Disentangling Linkage and Population Structure in Association Mapping

TL;DR

This paper investigates the relationship between surrogate models used in GWAS and the true causal models, revealing how population structure and environmental confounding are addressed through genetic covariates.

Contribution

It establishes the theoretical connection between surrogate models and true causal models in GWAS, clarifying the role of population structure and correction methods.

Findings

Population structure is modeled via variants, not traits.

Environmental confounding can be partially corrected with genetic covariates.

The link between PC correction and linear mixed models is clarified.

Abstract

Genome-wide association study (GWAS) tests single nucleotide polymorphism (SNP) markers across the genome to localize the underlying causal variant of a trait. Because causal variants are seldom observed directly, a surrogate model based on genotyped markers are widely considered. Although many methods estimating the parameters of the surrogate model have been proposed, the connection between the surrogate model and the true causal model is yet investigated. In this work, we establish the connection between the surrogate model and the true causal model. The connection shows that population structure is accounted in GWAS by modelling the variant of interest and not the trait. Such observation explains how environmental confounding can be partially corrected using genetic covariates and why the previously claimed connection between PC correction and linear mixed models is incorrect.