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 .
Tessa R. Macnish, H. Al-Mamun, Thomas Bergmann et al.· Plant Biotechnology Journal· 0 citations
There is a need to breed superior wheat cultivars to meet increasing global food demand. However, modern cultivars have undergone a substantial loss of genetic diversity due to intensive breeding. A pool of genetic diversity remains untapped in wheat landraces, and pangenomics can help identify genes of potential agronomic importance in these old lines that can be applied to accelerate wheat improvement. Here, we have constructed the largest wheat pangenome to date, representing 1,061 diverse individuals (827 landraces and 234 modern cultivars) from 47 countries across six continents. We identified 10,426 predicted gene models specific to landraces that are enriched for functions associated with disease resistance, abiotic stress adaptation, and symbiosis. Our results reveal that landraces harbour an extensive repertoire of genes that are absent from modern wheat cultivars and provide a foundational resource for the systematic reintroduction of adaptive variation to enhance wheat resilience and sustainability in the face of climate change.
Teng Li, Felipe E. Albornoz, John Bruton et al.· bioRxiv· 0 citations
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