Accurate Liability Estimation Improves Power in Ascertained Case Control Studies

TL;DR

This paper introduces LEAP, a framework that estimates liability as a phenotype to enhance power in ascertained case-control GWAS, addressing limitations of traditional linear mixed models in non-random samples.

Contribution

The paper presents LEAP, a novel method for liability estimation that improves association testing power in non-random case-control studies.

Findings

LEAP significantly increases power in ascertained case-control studies.

Liability estimation reduces confounding effects in GWAS.

The framework is publicly available at https://github.com/omerwe/LEAP.

Abstract

Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (Liability Estimator As a Phenotype, https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and demonstrate that this can lead to a substantial power increase.