Qineng  Si

BIotechnology Reseach Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Animal Molecular Breeding, 2024, Vol. 14, No. 5   
Received: 28 Jul., 2024    Accepted: 06 Sep., 2024    Published: 20 Sep., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This study synthesizes findings from multiple GWAS, highlighting key genomic regions and candidate genes associated with milk yield, fat percentage, protein percentage, and somatic cell score (SCS). Notable genes such as DGAT1, ABCG2, and MGST1 are consistently implicated, along with novel candidates like CCSER1 and CUX2. By integrating high-density SNP chips and whole-genome sequencing, these studies have enhanced the detection of quantitative trait loci (QTLs) and refined genomic selection strategies. The findings underscore the polygenic nature of milk production traits and the utility of GWAS in improving breeding accuracy. Future prospects include the integration of machine learning, epigenomics, and metabolomics to further enhance genetic predictions, optimize breeding programs, and ensure sustainable dairy farming practices.

Genome-wide association studies (GWAS); Milk production; Quantitative trait loci (QTL); Genomic selection; Dairy cattle