Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 312 6.3 Case study of crossbreeding programs influenced by GWAS findings Crossbreeding programs have also benefited from GWAS findings, particularly in breeds like Girolando, which is a cross between Gir and Holstein cattle. A study on Girolando cattle identified QTL and candidate genes associated with 305-day milk yield, revealing significant genetic variance attributed to breed-specific SNP alleles (Otto et al., 2020) (Figure 2). This information is crucial for designing genomic selection panels tailored to crossbred populations, thereby optimizing milk production traits. Additionally, multibreed GWAS have shown that combining data from different breeds can refine QTL mapping, as demonstrated in a study involving Holstein and Jersey cattle, which identified small genomic intervals and potential candidate genes for milk production (Bindea et al., 2009). These case studies illustrate how GWAS findings can be leveraged to enhance crossbreeding programs, ultimately leading to improved dairy cattle performance. Figure 2 Functional networks showing gene interactions (triangle nodes) related to 305-d milk yield and the relationships across genes and their subnetworks related to regulation of hormone secretion, ATPase activity, and the JAK/STAT cascade (Janus kinase/signal transducers and activators of transcription) in the Girolando population. Node size denotes term enrichment significance, from the ClueGO Cytoscape plug-in (Bindea et al., 2009). The most enriched terms per group are shown in bold purple, green, and light green letters, whereas the sub-biological process terms are labeled in black (Adopted from Otto et al., 2020) 7 Challenges and Limitations of GWAS in Milk Production 7.1 Population structure and genetic diversity One of the primary challenges in conducting genome-wide association studies (GWAS) for milk production traits in dairy cattle is accounting for population structure and genetic diversity. Different cattle breeds and even subpopulations within a breed can exhibit significant genetic variation, which can confound GWAS results. For instance, combining data from multiple populations can enhance the detection power of GWAS, but it also introduces complexity due to varying genetic backgrounds (Gebreyesus et al., 2019). Additionally, environmental conditions, such as those found in tropical climates, can influence the expression of milk production traits, making it difficult to confirm GWAS findings across different populations (Coutinho et al., 2019). 7.2 Issues of false positives and statistical power False positives and limited statistical power are significant issues in GWAS. The detection of false positives can be exacerbated by the large number of single nucleotide polymorphisms (SNPs) tested simultaneously. Multivariate GWAS methods have been shown to improve the power to detect true associations without increasing the false discovery rate (Bolormaa et al., 2010). However, the sample size remains a critical factor affecting
RkJQdWJsaXNoZXIy MjQ4ODYzNA==