City Research Online

Abstract

Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell traits in 87% of our comparisons. In all cases univariate latent factor CS99 contained those from flashfmZero. Our latent factor approaches can be applied to GWAS summary statistics and will enhance power for the discovery and fine-mapping of associations for many traits.

Publication Type:	Article
Additional Information:	This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords:	blood cell traits, GWAS, factor analysis, fine-mapping, latent factors, multi-trait
Subjects:	H Social Sciences > HA Statistics Q Science > QH Natural history > QH301 Biology
Departments:	Bayes Business School
SWORD Depositor:	Symplectic Administrator

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