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<p>The contribution of genetic interactions (epistasis) to human complex trait variation remains poorly understood due, in part, to the statistical and computational challenges involved in testing for interaction effects. Here we introduce FAME (FAst Marginal Epistasis test), a method that can test for marginal epistasis of a single-nucleotide polymorphism (SNP) on a quantitative trait (whether the effect of an SNP on the trait is modulated by genetic background). FAME is computationally efficient, enabling tests of marginal epistasis on biobank-scale data. Applying FAME to genome-wide association study (GWAS)-significant trait-SNP associations across 53 quantitative traits and ≈300 000 unrelated White British individuals in the UK Biobank (UKBB), we identified 16 significant marginal epistasis signals across 12 traits (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(P &lt; \frac{5\times {10}^{-8}}{53}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>P</mi> <mo>&lt;</mo> <mfrac> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> </mrow> <mrow> <mn>53</mn> </mrow> </mfrac> </mrow> </math></EquationSource> </InlineEquation>). Leveraging the scalability of FAME, we further localized marginal epistasis signals across chromosomes and estimated the proportion of variance explained by marginal epistasis effects. Our study provides evidence for interactions between individual genetic variants and polygenic background influencing complex traits.</p>

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A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Boyang Fu,
  • Ali Pazokitoroudi,
  • Zhuozheng Shi,
  • Asha Kar,
  • Albert Xue,
  • Aakarsh Anand,
  • Prateek Anand,
  • Zhengtong Liu,
  • Richard Border,
  • Päivi Pajukanta,
  • Noah Zaitlen,
  • Sriram Sankararaman

摘要

The contribution of genetic interactions (epistasis) to human complex trait variation remains poorly understood due, in part, to the statistical and computational challenges involved in testing for interaction effects. Here we introduce FAME (FAst Marginal Epistasis test), a method that can test for marginal epistasis of a single-nucleotide polymorphism (SNP) on a quantitative trait (whether the effect of an SNP on the trait is modulated by genetic background). FAME is computationally efficient, enabling tests of marginal epistasis on biobank-scale data. Applying FAME to genome-wide association study (GWAS)-significant trait-SNP associations across 53 quantitative traits and ≈300 000 unrelated White British individuals in the UK Biobank (UKBB), we identified 16 significant marginal epistasis signals across 12 traits ( \(P < \frac{5\times {10}^{-8}}{53}\) P < 5 × 10 − 8 53 ). Leveraging the scalability of FAME, we further localized marginal epistasis signals across chromosomes and estimated the proportion of variance explained by marginal epistasis effects. Our study provides evidence for interactions between individual genetic variants and polygenic background influencing complex traits.