Meilin Zhang1,3, Heng Du1,3, Yu Zhang1,3, Yue Zhuo1, Zhen Liu1, Yahui Xue1, Lei Zhou1, Sixuan Zhou2, Wanying Li1 and Jian-Feng Liu1 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; 2Institute of Animal Husbandry and Veterinary Sciences, Guizhou Academy of Agricultural Sciences, Guiyang, Guizhou 550005, China

↵3 These authors contributed equally to this work.

Corresponding author: liujfcau.edu.cn 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 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.

Footnotes

[Supplemental material is available for this article.]

Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280176.124.

Freely available online through the Genome Research Open Access option.

Received October 30, 2024. Accepted May 22, 2025.