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We study variance estimation and associated confidence intervals forparameters characterizing genetic effects from genome-wide association studies(GWAS) misspecified mixed model analysis. Previous studies have shown that, inspite of the model misspecification, certain quantities of genetic interestsare estimable, and consistent estimators of these quantities can be obtainedusing the restricted maximum likelihood (REML) method under a misspecifiedlinear mixed model. However, the asymptotic variance of such a REML estimatoris complicated and not ready to be implemented for practical use. In thispaper, we develop practical and computationally convenient methods forestimating such asymptotic variances and constructing the associated confidenceintervals. Performance of the proposed methods is evaluated empirically basedon Monte-Carlo simulations and real-data application.

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