RT Journal
A1 Zhang, Meilin
A1 Du, Heng
A1 Zhang, Yu
A1 Zhuo, Yue
A1 Liu, Zhen
A1 Xue, Yahui
A1 Zhou, Lei
A1 Zhou, Sixuan
A1 Li, Wanying
A1 Liu, Jian-Feng
T1 A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry
JF Genome Research 
JO Genome Research 
YR 2025 
FD August 01 
VO 35 
IS 8 
SP 1875 
OP 1886 
DO 10.1101/gr.280176.124 
UL http://genome.cshlp.org/content/35/8/1875.abstract 
AB 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 feature selection methods in estimating accuracy and computational stability. Furthermore, HITSNP offers a new algorithm to predict the number and composition of ancestral populations using a small number of SNPs, and avoiding calculating the number of clusters. Taken together, HITSNP facilitates the research of population structure, animal breeding, and animal resource protection.