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Link V, Schraiber JG, Fan C, Dinh B, Mancuso N, Chiang CWK, Edge MD. Tree-based QTL mapping with expected local genetic relatedness matrices. Am J Hum Genet 2023; 110:2077-2091. [PMID: 38065072 PMCID: PMC10716520 DOI: 10.1016/j.ajhg.2023.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023] Open
Abstract
Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.
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Affiliation(s)
- Vivian Link
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Joshua G Schraiber
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Caoqi Fan
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bryan Dinh
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael D Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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2
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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Klau JH, Maj C, Klinkhammer H, Krawitz PM, Mayr A, Hillmer AM, Schumacher J, Heider D. AI-based multi-PRS models outperform classical single-PRS models. Front Genet 2023; 14:1217860. [PMID: 37441549 PMCID: PMC10335560 DOI: 10.3389/fgene.2023.1217860] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.
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Affiliation(s)
- Jan Henric Klau
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University Bonn, Bonn, Germany
| | - Peter M. Krawitz
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany
| | - Andreas Mayr
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University Bonn, Bonn, Germany
| | - Axel M. Hillmer
- Institute of Pathology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | | | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
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Yang L, Sadler MC, Altman RB. Genetic association studies using disease liabilities from deep neural networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.18.23284383. [PMID: 36712099 PMCID: PMC9882423 DOI: 10.1101/2023.01.18.23284383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The case-control study is a widely used method for investigating the genetic landscape of binary traits. However, the health-related outcome or disease status of participants in long-term, prospective cohort studies such as the UK Biobank are subject to change. Here, we develop an approach for the genetic association study leveraging disease liabilities computed from a deep patient phenotyping framework (AI-based liability). Analyzing 44 common traits in 261,807 participants from the UK Biobank, we identified novel loci compared to the conventional case-control (CC) association studies. Our results showed that combining liability scores with CC status was more powerful than the CC-GWAS in detecting independent genetic loci across different diseases. This boost in statistical power was further reflected in increased SNP-based heritability estimates. Moreover, polygenic risk scores calculated from AI-based liabilities better identified newly diagnosed cases in the 2022 release of the UK Biobank that served as controls in the 2019 version (6.2% percentile rank increase on average). These findings demonstrate the utility of deep neural networks that are able to model disease liabilities from high-dimensional phenotypic data in large-scale population cohorts. Our pipeline of genome-wide association studies with disease liabilities can be applied to other biobanks with rich phenotype and genotype data.
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Affiliation(s)
- Lu Yang
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Marie C. Sadler
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- University Center for Primary Care and Public Health, Lausanne, 1010, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
- Department of Medicine, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
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Saint Just Ribeiro M, Tripathi P, Namjou B, Harley JB, Chepelev I. Haplotype-specific chromatin looping reveals genetic interactions of regulatory regions modulating gene expression in 8p23.1. Front Genet 2022; 13:1008582. [PMID: 36160011 PMCID: PMC9490475 DOI: 10.3389/fgene.2022.1008582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
Abstract
A major goal of genetics research is to elucidate mechanisms explaining how genetic variation contributes to phenotypic variation. The genetic variants identified in genome-wide association studies (GWASs) generally explain only a small proportion of heritability of phenotypic traits, the so-called missing heritability problem. Recent evidence suggests that additional common variants beyond lead GWAS variants contribute to phenotypic variation; however, their mechanistic underpinnings generally remain unexplored. Herein, we undertake a study of haplotype-specific mechanisms of gene regulation at 8p23.1 in the human genome, a region associated with a number of complex diseases. The FAM167A-BLK locus in this region has been consistently found in the genome-wide association studies (GWASs) of systemic lupus erythematosus (SLE) in all major ancestries. Our haplotype-specific chromatin interaction (Hi-C) experiments, allele-specific enhancer activity measurements, genetic analyses, and epigenome editing experiments revealed that: 1) haplotype-specific long-range chromatin interactions are prevalent in 8p23.1; 2) BLK promoter and cis-regulatory elements cooperatively interact with haplotype-specificity; 3) genetic variants at distal regulatory elements are allele-specific modifiers of the promoter variants at FAM167A-BLK; 4) the BLK promoter interacts with and, as an enhancer-like promoter, regulates FAM167A expression and 5) local allele-specific enhancer activities are influenced by global haplotype structure due to chromatin looping. Although systemic lupus erythematosus causal variants at the FAM167A-BLK locus are thought to reside in the BLK promoter region, our results reveal that genetic variants at distal regulatory elements modulate promoter activity, changing BLK and FAM167A gene expression and disease risk. Our results suggest that global haplotype-specific 3-dimensional chromatin looping architecture has a strong influence on local allelic BLK and FAM167A gene expression, providing mechanistic details for how regional variants controlling the BLK promoter may influence disease risk.
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Affiliation(s)
- Mariana Saint Just Ribeiro
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Pulak Tripathi
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - John B. Harley
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
- *Correspondence: Iouri Chepelev, ; John B. Harley,
| | - Iouri Chepelev
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
- *Correspondence: Iouri Chepelev, ; John B. Harley,
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Shao Z, Wang T, Qiao J, Zhang Y, Huang S, Zeng P. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics 2022; 23:359. [PMID: 36042399 PMCID: PMC9429742 DOI: 10.1186/s12859-022-04897-3] [Citation(s) in RCA: 3] [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: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .
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Affiliation(s)
- Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuchen Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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7
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Ren D, Cai X, Lin Q, Ye H, Teng J, Li J, Ding X, Zhang Z. Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation. Genet Sel Evol 2022; 54:47. [PMID: 35761182 PMCID: PMC9235212 DOI: 10.1186/s12711-022-00737-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by - 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.
