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Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nat Commun 2024; 15:6985. [PMID: 39143063 PMCID: PMC11324957 DOI: 10.1038/s41467-024-51075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Diptavo Dutta
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Samsiddhi Bhattacharjee
- Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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2
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Mbatchou J, McPeek MS. JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression. Am J Hum Genet 2024; 111:1750-1769. [PMID: 39025064 PMCID: PMC11339629 DOI: 10.1016/j.ajhg.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, NY 10591, USA; Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
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3
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability. HGG ADVANCES 2024; 5:100319. [PMID: 38872309 PMCID: PMC11260573 DOI: 10.1016/j.xhgg.2024.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France; Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France.
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4
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Jaso-Vera ME, Takaoka S, Patel I, Ruan X. Integrative regulation of hLMR1 by dietary and genetic factors in nonalcoholic fatty liver disease and hyperlipidemia. Hum Genet 2024; 143:897-906. [PMID: 38493444 DOI: 10.1007/s00439-024-02654-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 02/05/2024] [Indexed: 03/19/2024]
Abstract
Long non-coding RNA (lncRNA) genes represent a large class of transcripts that are widely expressed across species. As most human lncRNAs are non-conserved, we recently employed a unique humanized liver mouse model to study lncRNAs expressed in human livers. We identified a human hepatocyte-specific lncRNA, hLMR1 (human lncRNA metabolic regulator 1), which is induced by feeding and promotes hepatic cholesterol synthesis. Recent genome-wide association studies (GWAS) found that several single-nucleotide polymorphisms (SNPs) from the hLMR1 gene locus are associated with blood lipids and markers of liver damage. These results suggest that dietary and genetic factors may regulate hLMR1 to affect disease progression. In this study, we first screened for nutritional/hormonal factors and found that hLMR1 was robustly induced by insulin/glucose in cultured human hepatocytes, and this induction is dependent on the transcription factor SREBP1. We then tested if GWAS SNPs genetically linked to hLMR1 could regulate hLMR1 expression. We found that DNA sequences flanking rs9653945, a SNP from the last exon of the hLMR1 gene, functions as an enhancer that can be robustly activated by SREBP1c depending on the presence of rs9653945 major allele (G). We further performed CRISPR base editing in human HepG2 cells and found that rs9653945 major (G) to minor (A) allele modification resulted in blunted insulin/glucose-induced expression of hLMR1. Finally, we performed genotyping and gene expression analyses using a published human NAFLD RNA-seq dataset and found that individuals homozygous for rs9653945-G have a higher expression of hLMR1 and risk of NAFLD. Taken together, our data support a model that rs9653945-G predisposes individuals to insulin/glucose-induced hLMR1, contributing to the development of hyperlipidemia and NAFLD.
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Affiliation(s)
- Marcos E Jaso-Vera
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Fundamental Biomedical Research, Johns Hopkins All Childrens Hospital, 600 Fifth Street S., St. Petersburg, FL, 33701, USA
| | - Shohei Takaoka
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Fundamental Biomedical Research, Johns Hopkins All Childrens Hospital, 600 Fifth Street S., St. Petersburg, FL, 33701, USA
| | - Ishika Patel
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Fundamental Biomedical Research, Johns Hopkins All Childrens Hospital, 600 Fifth Street S., St. Petersburg, FL, 33701, USA
| | - Xiangbo Ruan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Institute for Fundamental Biomedical Research, Johns Hopkins All Childrens Hospital, 600 Fifth Street S., St. Petersburg, FL, 33701, USA.
