Haplotypes and Machine Learning Improve Genomic Prediction in Brassica napus
TL;DR
While haplotypes did not significantly boost prediction accuracy over SNPs, they captured novel genetic variation and offered a broader diversity of variants for selection, suggesting a qualitative advantage for long‐term breeding goals.
Abstract
Genomic selection (GS) is a crop and livestock improvement method suited for predicting complex agronomical traits, while genomic prediction (GP) is the development of GS models prior to their practical use in breeding programs. One of the challenges in GP is accounting for how complex genomic interactions, such as epistasis, affect the resulting phenotype. Incorporating haplotypes and Machine Learning (ML) into GP models are two methods for accounting for local epistasis and non‐linear relationships. This study compared linear‐ and ML‐GP models for single nucleotide polymorphisms (SNPs) and haplotypes in Brassica napus. A publicly available dataset of 991 B. napus individuals, 4 286 896 SNPs, and the traits flowering time, oil content and oleic acid content was used for all GP models. The ML models improved trait prediction accuracy for all tested traits in both SNP‐ and haplotype‐based GP. While haplotypes did not significantly boost prediction accuracy over SNPs, they captured novel genetic variation and offered a broader diversity of variants for selection, suggesting a qualitative advantage for long‐term breeding goals. Here, we demonstrate how haplotypes and ML can improve GS in B. napus.