aceTrait-Informed Machine Learning Improves Precise Genomic Selection and Hybrid Design in Wheat
Machine learning can unlock genomic selection (GS) for polygenic traits, but only if millions of SNPs are distilled into breeder-actionable loci. Here, we presented genome-wide association signal ranking with linkage disequilibrium pruning and Random Forest feature importance selection (GLRS), a trait-informed framework for deriving ultra-sparse marker sets for pure-line and hybrid GS breeding in wheat. Training on 768 diverse accessions phenotyped for nine yield-related traits across six environments and 570,44…
A machine learning marker-selection pipeline cut millions of SNPs to a few hundred per trait while improving genomic prediction accuracy in wheat pure-line and hybrid breeding.
Evidence
- Peer-reviewedPlant Communications2026-10-20
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Truvace Impact Record TRV-2026-2110, v1: “Trait-Informed Machine Learning Improves Precise Genomic Selection and Hybrid Design in Wheat.” Truvace, 2026-10-21. /record/TRV-2026-2110 (accessed at citation time). sha256 e7558c3a13e95d32…
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