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5
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Jin J, Qi G, Yu Z, Chatterjee N. Mendelian randomization analysis using multiple biomarkers of an underlying common exposure. Biostatistics 2024:kxae006. [PMID: 38459704 DOI: 10.1093/biostatistics/kxae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 11/16/2023] [Accepted: 02/05/2024] [Indexed: 03/10/2024] Open
Abstract
Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104-6021, United States
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, 720 Rutland Avenue, Baltimore, MD 21205, United States
- Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195-1617, United States
| | - Zhi Yu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 415 Main St Cambridge, MA 02142, United States
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States
- Department of Oncology, School of Medicine, Johns Hopkins University, 733 N Broadway, Baltimore, MD 21205, United States
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6
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Mbatchou J, McPeek MS. JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.571948. [PMID: 38187553 PMCID: PMC10769254 DOI: 10.1101/2023.12.18.571948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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7
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564319. [PMID: 37961722 PMCID: PMC10634875 DOI: 10.1101/2023.10.27.564319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
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8
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Ahles A, Engelhardt S. Genetic Variants of Adrenoceptors. Handb Exp Pharmacol 2023. [PMID: 37578621 DOI: 10.1007/164_2023_676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Adrenoceptors are class A G-protein-coupled receptors grouped into three families (α1-, α2-, and β-adrenoceptors), each one including three members. All nine corresponding adrenoceptor genes display genetic variation in their coding and adjacent non-coding genomic region. Coding variants, i.e., nucleotide exchanges within the transcribed and translated receptor sequence, may result in a difference in amino acid sequence thus altering receptor function and signaling. Such variants have been intensely studied in vitro in overexpression systems and addressed in candidate-gene studies for distinct clinical parameters. In recent years, large cohorts were analyzed in genome-wide association studies (GWAS), where variants are detected as significant in context with specific traits. These studies identified two of the in-depth characterized 18 coding variants in adrenoceptors as repeatedly statistically significant genetic risk factors - p.Arg389Gly in the β1- and p.Thr164Ile in the β2-adrenoceptor, along with 56 variants in the non-coding regions adjacent to the adrenoceptor gene loci, the functional role of which is largely unknown at present. This chapter summarizes current knowledge on the two coding variants in adrenoceptors that have been consistently validated in GWAS and provides a prospective overview on the numerous non-coding variants more recently attributed to adrenoceptor gene loci.
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Affiliation(s)
- Andrea Ahles
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany
| | - Stefan Engelhardt
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
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9
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Willis TW, Wallace C. Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context. PLoS Genet 2023; 19:e1010852. [PMID: 37585442 PMCID: PMC10461826 DOI: 10.1371/journal.pgen.1010852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 08/28/2023] [Accepted: 06/30/2023] [Indexed: 08/18/2023] Open
Abstract
Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.
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Affiliation(s)
- Thomas W. Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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10
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Baltramonaityte V, Pingault JB, Cecil CAM, Choudhary P, Järvelin MR, Penninx BWJH, Felix J, Sebert S, Milaneschi Y, Walton E. A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity. PLoS Genet 2023; 19:e1010508. [PMID: 37390107 DOI: 10.1371/journal.pgen.1010508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.
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Affiliation(s)
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational, and Health Psychology, University College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Marjo-Riitta Järvelin
- Research Unit of Population Health, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Janine Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, University of Oulu, Oulu, Finland
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, United Kingdom
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11
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Benca-Bachman CE, Bubier J, Syed RA, Romero Villela PN, Palmer RHC. Polygenic influences on the behavioral effects of alcohol withdrawal in a mixed-ancestry population from the collaborative study on the genetics of alcoholism (COGA). Mol Cell Neurosci 2023; 125:103851. [PMID: 37031923 PMCID: PMC10315187 DOI: 10.1016/j.mcn.2023.103851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 04/11/2023] Open
Abstract
