GRE: A Framework for Significant SNP Identification Associated with Wheat Yield Leveraging GWAS-Random Forest Joint Feature Selection and Explainable Machine Learning Genomic Selection Algorithm.

ABSTRACT: Background: Facing global wheat production pressures such as environmental degradation and reduced cultivated land, breeding innovation is urgent to boost yields. Genomic selection (GS) is a useful wheat breeding technology to make the breeding process more efficient, increasing the genetic gain per unit time and cost. Precise genomic estimated breeding value (GEBV) via genome-wide markers is usually hampered by high-dimensional genomic data. Methods: To address this, we propose GRE, a framework combining genome-wide association study (GWAS)'s biological significance and random forest (RF)'s prediction efficiency for an explainable machine learning GS model. First, GRE identifies significant SNPs affecting wheat yield traits by comparison of the constructed 24 SNP subsets (in

SUBMITTER: Song M 

PROVIDER: S-EPMC12564831 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature