Understanding the population structure correction regression

TL;DR

This paper analyzes the statistical properties of the population structure correction method in GWAS and introduces a new corrected approach, CPC, which outperforms the standard PSC in various scenarios.

Contribution

The paper advances understanding of the PSC approach in GWAS and proposes the CPC correction, demonstrating its improved performance through theoretical analysis and simulations.

Findings

CPC is always comparable or better than PSC.

CPC shows dramatic improvements in certain cases.

Theoretical and simulation results support CPC's effectiveness.

Abstract

Although genome-wide association studies (GWAS) on complex traits have achieved great successes, the current leading GWAS approaches simply perform to test each genotype-phenotype association separately for each genetic variant. Curiously, the statistical properties for using these approaches is not known when a joint model for the whole genetic variants is considered. Here we advance in GWAS in understanding the statistical properties of the "population structure correction" (PSC) approach, a standard univariate approach in GWAS. We further propose and analyse a correction to the PSC approach, termed as "corrected population correction" (CPC). Together with the theoretical results, numerical simulations show that CPC is always comparable or better than PSC, with a dramatic improvement in some special cases.