Alcohol withdrawal (AW) is a feature of alcohol use disorder that may occur in up to half of individuals with chronic, heavy alcohol consumption whenever alcohol use is abruptly stopped or significantly reduced. To date, few genes have been robustly associated with AW; this may be partly due to most studies defining AW as a binary construct despite the multiple symptoms and their range in severity from mild to severe. The current study examined the effects of genome-wide loci on a factor score for AW in high risk and community family samples in the Collaborative Study for the Genetics of Alcoholism (COGA). In addition, we tested whether differentially expressed genes associated with alcohol withdrawal in model organisms are enriched in human genome-wide association study (GWAS) effects. Analyses employed roughly equal numbers of males and females (mean age 35, standard deviation = 15; total N = 8009) and included individuals from multiple ancestral backgrounds. Genomic data were imputed to the HRC reference panel and underwent strict quality control procedures using Plink2. Analyses controlled for age, sex, and population stratification effects using ancestral principal components. We found support that AW is a polygenic disease (SNP-heritability = 0.08 [95 % CI = 0.01, 0.15; pedigree-based heritability = 0.12 [0.08,0.16]. We identified five single nucleotide variants that met genomewide significance, some of which have previously been associated with alcohol phenotypes. Gene-level analyses suggest a role for COL19A1 in AW; H-MAGMA analyses implicated 12 genes associated with AW. Cross-species enrichment analyses indicated that variation within genes identified in model organism studies explained <1 % of the phenotypic variability in human AW. Notably, the surrounding regulatory regions of model organism genes explained more variance than expected by chance, indicating that these regulatory regions and gene sets may be important for human AW. Lastly, when comparing the overlap in genes identified from the human GWAS and H-MAGMA analyses with the genes identified from the animal studies, there was modest overlap, indicating some convergence between the methods and organisms.
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Affiliation(s)
- Chelsie E Benca-Bachman
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Providence Veterans Affairs Medical Center, Providence, RI 02908, USA
| | | | - Rameez A Syed
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Pamela N Romero Villela
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Institute for Behavior Genetics, University of Colorado of Boulder, Boulder, CO 80309, USA; Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
| | - Rohan H C Palmer
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Providence Veterans Affairs Medical Center, Providence, RI 02908, USA; Jackson Laboratory, Bar Harbor, ME 04609, USA.
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12
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Taraszka K, Zaitlen N, Eskin E. Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations. PLoS Genet 2022; 18:e1010447. [PMID: 36342933 PMCID: PMC9671458 DOI: 10.1371/journal.pgen.1010447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 11/17/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022] Open
Abstract
We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
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Affiliation(s)
- Kodi Taraszka
- Department of Computer Science, University of California, Los Angeles, California, United States of America
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- * E-mail:
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13
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Smith CJ, Sinnott-Armstrong N, Cichońska A, Julkunen H, Fauman EB, Würtz P, Pritchard JK. Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. eLife 2022; 11:e79348. [PMID: 36073519 PMCID: PMC9536840 DOI: 10.7554/elife.79348] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/15/2022] Open
Abstract
Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.
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Affiliation(s)
- Courtney J Smith
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
| | - Nasa Sinnott-Armstrong
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | | | | | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalCambridgeUnited States
| | | | - Jonathan K Pritchard
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
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14
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Ballard JL, O'Connor LJ. Shared components of heritability across genetically correlated traits. Am J Hum Genet 2022; 109:989-1006. [PMID: 35477001 PMCID: PMC9247834 DOI: 10.1016/j.ajhg.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/01/2022] [Indexed: 11/01/2022] Open
Abstract
Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed pleiotropic decomposition regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of five to six traits genetically correlated with coronary artery disease (CAD), asthma, and type II diabetes (T2D), producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension, and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes (compared with the original summary statistics) by 94% and 70% for asthma and T2D out of sample, respectively, and by a predicted 300% for CAD.
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Affiliation(s)
- Jenna Lee Ballard
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Luke Jen O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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15
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Cao X, Wang X, Zhang S, Sha Q. Gene-based association tests using GWAS summary statistics and incorporating eQTL. Sci Rep 2022; 12:3553. [PMID: 35241742 PMCID: PMC8894384 DOI: 10.1038/s41598-022-07465-0] [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: 06/10/2021] [Accepted: 02/11/2022] [Indexed: 01/29/2023] Open
Abstract
Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA.