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Affiliation(s)
- Duanyang Ren
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiaodian Cai
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Qing Lin
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Haoqiang Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
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Burch KS, Hou K, Ding Y, Wang Y, Gazal S, Shi H, Pasaniuc B. Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes. Am J Hum Genet 2022; 109:692-709. [PMID: 35271803 PMCID: PMC9069080 DOI: 10.1016/j.ajhg.2022.02.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/15/2022] [Indexed: 11/15/2022] Open
Abstract
Recent works have shown that SNP heritability-which is dominated by low-effect common variants-may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ("gene-level heritability"). We partition gene-level heritability by minor allele frequency (MAF) to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% ≤ MAF < 1%). Applying our method to ∼16K protein-coding genes and 25 quantitative traits in the UK Biobank (N = 290K "White British"), we find that, on average across traits, ∼2.5% of nonzero-heritability genes have a rare-variant component and only ∼0.8% (327 gene-trait pairs) have heritability exclusively from rare variants. Of these 327 gene-trait pairs, 114 (35%) were not detected by existing gene-level association testing methods. The additional genes we identify are significantly enriched for known disease genes, and we find several examples of genes that have been previously implicated in phenotypically related Mendelian disorders. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. While nonzero gene-level heritability does not imply causality, if interpreted in the correct context, gene-level heritability can reveal useful insights into complex-trait genetic architecture.
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Affiliation(s)
- Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yifei Wang
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; OMNI Bioinformatics, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
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Wang J, Yu J, Lipka AE, Zhang Z. Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:63-80. [PMID: 35641759 DOI: 10.1007/978-1-0716-2237-7_5] [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] [Indexed: 06/15/2023]
Abstract
With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.
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Affiliation(s)
- Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, Sichuan, China.
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
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10
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Lu H, Wei Y, Jiang Z, Zhang J, Wang T, Huang S, Zeng P. Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies. J Transl Med 2021; 19:418. [PMID: 34627275 PMCID: PMC8502405 DOI: 10.1186/s12967-021-03090-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 09/26/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. METHODS We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. RESULTS Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. CONCLUSION IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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11
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Khanzadeh H, Ghavi Hossein-Zadeh N, Ghovvati S. Statistical power and heritability in whole-genome association studies for quantitative traits. Meta Gene 2021. [DOI: 10.1016/j.mgene.2021.100869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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12
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Ithnin M, Vu WT, Shin MG, Suryawanshi V, Sherbina K, Zolkafli SH, Serdari NM, Amiruddin MD, Abdullah N, Mustaffa S, Marjuni M, Nookiah R, Kushairi A, Marjoram P, Nuzhdin SV, Chang PL, Singh R. Genomic diversity and genome-wide association analysis related to yield and fatty acid composition of wild American oil palm. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 304:110731. [PMID: 33568284 DOI: 10.1016/j.plantsci.2020.110731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 10/13/2020] [Accepted: 10/15/2020] [Indexed: 05/15/2023]
Abstract
Existing Elaeis guineensis cultivars lack sufficient genetic diversity due to extensive breeding. Harnessing variation in wild crop relatives is necessary to expand the breadth of agronomically valuable traits. Using RAD sequencing, we examine the natural diversity of wild American oil palm populations (Elaeis oleifera), a sister species of the cultivated Elaeis guineensis oil palm. We genotyped 192 wild E. oleifera palms collected from seven Latin American countries along with four cultivated E. guineensis palms. Honduras, Costa Rica, Panama and Colombia palms are panmictic and genetically similar. Genomic patterns of diversity suggest that these populations likely originated from the Amazon Basin. Despite evidence of a genetic bottleneck and high inbreeding observed in these populations, there is considerable genetic and phenotypic variation for agronomically valuable traits. Genome-wide association revealed several candidate genes associated with fatty acid composition along with vegetative and yield-related traits. These observations provide valuable insight into the geographic distribution of diversity, phenotypic variation and its genetic architecture that will guide choices of wild genotypes for crop improvement.
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Affiliation(s)
- Maizura Ithnin
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Wendy T Vu
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Min-Gyoung Shin
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Vasantika Suryawanshi
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Katrina Sherbina
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Siti Hazirah Zolkafli
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Norhalida Mohamed Serdari
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Mohd Din Amiruddin
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Norziha Abdullah
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Suzana Mustaffa
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Marhalil Marjuni
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Rajanaidu Nookiah
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Ahmad Kushairi
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia
| | - Paul Marjoram
- Department of Preventative Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Sergey V Nuzhdin
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Peter L Chang
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
| | - Rajinder Singh
- Malaysian Palm Oil Board, 6, Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, 43000, Malaysia.
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13
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Ortiz-Fernández L, Sawalha AH. Genetics of Behçet's Disease: Functional Genetic Analysis and Estimating Disease Heritability. Front Med (Lausanne) 2021; 8:625710. [PMID: 33644100 PMCID: PMC7907152 DOI: 10.3389/fmed.2021.625710] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/14/2021] [Indexed: 01/17/2023] Open
Abstract
Behçet's disease is a chronic multisystemic inflammatory disorder characterized by recurrent oral and genital ulcers. Although its etiology remains unclear, it is thought that both genetic and environmental factors contribute to the onset and progression of Behçet's disease. Here, we provide an updated view of the genetic landscape and architecture of Behçet's disease. Large-scale genetic studies performed to date revealed 21 genetic susceptibility loci associated with the disease at a GWAS level of significance (p-value = 5 × 10−8). We performed epigenetic pattern enrichment analysis in Behçet's disease associated loci, providing new insights into the molecular mechanisms underlying its pathophysiology. Our data suggest the crucial involvement of several immune cell types, including natural killer cells, monocytes, and B cells in the pathogenesis of the disease. Pathway enrichment analysis identified important biological processes involved. Using large-scale genetic data available from ~200 immune-related loci (Immunochip), we estimate Behçet's disease heritability to be at least 16%. We further used the same approach to estimate the heritability explained by the known Behçet's disease-associated loci, suggesting that they explain ~ 60% of the genetic component underlying Behçet's disease. These results indicate a significant role of non-genetic factors in causing Behçet's disease and that additional genetic variation influencing the risk of Behçet's disease remains to be identified. Finally, we calculated a cumulative genetic risk score across populations reinforcing the link between geographic variations in disease prevalence with its genetic component.
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Affiliation(s)
- Lourdes Ortiz-Fernández
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Amr H Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Lupus Center of Excellence, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
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14
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EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm. Genes (Basel) 2021; 12:genes12020191. [PMID: 33525573 PMCID: PMC7911965 DOI: 10.3390/genes12020191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 01/12/2021] [Accepted: 01/25/2021] [Indexed: 12/28/2022] Open
Abstract
In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, “small sample size problem”, diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer’s disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer’s disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases.
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15
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Chen H, Wang T, Yang J, Huang S, Zeng P. Improved Detection of Potentially Pleiotropic Genes in Coronary Artery Disease and Chronic Kidney Disease Using GWAS Summary Statistics. Front Genet 2020; 11:592461. [PMID: 33343632 PMCID: PMC7744760 DOI: 10.3389/fgene.2020.592461] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/17/2020] [Indexed: 12/24/2022] Open
Abstract
The coexistence of coronary artery disease (CAD) and chronic kidney disease (CKD) implies overlapped genetic foundation. However, the common genetic determination between the two diseases remains largely unknown. Relying on summary statistics publicly available from large scale genome-wide association studies (n = 184,305 for CAD and n = 567,460 for CKD), we observed significant positive genetic correlation between CAD and CKD (rg = 0.173, p = 0.024) via the linkage disequilibrium score regression. Next, we implemented gene-based association analysis for each disease through MAGMA (Multi-marker Analysis of GenoMic Annotation) and detected 763 and 827 genes associated with CAD or CKD (FDR < 0.05). Among those 72 genes were shared between the two diseases. Furthermore, by integrating the overlapped genetic information between CAD and CKD, we implemented two pleiotropy-informed informatics approaches including cFDR (conditional false discovery rate) and GPA (Genetic analysis incorporating Pleiotropy and Annotation), and identified 169 and 504 shared genes (FDR < 0.05), of which 121 genes were simultaneously discovered by cFDR and GPA. Importantly, we found 11 potentially new pleiotropic genes related to both CAD and CKD (i.e., ARHGEF19, RSG1, NDST2, CAMK2G, VCL, LRP10, RBM23, USP10, WNT9B, GOSR2, and RPRML). Five of the newly identified pleiotropic genes were further repeated via an additional dataset CAD available from UK Biobank. Our functional enrichment analysis showed that those pleiotropic genes were enriched in diverse relevant pathway processes including quaternary ammonium group transmembrane transporter, dopamine transport. Overall, this study identifies common genetic architectures overlapped between CAD and CKD and will help to advance understanding of the molecular mechanisms underlying the comorbidity of the two diseases.
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Affiliation(s)
- Haimiao Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ting Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Jinna Yang
- Department of Infectious Diseases, People's Hospital of Zhuji, Shaoxing, China
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
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16
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Abegaz F, Chaichoompu K, Génin E, Fardo DW, König IR, Mahachie John JM, Van Steen K. Principals about principal components in statistical genetics. Brief Bioinform 2020; 20:2200-2216. [PMID: 30219892 DOI: 10.1093/bib/bby081] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 07/21/2018] [Accepted: 08/12/2018] [Indexed: 12/13/2022] Open
Abstract
Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.
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17
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Shi H, Burch KS, Johnson R, Freund MK, Kichaev G, Mancuso N, Manuel AM, Dong N, Pasaniuc B. Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data. Am J Hum Genet 2020; 106:805-817. [PMID: 32442408 PMCID: PMC7273527 DOI: 10.1016/j.ajhg.2020.04.012] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 04/20/2020] [Indexed: 12/19/2022] Open
Abstract
Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8× enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.
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Affiliation(s)
- Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Ruth Johnson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Malika K Freund
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Astrid M Manuel
- Department of Biological Sciences, Florida International University, Miami, FL 33199, USA
| | - Natalie Dong
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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18
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Determining population stratification and subgroup effects in association studies of rare genetic variants for nicotine dependence. Psychiatr Genet 2020; 29:111-119. [PMID: 31033776 PMCID: PMC6636808 DOI: 10.1097/ypg.0000000000000227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Supplemental Digital Content is available in the text. Background Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of ‘missing heritability’ and have a proven role in dissecting the etiology for human diseases and complex traits. Methods We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as ‘str-CMC’ and ‘str-WSS’. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran–Mantel–Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes. Results The Cochran–Mantel–Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O. Conclusions Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.
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19
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Hernandez RD, Uricchio LH, Hartman K, Ye C, Dahl A, Zaitlen N. Ultrarare variants drive substantial cis heritability of human gene expression. Nat Genet 2019; 51:1349-1355. [PMID: 31477931 PMCID: PMC6730564 DOI: 10.1038/s41588-019-0487-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Accepted: 07/08/2019] [Indexed: 11/09/2022]
Abstract
The vast majority of human mutations have minor allele frequencies under 1%, with the plurality observed only once (that is, 'singletons'). While Mendelian diseases are predominantly caused by rare alleles, their cumulative contribution to complex phenotypes is largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of all alleles, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole-genome DNA and lymphoblastoid cell line RNA sequencing data from 360 European individuals, we conservatively estimate that singletons contribute approximately 25% of cis heritability across genes (dwarfing the contributions of other frequencies). The majority (approximately 76%) of singleton heritability derives from ultrarare variants absent from thousands of additional samples. We develop an inference procedure to demonstrate that our results are consistent with pervasive purifying selection shaping the regulatory architecture of most human genes.
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Affiliation(s)
- Ryan D Hernandez
- Bioengineering & Therapeutic Sciences, UCSF, San Francisco, CA, USA.
- Institute for Human Genetics, UCSF, San Francisco, CA, USA.
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA.
- Institute for Computational Health Sciences, UCSF, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- McGill University and the Genome Quebec Innovation Center, Montreal, Quebec, Canada.
| | | | - Kevin Hartman
- Biological and Medical Informatics Graduate Program, UCSF, San Francisco, CA, USA
| | - Chun Ye
- Institute for Human Genetics, UCSF, San Francisco, CA, USA
- Epidemiology & Biostatistics, UCSF, San Francisco, CA, USA
| | - Andrew Dahl
- Institute for Human Genetics, UCSF, San Francisco, CA, USA
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA
| | - Noah Zaitlen
- Institute for Human Genetics, UCSF, San Francisco, CA, USA.
- Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA.
- Department of Medicine Lung Biology Center, UCSF, San Francisco, CA, USA.
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20
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Cheng B, Liang X, Wen Y, Li P, Zhang L, Ma M, Cheng S, Du Y, Liu L, Ding M, Zhao Y, Zhang F. Integrative analysis of transcriptome‐wide association study data and messenger RNA expression profiles identified candidate genes and pathways for inflammatory bowel disease. J Cell Biochem 2019; 120:14831-14837. [DOI: 10.1002/jcb.28744] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 03/17/2019] [Accepted: 03/22/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Xiao Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Ping Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Lu Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Mei Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Yanan Du
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Miao Ding
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Yan Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education, Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, School of Public Health, Health Science Center Xi'an Jiaotong University Xi'an Shaanxi P.R. China
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21
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Schoech AP, Jordan DM, Loh PR, Gazal S, O'Connor LJ, Balick DJ, Palamara PF, Finucane HK, Sunyaev SR, Price AL. Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection. Nat Commun 2019; 10:790. [PMID: 30770844 PMCID: PMC6377669 DOI: 10.1038/s41467-019-08424-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 01/09/2019] [Indexed: 02/06/2023] Open
Abstract
Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.
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Affiliation(s)
- Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
| | - Daniel M Jordan
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Luke J O'Connor
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Daniel J Balick
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, USA
| | - Pier F Palamara
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
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23
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Kemppainen P, Husby A. Accounting for heteroscedasticity and censoring in chromosome partitioning analyses. Evol Lett 2018; 2:599-609. [PMID: 30564443 PMCID: PMC6292708 DOI: 10.1002/evl3.88] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 10/07/2018] [Accepted: 10/10/2018] [Indexed: 01/02/2023] Open
Abstract
A fundamental assumption in quantitative genetics is that traits are controlled by many loci of small effect. Using genomic data, this assumption can be tested using chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2c), is regressed on its size. However, as h2c‐estimates are necessarily positive (censoring) and the variance increases with chromosome size (heteroscedasticity), two fundamental assumptions of ordinary least squares (OLS) regression are violated. Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture. The degree of bias depends mainly on the number of chromosomes and their size distribution and is therefore specific to the species; using published data across many different species we estimate that not accounting for this effect overall resulted in 28% false positives. We introduce a new and computationally efficient resampling method that corrects for inflation caused by heteroscedasticity and censoring and that works under a large range of dataset sizes and genetic architectures in empirical datasets. Our new method substantially improves the robustness of inferences from chromosome partitioning analyses.
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Affiliation(s)
- Petri Kemppainen
- Organismal and Evolutionary Biology Research Programme University of Helsinki 00014 Helsinki Finland
| | - Arild Husby
- Organismal and Evolutionary Biology Research Programme University of Helsinki 00014 Helsinki Finland.,Department of Ecology and Genetics Uppsala University 75236 Uppsala Sweden
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Li B, Kremling KAG, Wu P, Bukowski R, Romay MC, Xie E, Buckler ES, Chen M. Coregulation of ribosomal RNA with hundreds of genes contributes to phenotypic variation. Genome Res 2018; 28:1555-1565. [PMID: 30166407 PMCID: PMC6169892 DOI: 10.1101/gr.229716.117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 08/29/2018] [Indexed: 12/16/2022]
Abstract
Ribosomal repeats occupy 5% of a plant genome, yet there has been little study of their diversity in the modern age of genomics. Ribosomal copy number and expression variation present an opportunity to tap a novel source of diversity. In the present study, we estimated the ribosomal DNA (rDNA) copy number and ribosomal RNA (rRNA) expression for a population of maize inbred lines and investigated the potential role of rDNA and rRNA dosage in regulating global gene expression. Extensive variation was found in both ribosomal DNA copy number and ribosomal RNA expression among maize inbred lines. However, rRNA abundance was not consistent with the copy number of the rDNA. We have not found that the rDNA gene dosage has a regulatory role in gene expression; however, thousands of genes are identified to be coregulated with rRNA expression, including genes participating in ribosome biogenesis and other functionally relevant pathways. We further investigated the potential roles of copy number and the expression level of rDNA on agronomic traits and found that both correlated with flowering time but through different regulatory mechanisms. This comprehensive analysis suggested that rRNA expression variation is a valuable source of functional diversity that affects gene expression variation and field-based phenotypic changes.
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Affiliation(s)
- Bo Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 100101
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Karl A G Kremling
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Penghao Wu
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- College of Agronomy, Xinjiang Agriculture University, Urumqi, China 830052
| | - Robert Bukowski
- Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York 14853, USA
| | - Maria C Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - En Xie
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 100101
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853, USA
- US Department of Agriculture-Agricultural Research Service (USDA-ARS), Ithaca, New York 14853, USA
| | - Mingsheng Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 100101
- University of Chinese Academy of Sciences, Beijing, China 100049
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25
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The Biological Contributions to Gender Identity and Gender Diversity: Bringing Data to the Table. Behav Genet 2018; 48:95-108. [DOI: 10.1007/s10519-018-9889-z] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 02/05/2018] [Indexed: 01/13/2023]
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26
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Gumpinger AC, Roqueiro D, Grimm DG, Borgwardt KM. Methods and Tools in Genome-wide Association Studies. Methods Mol Biol 2018; 1819:93-136. [PMID: 30421401 DOI: 10.1007/978-1-4939-8618-7_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.
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Affiliation(s)
- Anja C Gumpinger
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Damian Roqueiro
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dominik G Grimm
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Karsten M Borgwardt
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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27
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A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction. Heredity (Edinb) 2017; 120:356-368. [PMID: 29238077 PMCID: PMC5842222 DOI: 10.1038/s41437-017-0023-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/13/2017] [Accepted: 10/23/2017] [Indexed: 12/15/2022] Open
Abstract
Single nucleotide polymorphism (SNP)-heritability estimation is an important topic in several research fields, including animal, plant and human genetics, as well as in ecology. Linear mixed model estimation of SNP-heritability uses the structures of genomic relationships between individuals, which is constructed from genome-wide sets of SNP-markers that are generally weighted equally in their contributions. Proposed methods to handle dependence between SNPs include, “thinning” the marker set by linkage disequilibrium (LD)-pruning, the use of haplotype-tagging of SNPs, and LD-weighting of the SNP-contributions. For improved estimation, we propose a new conceptual framework for genomic relationship matrix, in which Mahalanobis distance-based LD-correction is used in a linear mixed model estimation of SNP-heritability. The superiority of the presented method is illustrated and compared to mixed-model analyses using a VanRaden genomic relationship matrix, a matrix used by GCTA and a matrix employing LD-weighting (as implemented in the LDAK software) in simulated (using real human, rice and cattle genotypes) and real (maize, rice and mice) datasets. Despite of the computational difficulties, our results suggest that by using the proposed method one can improve the accuracy of SNP-heritability estimates in datasets with high LD.
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Zhou X. A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES. Ann Appl Stat 2017; 11:2027-2051. [PMID: 29515717 PMCID: PMC5836736 DOI: 10.1214/17-aoas1052] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z-scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.
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Weng LC, Choi SH, Klarin D, Smith JG, Loh PR, Chaffin M, Roselli C, Hulme OL, Lunetta KL, Dupuis J, Benjamin EJ, Newton-Cheh C, Kathiresan S, Ellinor PT, Lubitz SA. Heritability of Atrial Fibrillation. CIRCULATION. CARDIOVASCULAR GENETICS 2017; 10:e001838. [PMID: 29237688 PMCID: PMC5966046 DOI: 10.1161/circgenetics.117.001838] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 09/25/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified. METHODS AND RESULTS We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. CONCLUSIONS Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.
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Affiliation(s)
- Lu-Chen Weng
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Seung Hoan Choi
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Derek Klarin
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - J Gustav Smith
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Po-Ru Loh
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Mark Chaffin
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Carolina Roselli
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Olivia L Hulme
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Kathryn L Lunetta
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Josée Dupuis
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Emelia J Benjamin
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Christopher Newton-Cheh
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Sekar Kathiresan
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Patrick T Ellinor
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Steven A Lubitz
- From the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA (L.-C.W., S.H.C., D.K., J.G.S. P.-R.L., M.C., C.R., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.); Department of Cardiology, Clinical Sciences, Lund University, Sweden (J.G.S.); Department of Heart Failure and Valvular Disease, Skane University Hospital, Lund, Sweden (J.G.S.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.-R.L.); Boston University and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (K.L.L., J.D., E.J.B.); Boston University School of Public Health, Boston, MA (K.L.L., J.D., E.J.B.); Boston University School of Medicine, Boston, MA (E.J.B.); and Cardiovascular Research Center (L.-C.W., D.K., J.G.S., O.L.H., C.N.-C., S.K., P.T.E., S.A.L.) and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA.
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30
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Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet 2017; 49:1304-1310. [PMID: 28854176 DOI: 10.1038/ng.3941] [Citation(s) in RCA: 240] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 07/31/2017] [Indexed: 12/17/2022]
Abstract
Narrow-sense heritability (h2) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h2 previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.
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Affiliation(s)
- Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
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31
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Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat Genet 2017; 49:1421-1427. [PMID: 28892061 DOI: 10.1038/ng.3954] [Citation(s) in RCA: 265] [Impact Index Per Article: 37.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 08/16/2017] [Indexed: 12/14/2022]
Abstract
Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 × 10-104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.
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32
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Dong SS, Guo Y, Yao S, Chen YX, He MN, Zhang YJ, Chen XF, Chen JB, Yang TL. Integrating regulatory features data for prediction of functional disease-associated SNPs. Brief Bioinform 2017; 20:26-32. [DOI: 10.1093/bib/bbx094] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Indexed: 12/21/2022] Open
Affiliation(s)
- Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Yi-Xiao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Mo-Nan He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Yu-Jie Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Jia-Bin Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
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33
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Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ. Reevaluation of SNP heritability in complex human traits. Nat Genet 2017; 49:986-992. [PMID: 28530675 PMCID: PMC5493198 DOI: 10.1038/ng.3865] [Citation(s) in RCA: 250] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 04/18/2017] [Indexed: 12/15/2022]
Abstract
SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.
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Affiliation(s)
- Doug Speed
- UCL Genetics Institute, University College London, London, UK
| | - Na Cai
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | | | | | | | - David J Balding
- UCL Genetics Institute, University College London, London, UK.,Centre for Systems Genomics, School of BioSciences, and School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
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34
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Macciotta NPP, Biffani S, Bernabucci U, Lacetera N, Vitali A, Ajmone-Marsan P, Nardone A. Derivation and genome-wide association study of a principal component-based measure of heat tolerance in dairy cattle. J Dairy Sci 2017; 100:4683-4697. [PMID: 28365122 DOI: 10.3168/jds.2016-12249] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 02/05/2017] [Indexed: 12/19/2022]
Abstract
Heat stress represents a key factor that negatively affects the productive and reproductive performance of farm animals. In the present work, a new measure of tolerance to heat stress for dairy cattle was developed using principal component analysis. Data were from 590,174 test-day records for milk yield, fat and protein percentages, and somatic cell score of 39,261 Italian Holstein cows. Test-day records adjusted for main systematic factors were grouped into 11 temperature-humidity index (THI) classes. Daughter trait deviations (DTD) were calculated for 1,540 bulls as means of the adjusted test-day records for each THI class. Principal component analysis was performed on the DTD for each bull. The first 2 principal components (PC) explained 42 to 51% of the total variance of the system across the 4 traits. The first PC, a measure of the level at which the curve is located, was interpreted as a measure of the level at which the DTD curve was located. The second PC, which shows the slope of increasing or decreases DTD curves, synthesized the behavior of the DTD pattern. Heritability of the 2 component scores was moderate to high for level across all traits (range = 0.23-0.82) and low to moderate for slope (range = 0.16-0.28). For each trait, phenotypic and genetic correlations between level and slope were equal to zero. A genome-wide association analysis was carried out on a subsample of 423 bulls genotyped with the Illumina 50K bovine bead chip (Illumina, San Diego, CA). Two single nucleotide polymorphisms were significantly associated with slope for milk yield, 4 with level for fat percentage, and 2 with level and slope of protein percentage, respectively. The gene discovery was carried out considering windows of 0.5 Mb surrounding the significant markers and highlighted some interesting candidate genes. Some of them have been already associated with the mechanism of heat tolerance as the heat shock transcription factor (HSF1) and the malonyl-CoA-acyl carrier protein transacylase (MCAT). The 2 PC were able to describe the overall level and the slope of response of milk production traits across increasing levels of THI index. Moreover, they exhibited genetic variability and were genetically uncorrelated. These features suggest their use as measures of thermotolerance in dairy cattle breeding schemes.
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Affiliation(s)
- N P P Macciotta
- Dipartimento di Agraria, Università di Sassari, 07100 Sassari, Italy.
| | - S Biffani
- Associazione Italiana Allevatori, 00161 Roma, Italy
| | - U Bernabucci
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - N Lacetera
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - A Vitali
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - P Ajmone-Marsan
- Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy
| | - A Nardone
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy.
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35
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Xiao Y, Liu H, Wu L, Warburton M, Yan J. Genome-wide Association Studies in Maize: Praise and Stargaze. MOLECULAR PLANT 2017; 10:359-374. [PMID: 28039028 DOI: 10.1016/j.molp.2016.12.008] [Citation(s) in RCA: 208] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Revised: 12/02/2016] [Accepted: 12/20/2016] [Indexed: 05/18/2023]
Abstract
Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.
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Affiliation(s)
- Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Liuji Wu
- Synergetic Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Marilyn Warburton
- United States of Department of Agriculture, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Box 9555, MS 39762, Mississippi, USA
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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36
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Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 2017; 18:117-127. [PMID: 27840428 PMCID: PMC5449190 DOI: 10.1038/nrg.2016.142] [Citation(s) in RCA: 256] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.
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Affiliation(s)
- Bogdan Pasaniuc
- Departments of Human Genetics, and Pathology and Laboratory Medicine, University of California, Los Angeles, California 90095, USA
| | - Alkes L Price
- Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
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37
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Lee PH, Baker JT, Holmes AJ, Jahanshad N, Ge T, Jung JY, Cruz Y, Manoach DS, Hibar DP, Faskowitz J, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Öngür D, Buckner R, Roffman J, Thompson PM, Smoller JW. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol Psychiatry 2016; 21:1680-1689. [PMID: 27725656 PMCID: PMC5144575 DOI: 10.1038/mp.2016.164] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 07/14/2016] [Accepted: 08/11/2016] [Indexed: 01/18/2023]
Abstract
Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.
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Affiliation(s)
- P H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - J T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - A J Holmes
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - N Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - T Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - J-Y Jung
- Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, USA
| | - Y Cruz
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Harvard Graduate School of Education, Cambridge, MA, USA
| | - D S Manoach
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - D P Hibar
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J Faskowitz
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - K L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - G I de Zubicaray
- Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - N G Martin
- Queensland Institute of Medical Research (QIMR) Berghofer, Brisbane, QLD, Australia
| | - M J Wright
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - D Öngür
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - R Buckner
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - J Roffman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia Clinical and Research Program, Massachusetts General Hospital, Boston, MA, USA
| | - P M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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38
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Kim K, Bang SY, Lee HS, Bae SC. Update on the genetic architecture of rheumatoid arthritis. Nat Rev Rheumatol 2016; 13:13-24. [PMID: 27811914 DOI: 10.1038/nrrheum.2016.176] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.
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Affiliation(s)
- Kwangwoo Kim
- Department of Biology, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
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39
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Corradin O, Cohen AJ, Luppino JM, Bayles IM, Schumacher FR, Scacheri PC. Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry. Nat Genet 2016; 48:1313-1320. [PMID: 27643537 PMCID: PMC5083135 DOI: 10.1038/ng.3674] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/22/2016] [Indexed: 12/21/2022]
Abstract
SNPs associated with disease susceptibility often reside in enhancer clusters, or super-enhancers. Constituents of these enhancer clusters cooperate to regulate target genes and often extend beyond the linkage disequilibrium (LD) blocks containing risk SNPs identified in genome-wide association studies (GWAS). We identified 'outside variants', defined as SNPs in weak LD with GWAS risk SNPs that physically interact with risk SNPs as part of a target gene's regulatory circuitry. These outside variants further explain variation in target gene expression beyond that explained by GWAS-associated SNPs. Additionally, the clinical risk associated with GWAS SNPs is considerably modified by the genotype of outside variants. Collectively, these findings suggest a potential model in which outside variants and GWAS SNPs that physically interact in 3D chromatin collude to influence target transcript levels as well as clinical risk. This model offers an additional hypothesis for the source of missing heritability for complex traits.
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Affiliation(s)
- Olivia Corradin
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA
| | - Andrea J Cohen
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jennifer M Luppino
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Ian M Bayles
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Fredrick R Schumacher
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA
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40
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Lee SH, Byrne EM, Hultman CM, Kähler A, Vinkhuyzen AAE, Ripke S, Andreassen OA, Frisell T, Gusev A, Hu X, Karlsson R, Mantzioris VX, McGrath JJ, Mehta D, Stahl EA, Zhao Q, Kendler KS, Sullivan PF, Price AL, O'Donovan M, Okada Y, Mowry BJ, Raychaudhuri S, Wray NR, Byerley W, Cahn W, Cantor RM, Cichon S, Cormican P, Curtis D, Djurovic S, Escott-Price V, Gejman PV, Georgieva L, Giegling I, Hansen TF, Ingason A, Kim Y, Konte B, Lee PH, McIntosh A, McQuillin A, Morris DW, Nöthen MM, O'Dushlaine C, Olincy A, Olsen L, Pato CN, Pato MT, Pickard BS, Posthuma D, Rasmussen HB, Rietschel M, Rujescu D, Schulze TG, Silverman JM, Thirumalai S, Werge T, Agartz I, Amin F, Azevedo MH, Bass N, Black DW, Blackwood DHR, Bruggeman R, Buccola NG, Choudhury K, Cloninger RC, Corvin A, Craddock N, Daly MJ, Datta S, Donohoe GJ, Duan J, Dudbridge F, Fanous A, Freedman R, Freimer NB, Friedl M, Gill M, Gurling H, De Haan L, Hamshere ML, Hartmann AM, Holmans PA, Kahn RS, Keller MC, Kenny E, Kirov GK, Krabbendam L, Krasucki R, Lawrence J, Lencz T, Levinson DF, Lieberman JA, Lin DY, Linszen DH, Magnusson PKE, Maier W, Malhotra AK, Mattheisen M, Mattingsdal M, McCarroll SA, Medeiros H, Melle I, Milanova V, Myin-Germeys I, Neale BM, Ophoff RA, Owen MJ, Pimm J, Purcell SM, Puri V, Quested DJ, Rossin L, Ruderfer D, Sanders AR, Shi J, Sklar P, St Clair D, Stroup TS, Van Os J, Visscher PM, Wiersma D, Zammit S, Bridges SL, Choi HK, Coenen MJH, de Vries N, Dieud P, Greenberg JD, Huizinga TWJ, Padyukov L, Siminovitch KA, Tak PP, Worthington J, De Jager PL, Denny JC, Gregersen PK, Klareskog L, Mariette X, Plenge RM, van Laar M, van Riel P. New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis. Int J Epidemiol 2016; 44:1706-21. [PMID: 26286434 DOI: 10.1093/ije/dyv136] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. METHODS We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. RESULTS We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090). CONCLUSIONS Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.
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41
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Shi H, Kichaev G, Pasaniuc B. Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data. Am J Hum Genet 2016; 99:139-53. [PMID: 27346688 DOI: 10.1016/j.ajhg.2016.05.013] [Citation(s) in RCA: 217] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 05/09/2016] [Indexed: 01/10/2023] Open
Abstract
Variance-component methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the genetic architecture of common diseases. Here, we introduce methods that estimate the total trait variance explained by the typed variants at a single locus in the genome (local SNP heritability) from genome-wide association study (GWAS) summary data while accounting for linkage disequilibrium among variants. We applied our estimator to ultra-large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture. First, we found that common SNPs have a high contribution to the heritability of all studied traits. Second, we identified traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identified GWAS risk loci where the entire locus explains significantly more variance in the trait than the GWAS reported variants. Finally, we identified loci that explain a significant amount of heritability across multiple traits.
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Affiliation(s)
- Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA.
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42
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A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics. Sci Rep 2016; 6:27644. [PMID: 27273519 PMCID: PMC4897708 DOI: 10.1038/srep27644] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/18/2016] [Indexed: 11/09/2022] Open
Abstract
Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (N = 7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.
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43
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Accuracy of heritability estimations in presence of hidden population stratification. Sci Rep 2016; 6:26471. [PMID: 27220488 PMCID: PMC4879529 DOI: 10.1038/srep26471] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 04/29/2016] [Indexed: 01/05/2023] Open
Abstract
The heritability of a trait is the proportion of its variance explained by genetic factors; it has historically been estimated using familial data. However, new methods have appeared for estimating heritabilities using genomewide data from unrelated individuals. A drawback of this strategy is that population stratification can bias the estimates. Indeed, an environmental factor associated with the phenotype may differ among population subgroups. This factor being associated both with the phenotype and the genetic variation in the population would be a confounder. A common solution consists in adjusting on the first Principal Components (PCs) of the genomic data. We study this procedure on simulated data and on 6000 individuals from the Three-City Study. We analyse the geographical coordinates of the birth cities, which are not genetically determined, but the heritability of which should be overestimated due to population stratification. We also analyse various anthropometric traits. The procedure fails to correct the bias in geographical coordinates heritability estimates. The heritability estimates of the anthropometric traits are affected by the inclusion of the first PC, but not by the following PCs, contrarily to geographical coordinates. We recommend to be cautious with heritability estimates obtained from a large population.
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44
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Terao C, Raychaudhuri S, Gregersen PK. Recent Advances in Defining the Genetic Basis of Rheumatoid Arthritis. Annu Rev Genomics Hum Genet 2016; 17:273-301. [PMID: 27216775 DOI: 10.1146/annurev-genom-090314-045919] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Rheumatoid arthritis (RA) is the most common inflammatory arthritis and exhibits genetic overlap with other autoimmune and inflammatory disorders. Although predominant associations with the HLA-DRB1 locus have been known for decades, recent data have revealed additional insight into the likely causative variants within HLA-DRB1 as well as within other HLA loci that contribute to disease risk. In addition, more than 100 common variants in non-HLA loci have been implicated in disease susceptibility. Genetic factors are involved not only in the development of RA, but also with various disease subphenotypes, including production and circulating levels of autoantibodies and joint destruction. The major current challenge is to integrate these new data into a precise understanding of disease pathogenesis, including the critical cell types and molecular networks involved as well as interactions with environmental factors. We predict that delineating the functional effects of genetic variants is likely to drive new diagnostic and therapeutic approaches to the disease.
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Affiliation(s)
- Chikashi Terao
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115.,Partners Center for Personalized Genetic Medicine, Boston, Massachusetts 02115.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142; .,Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto 606-8501, Japan;
| | - Soumya Raychaudhuri
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115.,Partners Center for Personalized Genetic Medicine, Boston, Massachusetts 02115.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142; .,Institute of Inflammation and Repair, University of Manchester, M15 6SZ Manchester, United Kingdom.,Rheumatology Unit, Department of Medicine, Karolinska Institutet and Karolinska University Hospital Solna, SE-171 76 Stockholm, Sweden
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, Manhasset, New York 11030;
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45
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Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies. Genetics 2016; 203:1105-16. [PMID: 27182951 DOI: 10.1534/genetics.116.188292] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 04/27/2016] [Indexed: 12/19/2022] Open
Abstract
There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester.
