Integrated Discriminant Evaluation of Molecular Genetic Markers and Genetic Diversity Parameters of Endangered Balearic Dog Breeds.

The genetic diversity analysis of six dog breeds, including Ca de Bestiar (CB), Ca de Bou (CBOU), Podenco Ibicenco (PI), Ca Rater (CR), Ca Mè (CM), and Ca de Conills (CC), reveals insightful findings. CB showcases the highest mean number of alleles (6.17) and heterozygosity values, with significant deviations from Hardy-Weinberg equilibrium (HWE) observed in five markers, indicating high intra-racial genetic diversity (average observed heterozygosity (Ho) = 0.754, expected heterozygosity (He) = 0.761). In contrast, CBOU presents the lowest mean number of alleles (5.05) and heterozygosity values, coupled with moderate polymorphic information content (PIC) values and a moderate level of intra-racial genetic diversity (average Ho = 0.313, He = 0.394). PI demonstrates moderate genetic diversity with an average of 5.75 alleles and highly informative PIC values, while CR displays robust genetic diversity with an average of 6.61 alleles and deviations from equilibrium, indicating potential risks of inbreeding (average Ho = 0.563, He = 0.658). CM exhibits moderate genetic diversity and deviations from equilibrium, similar to CBOU, with an average of 6.5 alleles and moderate PIC values (average Ho = 0.598, He = 0.676). Conversely, CC shows a wider range of allelic diversity and deviations from equilibrium (average Ho = 0.611, He = 0.706), suggesting a more diverse genetic background. Inter-racial analysis underscores distinct genetic differentiation between breeds, emphasizing the importance of informed breeding decisions and proactive genetic management strategies to preserve diversity, promote breed health, and ensure long-term sustainability across all breeds studied.

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.

Candidate Genes and Their Expressions Involved in the Regulation of Milk and Meat Production and Quality in Goats (Capra hircus).

Despite their pivotal position as relevant sources for high-quality proteins in particularly hard environmental contexts, the domestic goat has not benefited from the advances made in genomics compared to other livestock species. Genetic analysis based on the study of candidate genes is considered an appropriate approach to elucidate the physiological mechanisms involved in the regulation of the expression of functional traits. This is especially relevant when such functional traits are linked to economic interest. The knowledge of candidate genes, their location on the goat genetic map and the specific phenotypic outcomes that may arise due to the regulation of their expression act as a catalyzer for the efficiency and accuracy of goat-breeding policies, which in turn translates into a greater competitiveness and sustainable profit for goats worldwide. To this aim, this review presents a chronological comprehensive analysis of caprine genetics and genomics through the evaluation of the available literature regarding the main candidate genes involved in meat and milk production and quality in the domestic goat. Additionally, this review aims to serve as a guide for future research, given that the assessment, determination and characterization of the genes associated with desirable phenotypes may provide information that may, in turn, enhance the implementation of goat-breeding programs in future and ensure their sustainability.

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.