  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.