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16
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Pyne T, Ghosh P, Dhauria M, Ganguly K, Sengupta D, Nandagopal K, Sengupta M, Das M. Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being. J Psychiatr Res 2021; 145:92-101. [PMID: 34883412 DOI: 10.1016/j.jpsychires.2021.11.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/16/2021] [Accepted: 11/21/2021] [Indexed: 11/23/2022]
Abstract
Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as "Well-being spectrum". Genome-Wide Association studies (GWA studies) on "well-being measurement" have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on "well-being measurement", we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.
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Affiliation(s)
- Tushar Pyne
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Poulomi Ghosh
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Mrinmay Dhauria
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Kausik Ganguly
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Debmalya Sengupta
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Krishnadas Nandagopal
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
| | - Mainak Sengupta
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India.
| | - Madhusudan Das
- Department of Zoology, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, West Bengal, 700019, India.
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17
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Julienne H, Laville V, McCaw ZR, He Z, Guillemot V, Lasry C, Ziyatdinov A, Nerin C, Vaysse A, Lechat P, Ménager H, Le Goff W, Dube MP, Kraft P, Ionita-Laza I, Vilhjálmsson BJ, Aschard H. Multitrait GWAS to connect disease variants and biological mechanisms. PLoS Genet 2021; 17:e1009713. [PMID: 34460823 PMCID: PMC8437297 DOI: 10.1371/journal.pgen.1009713] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 09/13/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.
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Affiliation(s)
- Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Zachary R. McCaw
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Vincent Guillemot
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Carla Lasry
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Andrey Ziyatdinov
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Cyril Nerin
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Amaury Vaysse
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Pierre Lechat
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Hervé Ménager
- Department of Computational Biology, Institut Pasteur, Paris, France
| | - Wilfried Le Goff
- Sorbonne Université, INSERM, Institute of Cardiometabolism and Nutrition (ICAN), UMR_S 1166, Paris, France
| | - Marie-Pierre Dube
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, Canada
- Université de Montréal, Faculty of Medicine, Department of medicine, Université de Montréal, Montreal, Canada
| | - Peter Kraft
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, New York, United States of America
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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18
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Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat Genet 2021; 53:445-454. [PMID: 33686288 PMCID: PMC8038973 DOI: 10.1038/s41588-021-00787-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 01/14/2021] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case-case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case-control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case-case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case-control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.
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19
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Moccia C, Popovic M, Isaevska E, Fiano V, Trevisan M, Rusconi F, Polidoro S, Richiardi L. Birthweight DNA methylation signatures in infant saliva. Clin Epigenetics 2021; 13:57. [PMID: 33741061 PMCID: PMC7980592 DOI: 10.1186/s13148-021-01053-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/09/2021] [Indexed: 11/17/2022] Open
Abstract
Background Low birthweight has been repeatedly associated with long-term adverse health outcomes and many non-communicable diseases. Our aim was to look-up cord blood birthweight-associated CpG sites identified by the PACE Consortium in infant saliva, and to explore saliva-specific DNA methylation signatures of birthweight. Methods DNA methylation was assessed using Infinium HumanMethylation450K array in 135 saliva samples collected from children of the NINFEA birth cohort at an average age of 10.8 (range 7–17) months. The association analyses between birthweight and DNA methylation variations were carried out using robust linear regression models both in the exploratory EWAS analyses and in the look-up of the PACE findings in infant saliva. Results None of the cord blood birthweight-associated CpGs identified by the PACE Consortium was associated with birthweight when analysed in infant saliva. In saliva EWAS analyses, considering a false discovery rate p-values < 0.05, birthweight as continuous variable was associated with DNA methylation in 44 CpG sites; being born small for gestational age (SGA, lower 10th percentile of birthweight for gestational age according to WHO reference charts) was associated with DNA methylation in 44 CpGs, with only one overlapping CpG between the two analyses. Despite no overlap with PACE results at the CpG level, two of the top saliva birthweight CpGs mapped at genes associated with birthweight with the same direction of the effect also in the PACE Consortium (MACROD1 and RPTOR). Conclusion Our study provides an indication of the birthweight and SGA epigenetic salivary signatures in children around 10 months of age. DNA methylation signatures in cord blood may not be comparable with saliva DNA methylation signatures at about 10 months of age, suggesting that the birthweight epigenetic marks are likely time and tissue specific. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01053-1.
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Affiliation(s)
- Chiara Moccia
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy.
| | - Maja Popovic
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy
| | - Elena Isaevska
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy
| | - Valentina Fiano
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy
| | - Morena Trevisan
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy
| | - Franca Rusconi
- Unit of Epidemiology, 'Anna Meyer' Children's University Hospital, Florence, Italy
| | - Silvia Polidoro
- Italian Institute for Genomic Medicine (IIGM), Candiolo, Italy.,MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy
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20
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Raushan K, Benberin V, Vochshenkova T, Babenko D, Sibagatova A. Association of 3 single nucleotide polymorphisms of the eighth chromosome with remodeling of the myocardium and carotid arteries in the Kazakh population. Medicine (Baltimore) 2021; 100:e24608. [PMID: 33578567 PMCID: PMC7886467 DOI: 10.1097/md.0000000000024608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/25/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT Cardiovascular diseases are one of the key health issues in Kazakhstan. According to the WHO, the prevalence of arterial hypertension (AH) was 28% in males and 25% in females in 2015, which puts up vastly to premature mortality from non-communicable diseases.The search for genetic features of target organ lesions processes in AH is relevant. The goal of this study was to search for the genetic markers of myocardial remodeling (MR) and carotid artery remodeling (CAR).A total of 866 hypertensive individuals were recruited in Nur-Sultan, Kazakhstan. Their blood was genotyped for 9 single nucleotide polymorphisms (SNPs) of the eighth chromosome to find an association with remodeling. The analysis was carried out in the group pairs (control and CAR, control and MR, and control and CAR and MR). The genotype-phenotype association was assessed using 5 different inheritance models: dominant, codominant, recessive, overdominant, and log-additive.Statistically significant results were found for 3 SNPs (rs2407103, rs11775334, rs2071518) which minor alleles enlarged risks of MR and CAR in AH in the studied population. Three polymorphisms have previously been associated with АН and some other traits like pulse pressure and blood glucose in other ethnic populations: rs2407103 - in Afro-American population, rs11775334 - in the European population, rs2071518 is well studied in various ethnic populations (European, South Asian, Afro-American, Hispanic, East Asian).
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Affiliation(s)
- Karabayeva Raushan
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | - Valeriy Benberin
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | - Tamara Vochshenkova
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
| | | | - Ainur Sibagatova
- Medical Centre Hospital of President's Affairs Administration of the Republic of Kazakhstan
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21
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Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets. BMC Bioinformatics 2021; 22:50. [PMID: 33546598 PMCID: PMC7866684 DOI: 10.1186/s12859-021-03959-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. RESULTS In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratification, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimer's disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM .
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22
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Magri C, Giacopuzzi E, Sacco C, Bocchio-Chiavetto L, Minelli A, Gennarelli M. Alterations observed in the interferon α and β signaling pathway in MDD patients are marginally influenced by cis-acting alleles. Sci Rep 2021; 11:727. [PMID: 33436853 PMCID: PMC7804189 DOI: 10.1038/s41598-020-80374-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 12/17/2020] [Indexed: 11/09/2022] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder with a multifactorial aetiology determined by the interaction between genetic and environmental risk factors. Pieces of evidence indicate that inflammation and immune activation may contribute to the onset of MDD playing a role in the pathogenetic mechanism. To date, it is not known to which extent the association between MDD and inflammation is shaped by the genetic background or by the presence of environmental factors. To clarify this issue, we analyzed genotype and blood RNA profiles of 463 MDD cases and 459 controls (NIMH-Study 88/Site621) estimating the Genetic and Environmental Regulated eXpression component of gene expression (GReX and EReX respectively). Both components were tested for association with MDD. Many genes belonging to the α/β interferon signaling pathway showed an association between MDD and EReX, only two between MDD and GReX. Also other MDD differentially expressed genes were more influenced by the EReX than by GReX. These results suggest that impact of the genetic background on MDD blood gene expression alterations is much lower than the contribution of environmental factors and almost absent for the genes of the interferon pathway.
