Non‐parametric association analysis of additive and dominance effects of casein complex SNPs on milk content and quality in Murciano‐Granadina goats

Abstract

Goat milk casein proteins (αS1, αS2, β and κ) are encoded by four loci (CSN1S1, CSN1S2, CSN2 and CSN3, respectively) clustered within 250 kb in chromosome 6. In this study, 159 Murciano‐Granadina goats were genotyped for 48 SNPs within the entire casein region. Phenotypes on milk yield and components were obtained from 2,594 dairy registries. Additive and dominance effects on milk composition and quality were studied using non‐parametric tests and principal component analysis to prevent SNPs multicollinearity. Two deletions in exon 4 (CSN1S1 and CSN3), one in exon 7 (CSN2) and one in exon 15 (CSN1S2) have been found at frequencies ranging from 0.12 to 0.50. Bonferroni‐corrected significant SNP additive and dominance effects were found for milk yield, fat, protein, dry matter and lactose, and somatic cells. Exons 15 and 7 were significantly associated with milk yield and components except for lactose and somatic cells, while exon 4 was significantly associated with milk yield and components except for protein and dry matter. SNPs' associations with somatic cells were less frequent and weaker than those with milk yield and components. As caseins increase, somatic cells decrease, reducing milk enzymatic activity and consumption suitability. Hence, including molecular information in breeding schemes may promote production efficiency, as selecting against undesirable alleles could prevent the compromises derived from their dominance effects.

Integrating Casein Complex SNPs Additive, Dominance and Epistatic Effects on Genetic Parameters and Breeding Values Estimation for Murciano-Granadina Goat Milk Yield and Components

“…Milk yield and components were estimated until 210 days of lactation and expressed in Kg [12]. Each goat's real milk production (RP j ) was computed through the equation described in Pizarro Inostroza et al [7]. Official control policies are stated by the Royal Decree-Law 368/2005, on 8 th April 2005, regulating official milk yield controls for the genetic evaluation in the bovine, ovine, and caprine species of the Spanish Ministry of Agriculture (2005).…”

“…Milking policies varied across farms depending on whether milking was performed every 4 or 6 weeks, during the morning or afternoon (AT4, AT4T, AT4M, A6, AT6M, and AT6T). First control (d 1 ) and the last (d 2 ) were assessed individually for every goat computing the days between the day the animal was born (BD) and the date of the first control (FC), and the days between the penultimate control (PC) and the last control (LC), respectively using the formulas in Pizarro Inostroza et al [7]. To save interindividual differences that could be ascribed to differences in milking period among other factors, birthdate information, and the date on which several controls were performed until 210 lactation days were included to normalize milk yield for each goat.…”

“…An average of approximately five controls was performed per goat. Standardized yield to 210 days per goat was calculated using the formula and model described in Pizarro Inostroza, et al [7]. Milk sampling was carried out monthly and officially analyzed at the Milk Quality Laboratory, in Cordoba (Spain) to quantify protein, fat, solids, lactose content, and somatic cells count with a MilkoScan™ analyzer FT1.…”

“…Additive and dominance effects for each SNP were determined and encoded, as described in Pizarro Inostroza et al [7]. Normal parametrization encoding was not used as it may potentially presume an ordinal relationship among the levels of each factor (every possibility within each SNPs in our case) with dominant homozygous being encoded as -1, heterozygous being encoded as 0, and recessive homozygous being encoded as 1 for additive genetic component and so on.…”

“…The use of multivariate techniques, such as categorical principal components and nonlinear canonical correlations [7], could be used to find the genetic (single-nucleotide polymorphisms, SNP, combinations) or environmental factors that explain the greatest variability for a certain trait. This provides a tool that enables building relationships that allow grouping animals according to their similar productive traits, basing on the underlying genetic correlations across the different traits considered.…”