Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA.
Department of Biostatistics, Columbia University, New York, New York, USA.
Genet Epidemiol. 2021 Jun;45(4):413-424. doi: 10.1002/gepi.22379. Epub 2021 Feb 10.
Although genome-wide association studies have been widely used to identify associations between complex diseases and genetic variants, standard single-variant analyses often have limited power when applied to rare variants. To overcome this problem, set-based methods have been developed with the aim of boosting power by borrowing strength from multiple rare variants. We propose the adaptive hierarchically structured variable selection (HSVS-A) before test for association of rare variants in a set with continuous or dichotomous phenotypes and to estimate the effect of individual rare variants simultaneously. HSVS-A has the flexibility to integrate a pairwise weighting scheme, which adaptively induces desirable correlations among variants of similar significance such that we can borrow information from potentially causal and noncausal rare variants to boost power. Simulation studies show that for both continuous and dichotomous phenotypes, HSVS-A is powerful when there are multiple causal rare variants, either in the same or opposite direction of effect, with the presence of a large number of noncausal variants. We also apply HSVS-A to the Wellcome Trust Case Control Consortium Crohn's disease data for testing the association of Crohn's disease with rare variants in pathways. HSVS-A identifies two pathways harboring novel protective rare variants for Crohn's disease.
虽然全基因组关联研究已被广泛用于鉴定复杂疾病与遗传变异之间的关联，但标准的单变量分析在应用于稀有变异时往往效力有限。为了克服这个问题，已经开发了基于集合的方法，旨在通过从多个稀有变异中借取力量来提高效力。我们提出了适应性层次结构变量选择（HSVS-A），用于在连续或二分类表型的集合中对稀有变异与关联进行检验，并同时估计个体稀有变异的效应。HSVS-A 具有灵活性，可以集成一种两两加权方案，自适应地诱导相似重要性的变异之间产生理想的相关性，从而使我们能够从潜在的因果和非因果稀有变异中借取信息，以提高效力。模拟研究表明，对于连续和二分类表型，当存在多个因果稀有变异（无论是相同还是相反方向的效应）且存在大量非因果变异时，HSVS-A 具有强大的效力。我们还将 HSVS-A 应用于惠康信托基金会病例对照联盟的克罗恩病数据，以检验克罗恩病与途径中的稀有变异的关联。HSVS-A 确定了两个含有克罗恩病新型保护稀有变异的途径。
