TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

迄今为止，全基因组关联分析（Genome-Wide Association Study, GWAS）仍是目前用于识别引发表型变异的遗传变异的主流工具。由于GWAS分析本质上多为单变量形式，多变量表型信息通常会被简化为单一综合得分，此类操作常会导致检测因果变异的统计功效受损。尽管已存在多变量基因型-表型分析方法，但这类方法仅在特定场景下才能实现最优功效。
为此，我们受Li等人（2011）提出的GATES流程启发，提出了一种全新的多变量分析方法，命名为TATES（基于表型的关联检验：采用扩展赛姆斯检验流程，Trait-based Association Test that uses Extended Simes procedure）。针对多变量表型的各组成部分，TATES将标准单变量GWAS分析得到的P值进行整合，得到单一基于表型的P值，同时校正各组分间的相关性。
我们通过覆盖多种基因型-表型模型的大范围模拟实验证实，TATES的假阳性率控制准确；其检测可解释0.5%表型方差的因果变异的统计功效，相较基于综合得分的单变量检验可提升2.5至9倍，相较标准多元方差分析（Multivariate Analysis of Variance, MANOVA）可提升1.5至2倍。
与其他多变量分析方法不同，TATES既可识别对多种表型具有共同效应的遗传变异，也可识别仅对单一表型具有特异性的遗传变异，换言之，TATES能够更全面地展现复杂性状的遗传架构。
由于实际的因果基因型-表型模型通常未知，且往往在表型和遗传层面均具有复杂性，而TATES作为一款开源程序，为研究者提供了一种强大的新型多变量分析策略，能够让研究者在不受性状复杂性限制的前提下，识别出新的因果变异。