遗传关联研究中严格多效性的检验程序
Testing Procedures for Strict Pleiotropy in Genetic Association Studies
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中文总结 AI 辅助
本文提出两种基于GWAS z分数的检验程序，用于识别严格多效性SNP并确定最少关联性状数，分别控制家族-wise错误率和FDR，并通过模拟及阿尔茨海默病生物标志物应用验证其性能。
中文摘要 AI 辅助
多效性，即单个遗传变异影响多个性状的现象，是基因组生物学的一个基本特征，也是统计遗传学中的一个重要考虑因素。许多统计方法利用多效性来增强关联检验的功效，但这些方法通常旨在识别与至少一个潜在性状相关联的SNP。目前，用于识别“严格多效性”（即与多个性状相关联的SNP）的方法数量有限，且这些方法要么局限于少量性状，要么随着性状数量的增加而变得计算上具有挑战性。在本文中，我们开发了两种检验程序来识别处于严格多效性下的SNP，并确定相应的最少关联性状数量。这两种方法仅依赖于全基因组关联研究（GWAS）中可获得的z分数，并适用于独立性状以及相关性状。第一种程序基于频率论，旨在控制家族-wise错误率。第二种程序基于局部错误发现率，在维持FDR控制的同时提供了更高的功效。我们进行了广泛的模拟研究，以比较所提出方法与用于检验严格多效性的替代方法的性能。最后，我们展示了所提出方法在阿尔茨海默病相关生物标志物上的应用，目的是区分可能代表生物标志物特异性变异的SNP与那些与疾病相关生物学有关的SNP。
英文摘要
Pleiotropy, the phenomenon in which a single genetic variant influences multiple traits, is a fundamental feature of genome biology and an important consideration in statistical genetics. Many statistical methods exist that leverage pleiotropy to increase the power of association tests, but they are often designed to identify SNPs which are associated with at least one of the underlying traits. Only a limited number of methods exist for identification of ``strict pleiotropy'', i.e. those associated with more than one trait, but these methods are either restricted to a small number of traits or become computationally challenging as the number of traits increases. In this paper, we develop two testing procedures to identify SNPs under strict pleiotropy and determine the corresponding minimum number of associated traits. Both methods rely only on z-scores available from genome-wide association studies (GWAS) and are applicable to independent as well as correlated traits. The first procedure is frequentist in nature and is designed to control the family-wise-error rate. The second procedure is based on the local false discovery rate and provides improved power while maintaining FDR control. We conduct extensive simulation studies to compare the performance of the proposed methods with alternative methods for testing strict pleiotropy. Finally, we demonstrate an application of the proposed methods to Alzheimer's disease-related biomarkers for the goal of distinguishing SNPs that may represent biomarker-specific variations from those that are related to disease-relevant biology.
