Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits

Abstract

Genome-wide association studies (GWAS) of high-dimensional traits, such as molecular phenotypes or imaging features, often use univariate approaches, ignoring information from related traits. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through GWAS of a smaller number of latent factors from factor analysis. Here, we introduce a zero-correlation multi-trait fine-mapping approach, flashfmZero, for any number of latent factors. In our application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show how GWAS of latent factors enables detection of signals that have sub-threshold associations with several blood cell traits. FlashfmZero resulted in 99% credible sets with the same size or fewer variants than those for blood cell traits in 87% of our comparisons, and all latent trait fine-mapping credible sets were subsets of those from flashfmZero. These analysis techniques give enhanced power for discovery and fine-mapping for many traits.