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

Single and longitudinal genome-wide association studies for dairy traits available in goats with three recorded lactations

Milk yield and composition phenotypes are systematically recorded across several lactations in goats, but the majority of genome-wide association studies (GWAS) performed so far have rather ignored the longitudinal nature of such data. Here, we have used two different GWAS approaches to analyse data from three lactations recorded in Murciano-Granadina goats. In Analysis 1, independent GWAS have been carried out for each trait and lactation, while a single longitudinal GWAS, jointly considering all data, has been performed in Analysis 2. In both analyses, genome-wide significant QTL for lactose percentage on chromosome 2 (129.77-131.01 Mb) and for milk protein percentage on the chromosome 6 (74.8-94.6 Mb) casein gene cluster region were detected. In Analysis 1, several QTL were not replicated in all three lactations, possibly due to the existence of lactation-specific genetic determinants. In Analysis 2, we identified several genome-wide significant QTL related to milk yield and protein content that were not uncovered in Analysis 1. The increased number of QTL identified in Analysis 2 suggests that the longitudinal GWAS is particularly well suited for the genetic analysis of dairy traits. Moreover, our data confirm that variability within or close to the casein complex is the main genetic determinant of milk protein percentage in Murciano-Granadina goats.

Variations in Casein Genes Are Associated with Milk Protein and Fat Contents in Sarda Goats (Capra hircus), with an Important Role of CSN1S2 for Milk Yield

This work aimed to assess the variability of casein genes in a population of 153 bucks and 825 lactating does of the Sarda breed, and to perform association analysis between polymorphic sites and milk yield and composition traits. To genotype the casein genes, we chose an SNP panel including 44 SNPs mapping to the four casein genes CSN1S1, CSN2, CSN1S2, and CSN3. Genotyping (made by KASP™ genotyping assay, based on competitive allele-specific PCR) revealed the high variability of the Sarda goat, and haplotype analysis revealed linkage disequilibrium (LD) between CSN1S1 and CSN2 genes, in addition to two LD blocks within the CSN1S2 and two LD blocks within the CSN3 gene, in bucks and does. Association analysis revealed that variability at all four casein genes was associated with milk protein content, total solids, and milk energy. The three Ca-sensitive casein genes were associated with lipid content, and CSN1S2 showed a unique pattern, with intron variants associated with milk yield, in addition to milk pH, NaCl, and SCS (Somatic Cell Score). This information might prove useful in selection schemes and in future investigations aiming to better understand the biology of lactation, and the direct link between genotype and phenotype.

Discriminant canonical tool for inferring the effect of αS1, αS2, β, and κ casein haplotypes and haplogroups on zoometric/linear appraisal breeding values in Murciano-Granadina goats

Genomic tools have shown promising results in maximizing breeding outcomes, but their impact has not yet been explored. This study aimed to outline the effect of the individual haplotypes of each component of the casein complex (αS1, β, αS2, and κ-casein) on zoometric/linear appraisal breeding values. A discriminant canonical analysis was performed to study the relationship between the predicted breeding value for 17 zoometric/linear appraisal traits and the aforementioned casein gene haplotypic sequences. The analysis considered a total of 41,323 zoometric/linear appraisal records from 22,727 primiparous does, 17,111 multiparous does, and 1,485 bucks registered in the Murciano-Grandina goat breed herdbook. Results suggest that, although a lack of significant differences (p > 0.05) was reported across the predictive breeding values of zoometric/linear appraisal traits for αS1, αS2, and κ casein, significant differences were found for β casein (p < 0.05). The presence of β casein haplotypic sequences GAGACCCC, GGAACCCC, GGAACCTC, GGAATCTC, GGGACCCC, GGGATCTC, and GGGGCCCC, linked to differential combinations of increased quantities of higher quality milk in terms of its composition, may also be connected to increased zoometric/linear appraisal predicted breeding values. Selection must be performed carefully, given the fact that the consideration of apparently desirable animals that present the haplotypic sequence GGGATCCC in the β casein gene, due to their positive predicted breeding values for certain zoometric/linear appraisal traits such as rear insertion height, bone quality, anterior insertion, udder depth, rear legs side view, and rear legs rear view, may lead to an indirect selection against the other zoometric/linear appraisal traits and in turn lead to an inefficient selection toward an optimal dairy morphological type in Murciano-Granadina goats. Contrastingly, the consideration of animals presenting the GGAACCCC haplotypic sequence involves also considering animals that increase the genetic potential for all zoometric/linear appraisal traits, thus making them recommendable as breeding animals. The relevance of this study relies on the fact that the information derived from these analyses will enhance the selection of breeding individuals, in which a desirable dairy type is indirectly sought, through the haplotypic sequences in the β casein locus, which is not currently routinely considered in the Murciano-Granadina goat breeding program.

