ArticleAmerican journal of human genetics2023
Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.
Article in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Gene-gene interactions between a LMNA variant and common polymorphisms drive early-onset atrial fibrillation.Nature communications · 2026Article
- Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.Biostatistics (Oxford, England) · 2025Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects.Nature genetics · 2025Article
- Exploring Random Forest in Genetic Risk Score Construction.Genetic epidemiology · 2025Article
- Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.Brain : a journal of neurology · 2025Article
- Epistasis regulates genetic control of cardiac hypertrophy.Nature cardiovascular research · 2025Article
- Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- Pleiotropy, epistasis and the genetic architecture of quantitative traits.Nature reviews. Genetics · 2024Review
- The 330 risk loci known for systemic lupus erythematosus (SLE): a review.Frontiers in lupus · 2024Article
- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
- Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.American journal of human genetics · 2023Article
- Learning gene networks under SNP perturbation using SNP and allele-specific expression data.bioRxiv : the preprint server for biology · 2023Article
- Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
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Authors and funding
19 authors.
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Abstract
Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.
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