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Cui L, Yang B, Xiao S, Gao J, Baud A, Graham D, McBride M, Dominiczak A, Schafer S, Aumatell RL, Mont C, Teruel AF, Hübner N, Flint J, Mott R, Huang L. Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing. Genome Biol 2023; 24:215. [PMID: 37773188 PMCID: PMC10540365 DOI: 10.1186/s13059-023-03060-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/18/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively. RESULTS We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance. CONCLUSIONS Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.
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Affiliation(s)
- Leilei Cui
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- School of Life Sciences, Nanchang University, Nanchang, China
| | - Bin Yang
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Shijun Xiao
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Jun Gao
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China
| | - Amelie Baud
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Delyth Graham
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Martin McBride
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Anna Dominiczak
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Sebastian Schafer
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Regina Lopez Aumatell
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carme Mont
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Albert Fernandez Teruel
- Departamento de Psiquiatría y Medicina Legal, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Norbert Hübner
- Genetics and Genomics of Cardiovascular Diseases Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Center for Cardiovascular Research) Partner Site Berlin, Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jonathan Flint
- Department of Psychiatry and Behavioral Sciences, Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Richard Mott
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK.
| | - Lusheng Huang
- National Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China.
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Gregg JT, Himes BE, Asselbergs FW, Moore JH. Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.02.23293567. [PMID: 37577697 PMCID: PMC10418568 DOI: 10.1101/2023.08.02.23293567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Motivation Genome-Wide Association Studies (GWAS) commonly assume phenotypic and genetic homogeneity that is not present in complex conditions. We designed Transformative Regression Analysis of Combined Effects (TRACE), a GWAS methodology that better accounts for clinical phenotype heterogeneity and identifies gene-by-environment (GxE) interactions. We demonstrated with UK Biobank (UKB) data that TRACE increased the variance explained in All-Cause Heart Failure (AHF) via the discovery of novel single nucleotide polymorphism (SNP) and SNP-by-environment (i.e. GxE) interaction associations. First, we transformed 312 AHF-related ICD10 codes (including AHF) into continuous low-dimensional features (i.e., latent phenotypes) for a more nuanced disease representation. Then, we ran a standard GWAS on our latent phenotypes to discover main effects and identified GxE interactions with target encoding. Genes near associated SNPs subsequently underwent enrichment analysis to explore potential functional mechanisms underlying associations. Latent phenotypes were regressed against their SNP hits and the estimated latent phenotype values were used to measure the amount of AHF variance explained. Results Our method identified over 100 main GWAS effects that were consistent with prior studies and hundreds of novel gene-by-smoking interactions, which collectively accounted for approximately 10% of AHF variance. This represents an improvement over traditional GWAS whose results account for a negligible proportion of AHF variance. Enrichment analyses suggested that hundreds of miRNAs mediated the SNP effect on various AHF-related biological pathways. The TRACE framework can be applied to decode the genetics of other complex diseases. Availability All code is available at https://github.com/EpistasisLab/latent_phenotype_project.
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Affiliation(s)
- John T. Gregg
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Blanca E. Himes
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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