Robust genomic prediction and heritability estimation using density power divergence

Abstract

This manuscript delves into the intersection of genomics and phenotypic prediction, focusing on the statistical innovation required to navigate the complexities introduced by noisy covariates and confounders. The primary emphasis is on the development of advanced robust statistical models tailored for genomic prediction from single nucleotide polymorphism data in plant and animal breeding and multi-field trials. The manuscript highlights the significance of incorporating all estimated effects of marker loci into the statistical framework and aiming to reduce the high dimensionality of data while preserving critical information. This paper introduces a new robust statistical framework for genomic prediction, employing one-stage and two-stage linear mixed model analyses along with utilizing the popular robust minimum density power divergence estimator (MDPDE) to estimate genetic effects on phenotypic traits. The study illustrates the superior performance of the proposed MDPDE-based genomic prediction and associated heritability estimation procedures over existing competitors through extensive empirical experiments on artificial datasets and application to a real-life maize breeding dataset. The results showcase the robustness and accuracy of the proposed MDPDE-based approaches, especially in the presence of data contamination, emphasizing their potential applications in improving breeding programs and advancing genomic prediction of phenotyping traits.