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Affiliation(s)
- Chiara Magri
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
| | - Edoardo Giacopuzzi
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Chiara Sacco
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Luisella Bocchio-Chiavetto
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Faculty of Psychology, eCampus University, Novedrate, Como, Italy
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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23
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Chen TH, Chatterjee N, Landi MT, Shi J. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc 2020; 116:133-143. [PMID: 34483403 DOI: 10.1080/01621459.2020.1764849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training data set for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a comprehensive penalized regression for fitting l 1 regularized regression models to GWAS summary statistics. We propose incorporating Pleiotropy and ANnotation information into PRS (PANPRS) development through suitable formulation of penalty functions and associated tuning parameters. Extensive simulations show that PANPRS performs equally well or better than existing PRS methods when no functional annotation or pleiotropy is incorporated. When functional annotation data and pleiotropy are informative, PANPRS substantially outperforms existing PRS methods in simulations. Finally, we applied our methods to build PRS for type 2 diabetes and melanoma and found that incorporating relevant functional annotations and GWAS of genetically related traits improved prediction of these two complex diseases.
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Affiliation(s)
- Ting-Huei Chen
- Department of Mathematics and Statistics, Regular member, Cervo Brain Research Centre, University of Laval, 1045, av. of Medicine, Suite 1056, Quebec G1V 0A6, Canada
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Baltimore, Maryland, United States of America, 615 N Wolfe Street Baltimore, MD 21205
| | - Maria Teresa Landi
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E106, Bethesda, MD, 20892
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E122, Bethesda, MD, 20892
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24
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Wu C. Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank. Genetics 2020; 215:947-958. [PMID: 32540950 PMCID: PMC7404235 DOI: 10.1534/genetics.120.303242] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 06/09/2020] [Indexed: 01/08/2023] Open
Abstract
Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.
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Affiliation(s)
- Chong Wu
- Department of Statistics, Florida State University, Tallahassee, Florida 32306
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25
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Julienne H, Lechat P, Guillemot V, Lasry C, Yao C, Araud R, Laville V, Vilhjalmsson B, Ménager H, Aschard H. JASS: command line and web interface for the joint analysis of GWAS results. NAR Genom Bioinform 2020; 2:lqaa003. [PMID: 32002517 PMCID: PMC6978790 DOI: 10.1093/nargab/lqaa003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 12/03/2019] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.
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Affiliation(s)
- Hanna Julienne
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Pierre Lechat
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Vincent Guillemot
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Carla Lasry
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Chunzi Yao
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Robinson Araud
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Vincent Laville
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Bjarni Vilhjalmsson
- National Center for Register-Based Research, Aarhus University, DK-8210 Aarhus, Denmark
| | - Hervé Ménager
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
| | - Hugues Aschard
- Department of Computational Biology—USR 3756 CNRS, Institut Pasteur, 75015 Paris, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 02115 Boston, MA, USA
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26
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Effect of non-normality and low count variants on cross-phenotype association tests in GWAS. Eur J Hum Genet 2019; 28:300-312. [PMID: 31582815 DOI: 10.1038/s41431-019-0514-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 09/01/2019] [Accepted: 09/05/2019] [Indexed: 01/21/2023] Open
Abstract
Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious association signals. Even after controlling for confounders that may contribute to non-normality and then applying inverse normal transformation on the residuals of each trait, these tests may have inflated type I errors for variants with low minor allele counts (MACs). A likelihood ratio test of association based on the ordinal regression of individual-level genotype conditional on the traits seems to be the least biased and can maintain type I error when the MAC is reasonably large (e.g., MAC > 30). Application of these methods to publicly available summary statistics of eight amino acid traits on European samples seem to exhibit systematic inflation (especially for variants with low MAC), which is consistent with our findings from simulation experiments.
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