Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 310 Figure 1 Manhattan plots of the additive genetic variance explained by windows of 20 adjacent SNPs for milk production traits and SCS in Thai dairy cattle: (A) milk yield, (B) fat yield, (C) protein yield, (D) TS yield, (E) fat percentage, (F) protein percentage, (G) TS percentage, and (H) SCS (Adopted from Buaban et al., 2021) 4.3 GWAS contributions to improving milk quality and yield The application of GWAS in dairy cattle has not only enhanced our understanding of the genetic determinants of milk production and composition but also contributed to practical improvements in milk quality and yield (Yang, 2024). By identifying key genetic variants and QTLs, GWAS enables the development of genomic selection strategies that can be used to breed cattle with superior milk production traits. For example, the identification of novel genes involved in tissue repair, immune response, and glucose homeostasis in Brazilian Holstein cattle can inform the design of customized SNP arrays for genomic selection, potentially improving milk yield and quality under specific environmental conditions (Iung et al., 2019). Furthermore, the integration of GWAS findings into breeding programs has been shown to increase the accuracy of genomic predictions, as demonstrated in studies on Thai dairy cattle and Brazilian Girolando cattle (Otto et al., 2020; Buaban et al., 2021). These advancements highlight the potential of GWAS to drive genetic gains in dairy cattle, ultimately leading to more efficient and productive dairy farming practices.
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