Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 309 3.2 Heritability of milk production traits Heritability estimates for milk production traits indicate a substantial genetic component, which makes these traits amenable to selection and genetic improvement. For example, heritability estimates for milk yield, fat yield, and protein yield are generally high, suggesting that a significant proportion of the variation in these traits can be attributed to genetic differences among individuals (Jiang et al., 2010; Taherkhani et al., 2022; Laodiim et al., 2023). This high heritability underscores the potential for using genomic selection to enhance these traits in dairy cattle populations. Longitudinal studies have further demonstrated that the genetic influence on these traits can be consistently observed across different stages of lactation, reinforcing the importance of genetic factors in milk production (Teng et al., 2023). 3.3 Major genes and QTLs (Quantitative Trait Loci) associated with milk yield and composition Genome-wide association studies (GWAS) have identified numerous quantitative trait loci (QTLs) and candidate genes associated with milk production traits. Notably, the DGAT1 gene on chromosome 14 has been repeatedly implicated in influencing milk yield, fat percentage, and protein percentage across various studies (Wang et al., 2019; Bakhshalizadeh et al., 2021; Kim et al., 2021). Other significant genes include GHR, which is associated with milk yield, and ABCG2, which affects milk composition traits such as fat and protein percentages (Iung et al., 2019; Teng et al., 2023). Meta-analyses and gene network analyses have further refined the understanding of these genetic influences by integrating data from multiple studies, thereby increasing the power to detect significant QTLs and elucidate the underlying biological mechanisms (Bakhshalizadeh et al., 2021; Taherkhani et al., 2022). For instance, a meta-analysis identified 9 QTLs for milk yield, 36 QTLs for fat percentage, and 10 QTLs for protein percentage, highlighting the complex genetic architecture of these traits (Bakhshalizadeh et al., 2021). Additionally, novel candidate genes such as PDE4B and ANO2 have been discovered, which may play roles in milk production through pathways related to fat metabolism and cellular signaling (Kim et al., 2021). 4 Applications of GWAS in Dairy Cattle 4.1 Identification of genetic variants associated with milk production Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic architecture underlying milk production traits in dairy cattle. By analyzing large datasets of phenotypic and genotypic information, researchers have identified numerous single nucleotide polymorphisms (SNPs) and genomic regions associated with milk yield, fat yield, protein yield, and other related traits. For instance, a study on Brazilian Holstein cattle identified 46 genomic windows explaining more than 1% of the genetic variance for milk yield and other traits, highlighting genes such as MGST1, ABCG2, DGAT1, and PAEP (Iung et al., 2019). Similarly, a meta-analysis of GWAS in Holstein cows identified significant QTLs for milk yield, fat percentage, and protein percentage, with notable SNPs located near the DGAT1 and PPP1R16A genes (Bakhshalizadeh et al., 2021). These findings underscore the polygenic nature of milk production traits and the importance of specific genomic regions in influencing these traits. 4.2 GWAS findings on milk composition (e.g., Fat, Protein, Lactose) GWAS have also been instrumental in elucidating the genetic basis of milk composition, including fat, protein, and lactose content. In a study involving Chinese Holstein cows, researchers identified 28 candidate SNPs associated with various milk composition traits using a mixed linear model (Wang et al., 2020). Another study focused on milk protein composition traits in Chinese Holstein cows identified significant associations with genes such as CSN1S1, CSN1S2, CSN2, CSN3, and DGAT1, which are known to influence milk protein fractions like casein and lactoglobulin (Zhou et al., 2019). Additionally, research on Thai dairy cattle revealed genomic regions associated with fat, protein, and total solid percentages, further emphasizing the role of specific genes in determining milk composition (Buaban et al., 2021) (Figure 1). These studies provide valuable insights into the genetic factors that influence milk quality and composition, which are crucial for dairy production and processing.
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