Presenter: Zerui Zhang, PhD Candidate in Bioinformatics and Statistics

Title: Topics in Genomic Selection and Matrix Completion

Abstract:  Genomic selection in plant breeding is a common technique to explore molecular genetic markers and accumulate favorable ones to assist in trait selection. We aim to incorporate simulation-based operations research into decision making under uncertainties including recombination and environmental effects. In the first section of the presentation, we introduce a modular simulation platform to extend attention from additive effects into non-additive effects to handle hybrid breeding. We propose the idea of using the opaque simulator to mimic nature more realistically and highlight the differences and benefits brought by resource allocation.

In the second section, we present a latent feature model to complete a partially observed matrix. Inspired by neighbor-based collaborative filtering and model-based singular value decomposition, we define radial neighbors and distance measure approximation between latent feature vectors, so that we could pool information into kernel regression for the prediction. We provide theoretical results to show the consistency of all estimators, and both simulation and empirical studies show that the new method gives better prediction accuracy and recovery capability.