Abstract
Assessing dominance and additive effects of casein complex single-nucleotide polymorphisms (SNPs) (αS1, αS2, β, and κ casein), and their epistatic relationships may maximize our knowledge on the genetic regulation of profitable traits. Contextually, new genomic selection perspectives may translate this higher efficiency into higher accuracies for milk yield and components’ genetic parameters and breeding values. A total of 2594 lactation records were collected from 159 Murciano-Granadina goats (2005–2018), genotyped for 48 casein loci-located SNPs. Bonferroni-corrected nonparametric tests, categorical principal component analysis (CATPCA), and nonlinear canonical correlations were performed to quantify additive, dominance, and interSNP epistatic effects and evaluate the outcomes of their inclusion in quantitative and qualitative milk production traits’ genetic models (yield, protein, fat, solids, and lactose contents and somatic cells count). Milk yield, lactose, and somatic cell count heritabilities increased considerably when the model including genetic effects was considered (0.46, 0.30, 0.43, respectively). Components standard prediction errors decreased, and accuracies and reliabilities increased when genetic effects were considered. Conclusively, including genetic effects and relationships among these heritable biomarkers may improve model efficiency, genetic parameters, and breeding values for milk yield and composition, optimizing selection practices profitability for components whose technological application may be especially relevant for the cheese-making dairy sector.
Highlights
The Murciano-Granadina goat is one of the main dairy goat breeds in Spain
As suggested by Pizarro Inostroza et al [7], we can infer that categorical principal component analysis (CATPCA) method overcomes existing haplotype-tagging Single-nucleotide polymorphism (SNP) (htSNP) methods, as it proposes the optimal number of
Inostroza et al [7]), which is over the minimum level to support reasonable internal consistency of the elements (SNPs) selected, the reliability is acceptable after redundant SNPs are removed
Summary
The Murciano-Granadina goat is one of the main dairy goat breeds in Spain. This special relevance has been achieved through its census, geographical distribution, and its milk quality and production, which on the whole, is well-suited for the dairy industry [1].The demands for goat milk have increased in recent decades, selection programs have faced the need to adapt through new technological and methodological advances to more suitably and appropriately respond to them. The genetic variants for caseins (αS1, αS2, β, and κ casein) influence the traits related to the components of milk and its profitability [2] In this sense, the estimation of genetic parameters and breeding values for milk production characteristics and components in dairy goats is necessary, as these parameters are the indicators of the genetic progress that can be achieved when a good selection and mating program in this species has been implemented [3]. The estimation of genetic parameters and breeding values for milk production characteristics and components in dairy goats is necessary, as these parameters are the indicators of the genetic progress that can be achieved when a good selection and mating program in this species has been implemented [3] Knowledge of these parameters will help in the selection of superior individuals for these characteristics [4], allowing to increase the average performance of animals and in this way, establishing a constant expansion of the goat milk industry and its derivatives [5]
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