Abstract
AbstractThe 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. We show that the asymptotic test presents controlled type I error and high power, comparable to or higher than parametric approaches. We illustrate the applicability of our method in a number of use-cases. Using the GTEx cohort, we perform the first population-biased splicing QTL mapping study across multiple tissues. We identify thousands of genetic variants that affect alternative splicing differently depending on ethnicity, including potential disease markers. Using the UK Biobank cohort, we perform the largest GWAS to date of MRI-derived volumes of hippocampal subfields. Most of the identified loci have not been previously related to the hippocampus, but many are associated to cognition or brain disorders, thus contributing to understand the intermediate traits through which genetic variants impact complex organismal phenotypes.
Publisher
Cold Spring Harbor Laboratory
Cited by
2 articles.
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