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46
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Xiao Y, Tong H, Yang X, Xu S, Pan Q, Qiao F, Raihan MS, Luo Y, Liu H, Zhang X, Yang N, Wang X, Deng M, Jin M, Zhao L, Luo X, Zhou Y, Li X, Liu J, Zhan W, Liu N, Wang H, Chen G, Cai Y, Xu G, Wang W, Zheng D, Yan J. Genome-wide dissection of the maize ear genetic architecture using multiple populations. THE NEW PHYTOLOGIST 2016; 210:1095-106. [PMID: 26715032 DOI: 10.1111/nph.13814] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 11/23/2015] [Indexed: 05/02/2023]
Abstract
Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.
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Affiliation(s)
- Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hao Tong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaohong Yang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Qingchun Pan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Qiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mohammad Sharif Raihan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuehai Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaqing Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Min Deng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Minliang Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lijun Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xin Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yang Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jie Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Zhan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Nannan Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hong Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Gengshen Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ye Cai
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Gen Xu
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Weidong Wang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Debo Zheng
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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47
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Mosley JD, Witte JS, Larkin EK, Bastarache L, Shaffer CM, Karnes JH, Stein CM, Phillips E, Hebbring SJ, Brilliant MH, Mayer J, Ye Z, Roden DM, Denny JC. Identifying genetically driven clinical phenotypes using linear mixed models. Nat Commun 2016; 7:11433. [PMID: 27109359 PMCID: PMC4848547 DOI: 10.1038/ncomms11433] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 03/24/2016] [Indexed: 01/06/2023] Open
Abstract
We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1-1.2), P=9.8 × 10(-11)) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3-1.6), P=1.3 × 10(-10)). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations.
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Affiliation(s)
- Jonathan D. Mosley
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94158, USA
| | - Emma K. Larkin
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Lisa Bastarache
- Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37203, USA
| | | | - Jason H. Karnes
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - C. Michael Stein
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Elizabeth Phillips
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Scott J. Hebbring
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA
| | - Murray H. Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA
| | - John Mayer
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA
| | - Zhan Ye
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Joshua C. Denny
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA
- Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37203, USA
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48
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WWOX CNV-67048 Functions as a Risk Factor for Epithelial Ovarian Cancer in Chinese Women by Negatively Interacting with Oral Contraceptive Use. BIOMED RESEARCH INTERNATIONAL 2016; 2016:6594039. [PMID: 27190995 PMCID: PMC4842385 DOI: 10.1155/2016/6594039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 03/22/2016] [Indexed: 12/22/2022]
Abstract
Copy number variations (CNVs) have attracted increasing evidences to represent their roles as cancer susceptibility regulators. However, little is known about the role of CNV in epithelia ovarian cancer (EOC). Recently, the CNV-67048 of WW domain-containing oxidoreductase (WWOX) was reported to alter cancer risks. Considering that WWOX also plays a role in EOC, we hypothesized that the CNV-67048 was associated with EOC risk. In a case-control study of 549 EOC patients and 571 age (±5 years) matched cancer-free controls, we found that the low copy number of CNV-67048 (1-copy and 0-copy) conferred a significantly increased risk of EOC (OR = 1.346, 95% CI = 1.037–1.747) and it determined the risk by means of copy number-dependent dosage effect (P = 0.009). Data from TCGA also confirmed the abovementioned association as the frequency of low copies in EOC group was 3.68 times more than that in healthy group (P = 0.023). The CNV also negatively interacted with oral contraceptive use on EOC risk (P = 0.042). Functional analyses further showed a lower mRNA level of WWOX in tissues with the 0-copy or 1-copy than that in those with the 2-copy (P = 0.045). Our data suggested the CNV-67048 to be a risk factor of EOC in Chinese women.
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49
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Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, Zheng J, Duggirala A, McArdle WL, Ho K, Ring SM, Evans DM, Davey Smith G, Relton CL. Systematic identification of genetic influences on methylation across the human life course. Genome Biol 2016; 17:61. [PMID: 27036880 PMCID: PMC4818469 DOI: 10.1186/s13059-016-0926-z] [Citation(s) in RCA: 387] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 03/15/2016] [Indexed: 12/13/2022] Open
Abstract
Background The influence of genetic variation on complex diseases is potentially mediated through a range of highly dynamic epigenetic processes exhibiting temporal variation during development and later life. Here we present a catalogue of the genetic influences on DNA methylation (methylation quantitative trait loci (mQTL)) at five different life stages in human blood: children at birth, childhood, adolescence and their mothers during pregnancy and middle age. Results We show that genetic effects on methylation are highly stable across the life course and that developmental change in the genetic contribution to variation in methylation occurs primarily through increases in environmental or stochastic effects. Though we map a large proportion of the cis-acting genetic variation, a much larger component of genetic effects influencing methylation are acting in trans. However, only 7 % of discovered mQTL are trans-effects, suggesting that the trans component is highly polygenic. Finally, we estimate the contribution of mQTL to variation in complex traits and infer that methylation may have a causal role consistent with an infinitesimal model in which many methylation sites each have a small influence, amounting to a large overall contribution. Conclusions DNA methylation contains a significant heritable component that remains consistent across the lifespan. Our results suggest that the genetic component of methylation may have a causal role in complex traits. The database of mQTL presented here provide a rich resource for those interested in investigating the role of methylation in disease. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0926-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Hashem A Shihab
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Geoff Woodward
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Oliver Lyttleton
- Avon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Aparna Duggirala
- Avon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Wendy L McArdle
- Avon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Karen Ho
- Avon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.,Avon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.,University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, 4102, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit (IEU) & School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.,Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
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50
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Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet 2016; 98:456-472. [PMID: 26924531 PMCID: PMC4827102 DOI: 10.1016/j.ajhg.2015.12.022] [Citation(s) in RCA: 226] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 12/31/2015] [Indexed: 01/13/2023] Open
Abstract
Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.
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Affiliation(s)
- Kevin J Galinsky
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Gaurav Bhatia
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Sayan Mukherjee
- Departments of Statistical Science, Computer Science, and Mathematics, Duke University, Durham, NC 27708, USA
| | - Nick J Patterson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
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