Nonparametric analysis of casein complex genes' epistasis and their effects on phenotypic expression of milk yield and composition in Murciano-Granadina goats

Abstract

Improving knowledge on the causative polymorphisms or genes regulating the expression of milk quantitative and qualitative traits and their interconnections plays a major role in dairy goat breeding programs and genomic research. This information enables optimization of predictive and selective tools, to obtain better-performing animals to help satisfy market demands more efficiently. Goat milk casein proteins (α S1 , α S2 , β, and κ) are encoded by 4 loci (CSN1S1, CSN1S2, CSN2, and CSN3) clustered within 250 kb on chromosome 6. Among the statistical methods used to identify epistatic interactions in genome-wide qualitative association studies (GWAS), gene-based methods have recently grown in popularity due to their better statistical power and biological interpretability. However, most of these methods make strong assumptions about the magnitude of the relationships between SNP and phenotype, limiting statistical power. Thus, the aims of this study were to quantify the epistatic relationships among 48 SNP in the casein complex on the expression of milk yield and components (fat, protein, dry matter, lactose, and somatic cells) in Murciano-Granadina goats, to explain the qualitative nature of the SNP used to quantify the genotypes produced as a result. Categorical principal component analysis (CATPCA) was used to delimit and group the number of SNP studied depending on their implications in the explanation of milk yield and components variability. Afterward, nonlinear canonical correlation analysis was used to identify relationships among and within the SNP groups detected by CATPCA. Our results suggest that 79.65% of variability in the traits evaluated may be ascribed to the epistatic relationships across and within 7 SNP groups. Two partially overlapping groups of epistatically interrelated SNP were detected: one group of 21 SNP, explaining 57.56% of variability, and another group of 20 SNP, explaining 42.43% (multiple fit ≥ 0.1). Additionally, SNP18, 32, and 36 (CSN1S2, CSN1S1, and CSN2 loci, respectively) were the most significant SNP to explain intragroup epistatic variability (component loading > |0.5|). Conclusively, milk yield and quality may not only depend on the specific casein gene pool of individuals, but may also be relevantly conditioned by the relationships set across and within such genes. Hence, studying epistasis in isolation may be crucial to optimize selective practices for economically important dairy traits.

Non-parametric analysis of the effects of nongenetic factors on milk yield, fat, protein, lactose, dry matter content and somatic cell count in Murciano-Granadina goats

“…The present study is complementary to two studies focussing on the assessment of the genetic fraction (additive and dominance (Pizarro et al 2019c(Pizarro et al , 2020b and epistatic effects (Pizarro et al 2020a(Pizarro et al , 2020b) of 48 SNPs in the goat casein gene complex, which regulates for the expression of milk yield, fat, protein, dry matter, lactose and SCC Â 10 3 sc/mL.…”

Exploring Breed-Specific Milk Coagulation in Spanish Dairy Sheep: A Canonical Correlation Approach

“…In recent years, canonical correlation analysis has been used in some studies involving dairy ruminants for the analysis of aspects such as milk immunoglobulin profiles, genomic characterisation, or the impact of housing on animal health [ 59 , 60 , 61 , 62 ]. Moreover, a recent study from our research group has used CCA to explore species-specific differences in milk processing [ 43 ].…”

Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties

“…This could be of particular interest in goats, especially for those breeds (e.g., Alpine, Toggenburg) characterized by weak or nonexpressing alleles (e.g., F, N allele) of α S1 -casein, associated with unfavorable coagulation process (Maga et al, 2009;Devold et al, 2011). It worth noting that genetic pattern at a specific locus might change over time because of the selective pressure, as recently evidenced in casein genes for the Murciano-Granadina goat breed (Pizarro et al, 2020). However, high MCP analysis costs and logistics pose restriction for their wide-scale application.…”