Nonparametric Reduced-Rank Regression for Multi-SNP, Multi-Trait Association Mapping

TL;DR

This paper introduces a nonparametric Bayesian reduced rank regression model for multi-SNP, multi-trait association mapping, enhancing power to detect genetic associations in high-dimensional phenotypes without pre-specifying the rank.

Contribution

It presents a novel nonparametric Bayesian approach that automatically determines the rank, improving detection of pleiotropic genetic effects in complex traits.

Findings

Improved statistical power over classical methods.

Identified pleiotropic QTLs missed by univariate tests.

Model effectively captures shared genetic influences across traits.

Abstract

Genome-wide association studies have proven to be essential for understanding the genetic basis of disease. However, many complex traits---personality traits, facial features, disease subtyping---are inherently high-dimensional, impeding simple approaches to association mapping. We developed a nonparametric Bayesian reduced rank regression model for multi-SNP, multi-trait association mapping that does not require the rank of the linear subspace to be specified. We show in simulations and real data that our model shares strength over SNPs and over correlated traits, improving statistical power to identify genetic associations with an interpretable, SNP-supervised low-dimensional linear projection of the high-dimensional phenotype. On the HapMap phase 3 gene expression QTL study data, we identify pleiotropic expression QTLs that classical univariate tests are underpowered to find and that two step approaches cannot recover. Our Python software, BERRRI, is publicly available at GitHub: https://github.com/ashlee1031/BERRRI.