A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry
文献类型： 外文期刊
作者： Zhang, Meilin 1 ; Du, Heng 1 ; Zhang, Yu 1 ; Zhuo, Yue 1 ; Liu, Zhen 1 ; Xue, Yahui 1 ; Zhou, Lei 1 ; Zhou, Sixuan 5 ; Li, Wanying 1 ; Liu, Jian-Feng 1 ;
作者机构： 1.Minist Agr, Natl Engn Lab Anim Breeding, Key Lab Anim Genet Breeding & Reprod, Beijing 100193, Peoples R China
2.State Key Lab Anim Biotech Breeding, Beijing 100193, Peoples R China
3.Frontiers Sci Ctr Mol Design Breeding MOE, Beijing 100193, Peoples R China
4.China Agr Univ, Coll Anim Sci & Technol, Beijing 100193, Peoples R China
5.Guizhou Acad Agr Sci, Inst Anim Husb & Vet Sci, Guiyang 550005, Guizhou, Peoples R China
期刊名称：GENOME RESEARCH （ 影响因子：5.5； 五年影响因子：7.3 ）
ISSN： 1088-9051
年卷期： 2025 年 35 卷 8 期
页码：
收录情况： SCI
摘要： 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.
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