Variance Estimation and Confidence Intervals from High-dimensional Genome-wide Association Studies Through Misspecified Mixed Model Analysis

TL;DR

This paper develops practical methods for estimating variance and constructing confidence intervals for genetic effect parameters in high-dimensional GWAS, despite model misspecification, validated through simulations and real data.

Contribution

It introduces computationally feasible techniques for variance estimation and confidence interval construction in misspecified mixed models for GWAS.

Findings

Proposed methods accurately estimate variances in simulations.

Methods produce reliable confidence intervals in real GWAS data.

Performance is validated through Monte Carlo simulations.

Abstract

We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) misspecified mixed model analysis. Previous studies have shown that, in spite of the model misspecification, certain quantities of genetic interests are estimable, and consistent estimators of these quantities can be obtained using the restricted maximum likelihood (REML) method under a misspecified linear mixed model. However, the asymptotic variance of such a REML estimator is complicated and not ready to be implemented for practical use. In this paper, we develop practical and computationally convenient methods for estimating such asymptotic variances and constructing the associated confidence intervals. Performance of the proposed methods is evaluated empirically based on Monte-Carlo simulations and real-data application.