  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 (SNP) data collected
from genome-wide association studies (GWAS) in plant and animal breeding and
multi-field trials. The manuscript explores the limitations of traditional
marker-assisted recurrent selection, highlighting the significance of
incorporating all estimated effects of marker loci into the statistical
framework and aiming to reduce the high dimensionality of GWAS 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.
