Machine learning-based discovery of informative SNPs for population assignment through whole genome sequencing.

ABSTRACT:

Background

Whole-genome sequencing (WGS) data contain a large proportion of genetic information for farm animals, while machine learning algorithms offer a powerful approach for discovering population-informative genetic markers for population assignment. These methods can enhance population classification, supporting genetic resource conservation and optimizing breeding strategies.

Methods

WGS data from 187 individuals representing eight water buffalo breeds (four river type breeds, i.e., Mediterranean, Murrah, Nili Ravi, and Binglangjiang, and four swamp type breeds, i.e., Diandongnan, Dehong, Guizhoubai, and Shanghai) were analyzed. Multiple single nucleotide polymorphism (SNP) selection methods including the population genetic differentiation index (F_ST), the

SUBMITTER: Liang H 

PROVIDER: S-EPMC12751628 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature