A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry.

ABSTRACT: As the scale of deep whole-genome sequencing (WGS) data has grown exponentially, hundreds of millions of single nucleotide polymorphisms (SNPs) have been identified in livestock. Utilizing these massive SNP data in population stratification analysis, ancestry prediction, and breed diversity assessments leads to overfitting issues in computational models and creates computational bottlenecks. Therefore, selecting genetic variants that express high amounts of information for use in population diversity studies and ancestry inference becomes critically important. Here, we develop a method, HITSNP, that combines feature selection and machine learning algorithms to select high-representative SNPs that can effectively estimate breed diversity and infer ancestry. HITSNP outperforms existing featu

SUBMITTER: Zhang M 

PROVIDER: S-EPMC12315865 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature