Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Av. Diagonal 643, Barcelona, 08028, Spain.
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.
Genome Biol. 2023 Oct 12;24(1):230. doi: 10.1186/s13059-023-03076-8.
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.
多维表型数据在大量基因分型个体中的可用性不断增加，这就需要有效的方法来识别遗传对多种性状的影响。置换多元方差分析 (PERMANOVA) 提供了一种强大的非参数方法。然而，它依赖于置换来评估显著性，这阻碍了对大型数据集的分析。在这里，我们推导出 PERMANOVA 检验统计量的极限 null 分布，为快速计算渐近 p 值提供了一个框架。我们的渐近检验具有受控的Ⅰ型错误和高功效，通常优于参数方法。我们在 QTL 映射和 GWAS 的背景下说明了它的适用性。
