Department of Computer Science, University of California, Los Angeles, California, United States of America.
Department of Neurology, University of California, Los Angeles, California, United States of America.
PLoS Genet. 2022 Nov 7;18(11):e1010447. doi: 10.1371/journal.pgen.1010447. eCollection 2022 Nov.
We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
我们介绍了一种多效关联测试（PAT）方法，用于使用全基因组关联研究（GWAS）汇总统计数据联合分析多个性状。该方法利用表型变异的遗传和环境成分分解来为每个遗传变异创建似然比检验统计量。尽管 PAT 不能直接解释哪些性状驱动关联，但通过扩展到荟萃分析框架 m 值，提供了对总检验 p 值的逐性状解释。在模拟中，我们表明 PAT 控制了假阳性率，提高了统计功效，并且对遗传效应的模型误设定具有鲁棒性。此外，比较 PAT 与三种多性状方法（HIPO、MTAG 和 ASSET）的模拟显示，PAT 比下一个最佳方法多发现了 15.3%的总关联。当使用 m 值对这些关联进行逐性状解释时，PAT 有 37.5%的真实逐性状解释，假阳性分配率为 0.92%。当分析来自英国生物库的四个性状时，PAT 发现了 22,095 个新变体。通过 m 值解释框架，两个性状的每个性状关联的数量几乎增加了两倍，另一个性状的每个性状关联的数量几乎增加了一倍，与原始的单个性状 GWAS 相比。
