A1027
Title: Semiparametric efficient estimation of genetic relatedness with double machine learning
Authors: Niwen Zhou - Beijing Normal University (China) [presenting]
Abstract: Double machine learning procedures are proposed to estimate genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model misspecification may yield misleading statistical results. Moreover, the semiparametric efficient bounds for estimators of genetic relatedness are still lacking. Semi-parametric efficient and model-free estimators are developed, and valid confidence intervals are constructed for two important measures of genetic relatedness: genetic covariance and genetic correlation, allowing both continuous and discrete responses. Based on the derived efficient influence functions of genetic relatedness, a consistent estimator of the genetic covariance is proposed as long as one of the genetic values is consistently estimated. The data of two traits may be collected from the same group or different groups of individuals. Various numerical studies are performed to illustrate the introduced procedures. Also proposed procedures are applied to analyze Carworth Farms White mice genome-wide association study data.