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

Simple Summary

During the present decade, highly selected caprine farming has increased in popularity due to the hardiness and adaptability inherent to goats. Recent advances in genetics have enabled the improvement in goat selection efficiency. The present review explores how genetic technologies have been applied to the goat-farming sector in the last century. The main candidate genes related to economically relevant traits are reported. The major source of income in goat farming derives from the sale of milk and meat. Consequently, yield and quality must be specially considered. Meat-related traits were evaluated considering three functional groups (weight gain, carcass quality and fat profile). Milk traits were assessed in three additional functional groups (milk production, protein and fat content).

Abstract

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.

Detecting the footprint of selection on the genomes of Murciano-Granadina goats

Artificial selection is one of the major forces modifying the genetic composition of livestock populations. Identifying genes under selection could be useful to elucidate their impact on phenotypic variation. We aimed to identify genomic regions targeted by selection for dairy and pigmentation traits in Murciano-Granadina goats. Performance of a selection scan based on the integrated haplotype score test in a population of 1183 Murciano-Granadina goats resulted in the identification of 77 candidate genomic regions/SNPs. The most significant selective sweeps mapped to chromosomes 1 (69.86 Mb), 4 (41.80-49.95 Mb), 11 (65.74 Mb), 12 (31.24 and 52.51 Mb), 17 (34.76-37.67 Mb), 22 (31.75 Mb), and 26 (26.69-31.05 Mb). By using previously generated RNA-Seq data, we built a catalogue of 6414 genes that are differentially expressed across goat lactation (i.e. 78 days post-partum, early lactation; 216 days post-partum, late lactation; 285 days post-partum, dry period). Interestingly, 183 of these genes mapped to selective sweeps and several of them display functions related with lipid, protein, and carbohydrate metabolism, insulin signaling, cell proliferation, as well as mammary development and involution. Of particular interest are the CSN3 and CSN1S2 genes, which encode two major milk proteins. Additionally, we found three pigmentation genes (GLI3, MC1R, and MITF) co-localizing with selective sweeps. Performance of a genome-wide association study and Sanger sequencing and TaqMan genotyping experiments revealed that the c.801C>G (p.Cys267Trp) polymorphism in the melanocortin 1 receptor (MC1R) gene is the main determinant of the black (GG or GC genotypes) and brown (CC genotypes) colorations of Murciano-Granadina goats.

Evaluation of Different Test-Day Milk Recording Protocols by Wood's Model Application for the Estimation of Dairy Goat Milk and Milk Constituent Yield

Simple Summary

Considering the seasonal trend of milk production due to application of estrus synchronization treatments in autumn or in spring season, as well as to meet meat production and market requirements for religious holidays, one may find it useful to simplify the milk and milk constituent yield quantification during the entire period of lactation. The present work aimed to verify the application of Wood’s model to a different number of test-day milk recording protocols in order to reduce the frequency of National Breeding Association test-day milking record protocol. Wood’s model fit well on goat milk production, showing high R² values. Moreover, the milk yield, fat-corrected milk yield, fat yield, and protein yield estimation applying Wood’s model to four recordings (first, second, fourth, and sixth month records) showed results superimposable to International Committee of Animal Recording (ICAR) application and Wood’s model application on test-day records every 15 days.

Abstract

Goats have important social and economic roles in many countries because of their ability to survive and be productive in marginal areas. The overarching aim of this study was to compare the application of Wood’s model to different test-day milk recording protocols for estimation of total milk, fat, and protein yield in dairy goats. A total of 465 goats were used (Garganica, 78; Girgentana, 81; Jonica, 76; Maltese, 77; Red Mediterranean, 76; Saanen, 77). Milk yield was recorded every 15 days throughout lactation of 210 days, for a total of 14 collection days, during both morning and afternoon milking sessions. Milk samples were collected and analyzed for protein and fat. The fat-corrected milk was standardized at 35g fat/kg of milk. Wood models showed high R² values, and thus good fitting, in all the considered breeds. Wood model applied to first, second, fourth, and sixth month recordings (C) and ICAR estimation showed total milk yield very close to Wood’s model applied to all 14 recordings (A) (p > 0.38). Differently, Wood’s model applied to the first, second, third, and fourth month recording (B) estimation showed great differences (p < 0.01). This could be applied for farms that had the necessity to synchronize flock groups for kidding in order to produce kid meat. In farms that apply the estrus induction and/or synchronization for kidding, it would be possible to perform only four test-day milk recordings and to apply the Wood’s model on them in order to obtain the estimation of total milk, fat, and protein yield during lactation for animals inscribed, or to be inscribed, to the genealogical book.

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats.

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

Simple Summary

The high costs of genotyping normally compel researchers to work with reduced sample sizes. Contextually, population observations may no longer compensate for the lack of sufficient data to fit lactation curves, hindering model efficiency, explicative ability, and predictive potential. Individualized lactation curve analyses may save these drawbacks, but may be time-demanding, which may be prevented through computational automatization. An SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the curve described by the milk components of the milk of 159 Murciano-Granadina does selected for genotyping analyses. Protein, fat, dry matter, lactose, and somatic cell counts curves were evaluated and modelled, while peak and persistence were estimated to maximize the ability to understand and anticipate productive responses in Murciano-Granadina goats, which may translate into improved profitability of goat milk as a product.

Abstract

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, 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.