| | - Dan L Nicolae
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Statistics, University of Chicago, Chicago, IL 60637, USA
| | - Lin S Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA.
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48
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Liu J, Wan X, Ma S, Yang C. EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes. Bioinformatics 2016; 32:1856-64. [DOI: 10.1093/bioinformatics/btw081] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 02/05/2016] [Indexed: 12/12/2022] Open
Affiliation(s)
- Jin Liu
- Center of Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore,
| | - Xiang Wan
- Department of Computer Science, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Shuangge Ma
- Department of Biostatistics, Yale University, New Heaven, CT, USA
| | - Can Yang
- Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong
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49
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Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, Jansen R, de Geus EJC, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 2016; 48:245-52. [PMID: 26854917 DOI: 10.1038/ng.3506] [Citation(s) in RCA: 1200] [Impact Index Per Article: 150.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 01/14/2016] [Indexed: 02/07/2023]
Abstract
Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.
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Affiliation(s)
- Alexander Gusev
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA
| | - Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, California, USA
| | - Gaurav Bhatia
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Wonil Chung
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Fred A Wright
- Bioinformatics Research Center, Department of Statistics, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.,Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Elina Nikkola
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Mete Civelek
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Aldons J Lusis
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Pirkanmaa Hospital District and University of Tampere School of Medicine, Tampere, Finland
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, California, USA.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
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Thomsen SK, McCarthy MI, Gloyn AL. The Importance of Context: Uncovering Species- and Tissue-Specific Effects of Genetic Risk Variants for Type 2 Diabetes. Front Endocrinol (Lausanne) 2016; 7:112. [PMID: 27630614 PMCID: PMC5005446 DOI: 10.3389/fendo.2016.00112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Accepted: 08/04/2016] [Indexed: 12/30/2022] Open
Affiliation(s)
- Soren K. Thomsen
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Anna L. Gloyn
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
- *Correspondence: Anna L. Gloyn,
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