Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

TL;DR

This paper analyzes the statistical properties of linear mixed models in GWAS, focusing on their sensitivity to candidate SNP inclusion and their effectiveness in correcting confounders like population stratification and environmental factors.

Contribution

It provides theoretical insights into the impact of including candidate SNPs in kinship matrices and compares how different methods handle confounders in GWAS.

Findings

Including candidate SNPs in kinship matrices introduces quantifiable errors.

Mixed models effectively correct for population stratification.

Different methods trade off confounder correction and computational efficiency.

Abstract

Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate SNP in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique in order to trade-off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors, population stratification and environmental confounding factors, and study how different methods that are commonly used in practice trade-off these two confounding factors differently.