Optimization of Genomic Breeding Value Estimation Model for Abdominal Fat Traits Based on Machine Learning.

ABSTRACT: Abdominal fat is a key indicator of chicken meat quality. Excessive deposition not only reduces meat quality but also decreases feed conversion efficiency, making the breeding of low-abdominal-fat strains economically important. Genomic selection (GS) uses information from genome-wide association studies (GWASs) and high-throughput sequencing data. It estimates genomic breeding values (GEBVs) from genotypes, which enables early and precise selection. Given that abdominal fat is a polygenic trait controlled by numerous small-effect loci, this study combined population genetic analyses with machine learning (ML)-based feature selection. Relevant single-nucleotide polymorphisms (SNPs) were first identified using a combined GWAS and linkage disequilibrium (LD) approach, followed by a two-stage

SUBMITTER: Chen H 

PROVIDER: S-EPMC12523389 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature