A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

作者：Zhonghe Shao, Ting Wang, Jiahao Qiao, Yuchen Zhang, Shuiping Huang, Ping Zeng · 发表于：BMC Bioinformatics · 年份：2022 · DOI：10.1186/s12859-022-04897-3 · 被引用次数：14 · 研究领域：Genetic Associations and Epidemiology、Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals

BACKGROUND: Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS: We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording compu...