A Proximal Multi-Objective Optimization Method for Incorporation of Polygenic Breeding Values in Genomic Prediction

Traditional quantitative genetics rely on known pedigrees for estimating polygenic breeding values and variance components. With the advent of high-throughput sequencing, estimation of empirical relationships between individuals have become feasible. The single-step genomic BLUP procedure integrates polygenic and genomic information but requires intricate pedigree connections, leading to computationally demanding matrix operations. Here, we propose DYpREG, a flexible proximal operator multi-objective regularization method. DYpREG is a splitting algorithm that accommodates two distinct loss functions and a regularization function that can be chosen as LASSO, ridge regression (RR) or elastic net (EN). Notably, it incorporates pre-calculated breeding values without the need for extensive relationship matrix computations. Evaluated on two traits with different heritabilities from mice and pig, DYpREG with RR regularizer showed the best predictive performance compared to DYpREG with LASSO and EN regularizers as well as to all methods lacking polygenic information. The mean squared error (MSE) reduction was notable for both mice traits (1.5% and 24.77%) and both pig traits (2.8% and 2.1%). Comparative analyses with Bayesian reproducing kernel Hilbert space (RKHS) and spike-and-slab (BayesC) regression also revealed consistent improvements. Hence, DYpREG effectively integrates polygenic information, enhancing prediction accuracy in genomic evaluations.