SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations

Shijia Bian, Andrew J. Bass, Yue Liu, Aliza P. Wingo, Thomas Wingo, David J. Cutler, Michael P. Epstein
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Abstract

Family-based heritability estimates of complex traits are often considerably larger than their single-nucleotide polymorphism (SNP) heritability estimates. This discrepancy may be due to non-additive effects of genetic variation, including variation that interacts with other genes or environmental factors to influence the trait. Variance-based procedures provide a computationally efficient strategy to screen for SNPs with potential interaction effects without requiring the specification of the interacting variable. While valuable, such variance-based tests consider only a single trait and ignore likely pleiotropy among related traits that, if present, could improve power to detect such interaction effects. To fill this gap, we propose SCAMPI (Scalable Cauchy Aggregate test using Multiple Phenotypes to test Interactions), which screens for variants with interaction effects across multiple traits. SCAMPI is motivated by the observation that SNPs with pleiotropic interaction effects induce genotypic differences in the patterns of correlation among traits. By studying such patterns across genotype categories among multiple traits, we show that SCAMPI has improved performance over traditional univariate variance-based methods. Like those traditional variance-based tests, SCAMPI permits the screening of interaction effects without requiring the specification of the interaction variable and is further computationally scalable to biobank data. We employed SCAMPI to screen for interacting SNPs associated with four lipid-related traits in the UK Biobank and identified multiple gene regions missed by existing univariate variance-based tests. SCAMPI is implemented in software for public use.
SCAMPI:利用跨性状相关性进行全基因组交互作用测试的可扩展统计框架
基于家系的复杂性状遗传率估计值往往大大高于其单核苷酸多态性(SNP)遗传率估计值。造成这种差异的原因可能是遗传变异的非加成效应,包括与其他基因或环境因素相互作用影响性状的变异。基于方差的程序提供了一种计算效率高的策略,用于筛选具有潜在交互效应的 SNP,而无需说明交互变量。虽然这种基于方差的检验很有价值,但它只考虑了单一性状,而忽略了相关性状之间可能存在的多效性。为了填补这一空白,我们提出了 SCAMPI(使用多表型测试交互作用的可扩展考奇聚合检验),它可以筛选出在多个性状中具有交互作用的变异体。SCAMPI 的原理是观察到具有多向交互效应的 SNPs 会导致性状间相关模式的基因型差异。通过研究多个性状间不同基因型类别的这种模式,我们发现 SCAMPI 比传统的基于单变量方差的方法有更好的表现。与那些传统的基于方差的检验方法一样,SCAMPI 允许筛选交互效应,而不需要指定交互变量,而且在计算上可进一步扩展到生物库数据。我们利用 SCAMPI 筛选了英国生物库中与四种脂质相关性状有关的互作 SNPs,并发现了现有基于单变量方差检验所遗漏的多个基因区域。SCAMPI 已在软件中实现,供公众使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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