Fu Boyang, Pazokitoroudi Ali, Xue Albert, Anand Aakarsh, Anand Prateek, Zaitlen Noah, Sankararaman Sriram
Department of Computer Science, UCLA, Los Angeles, CA, USA.
Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
bioRxiv. 2023 Sep 12:2023.09.10.557084. doi: 10.1101/2023.09.10.557084.
The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value ). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.
上位性（基因或基因变体之间的相互作用）对人类复杂性状变异的贡献仍知之甚少。旨在明确识别与某一性状相关的基因变体对（通常是单核苷酸多态性，即SNP）的方法，由于测试的假设数量众多，功效较低，同时还必须处理在大量潜在候选对中进行搜索的计算问题。另一种方法是测试单个SNP在多基因背景下是否调节性状变异。虽然克服了功效低的局限性，但这种多基因或边际上位性（ME）测试在生物样本库规模的数据上是不可行的，因为在这些数据中，数十万个体针对数百万个SNP进行了基因分型。我们提出了一种适用于生物样本库规模数据的方法来测试SNP对某一性状的ME。我们进行了广泛的模拟，以表明我们的方法提供了校准的ME测试。我们将我们的方法应用于在英国生物样本库（UKBB）中约30万不相关的英国白人个体中，测试与53个数量性状相关的SNP的ME。测试了在全基因组关联研究（GWAS）中显著的15,601个性状-基因座关联，我们在12个性状中鉴定出16个性状-基因座对，这些对显示出ME信号的有力证据（p值）。我们进一步对全基因组中显著的ME信号进行划分，以识别出6个性状-基因座对具有局部（染色体内）ME的证据，而15个显示出远端（跨染色体）ME的证据。在这16个性状-基因座对中，我们记录到ME解释的性状变异比例平均约为GWAS效应解释的比例的12倍（范围：0.59至43.89）。我们的结果首次表明了个体基因变体与整体多基因背景之间相互作用效应调节复杂性状变异的证据。
