Bayesian evaluation of the effect of non-genetic factors on the phenomics for quality-related milk nutrients and yield in Murciano-Granadina goats.

The aim of this study was to evaluate the effect of non-genetic factors on the variability of milk production and composition using Bayesian linear regression. We analyzed 2594 milk records from 159 dairy goats from the breeding nucleus of the Murciano-Granadina breed. Bayesian linear regression was used to determine the effects of non-genetic factors on the phenomics for quality-related milk nutrients and yield. Multivariate regression model significantly explained 21.5%, 40.0%, 41.5%, 44.3%, 44.6%, and 47.5% of the variability in somatic cell count (SCC, sc/mL), lactose (%), protein (%), milk yield (kg), fat (%), and dry matter (%), respectively. Although the aforementioned factor combination significantly conditions milk production and composition, SCC may be particularly affected by collateral factors. Milking routine and drying period factors are reference predictors to be considered in the evaluation of milk production and composition progression. Drying period extensions positively repercussed on milk yield and lactose content, but negatively affected fat, protein, dry matter contents, and somatic cell count. Variability across drying years may depend on the drying season rather than the drying month course, except for milk yield, for which an increasing trend was reported from winter to summer. Including drying period-related non-genetic factors in genetic evaluations improves the accuracy of the regression models and permits to boost the commercial possibilities and profitability of local breeds.

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison.

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry.

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

Assessing dominance and additive effects of casein complex single-nucleotide polymorphisms (SNPs) (αS1, αS2, ß, 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.

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

Goat milk casein proteins (αS1, αS2, ß 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.

Sexual Dimorphism for Coping Styles Complements Traditional Methods for Sex Determination in a Multivariety Endangered Hen Breed.

Sex determination is key to designing endangered poultry population conservation and breeding programs when sex distribution departs from Hardy-Weinberg equilibrium. A total of 112 Utrerana chickens (28 per variety, partridge, black, white, and franciscan) were selected for hatching day sexing. Sex assignation was performed through 10 methods. Three sex assignment criteria comprised criteria found in literature, opposite criteria to that in the literature, and composite criteria combining methods reporting the highest predictive success from the previous ones. This study aims to determine which method combinations may more successfully determine sex across the four varieties of Utrerana endangered hen breed to tailor noninvasive early specific models to determine sex in local chicken populations. Although the explanatory power of the three assignation criteria is equal (75%), assignation criteria 2 resulted to be the most efficient as it correctly assigns males more frequently. Only methods 3 (English method), 5 (general down feathers coloration), 7 (wing fan), and 10 (behavior/coping styles) reported significant differences regardless of the variety, hence, are appropriate for early sexing. Sex confirmation was performed at 1.5 months old. Identifying sex proportions enhances genetic management tasks in endangered populations, complementing more standardized techniques, which may result inefficient given the implicit diversity found in local populations.

Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005-2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model's efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from -0.15 to -0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were -0.02 for milk/protein and -0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45-0.71) was similar to that of females (0.37-0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61-0.77) for males and females (0.62-0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs.