Genome-wide association studies of lactation yields of milk, fat, protein and somatic cell score in New Zealand dairy goats

Abstract

Background: Identifying associations between genetic markers and traits of economic importance will provide practical benefits for the dairy goat industry, enabling genomic prediction of the breeding value of individuals, and facilitating discovery of the underlying genes and mutations. Genome-wide association studies were implemented to detect genetic regions that are significantly associated with effects on lactation yields of milk (MY), fat (FY), protein (PY) and somatic cell score (SCS) in New Zealand dairy goats. Methods: A total of 4,840 goats were genotyped with the Caprine 50 K SNP chip (Illumina Inc., San Diego, CA). After quality filtering, 3,732 animals and 41,989 SNPs were analysed assuming an additive linear model. Four GWAS models were performed, a single-SNP additive linear model and three multi-SNP BayesC models. For the single-SNP GWAS, SNPs were fitted individually as fixed covariates, while the BayesC models fit all SNPs simultaneously as random effects. A cluster of significant SNPs were used to define a haplotype block whose alleles were fitted as covariates in a Bayesian model. The corresponding diplotypes of the haplotype block were then fit as class variables in another Bayesian model. Results: Across all four traits, a total of 43 genome-wide significant SNPs were detected from the SNP GWAS. At a genome-wide significance level, the single-SNP analysis identified a cluster of variants on chromosome 19 associated with MY, FY, PY, and another cluster on chromosome 29 associated with SCS. Significant SNPs mapped in introns of candidate genes (45%), in intergenic regions (36%), were 0-5 kb upstream or downstream of the closest gene (14%) or were synonymous substitutions (5%). The most significant genomic window was located on chromosome 19 explaining up to 9.6% of the phenotypic variation for MY, 8.1% for FY, 9.1% for PY and 1% for SCS. Conclusions: The quantitative trait loci for yield traits on chromosome 19 confirms reported findings in other dairy goat populations. There is benefit to be gained from using these results for genomic selection to improve milk production in New Zealand dairy goats.

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

“…Some authors suggest that assessing haplotypes may provide more robust and powerful information than considering individual SNPs, which may also translate into a higher ability to capture the regional LD information. As a result, this may enhance the understanding of genetic variability and, in turn, the combined regulation of phenotypic expression by different heritable units [ 43 ].…”

“…The association between haplotypic sequences in the caprine chromosome 6 and milk yield, fat, and protein content has been addressed by many authors as reported by Mucha et al [ 51 ]. For instance, caprine autosome 6 hosts a genomic window (6:86,050,148–6:86,990,478) that explains 1% of the genomic variance in milk yield [ 43 ] and comprises the MTHFR gene, which accounts for a known relationship with milk protein synthesis [ 52 ]. However, associations with other important economical traits, such as lactose [ 53 ] or somatic cell counts, or parameters describing the shape of the curve for either milk yield or composition are scarce in goats.…”

Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

“…In this population, there is a known QTL with a large effect on milk traits [ 16 ]; therefore, it is appropriate to fit a BayesC model. This model was fitted using the JWAS Julia package [ 17 ] based on 50,000 Markov chain Monte Carlo (MCMC) iterations (including 1000 burned in), and π was assumed known and fixed at 0.98.…”

“…The prior for the marker effects depends on the marker variance, s ak 2 , and the prior probability p that SNP k has zero effect and follows a two-component mixture prior: where s 2 ak ~ v a ,S a 2 X 2 va . A previous study in this population reported that the markers captured 12% of the genetic variance [ 16 ]. To recognize the markers that did not explain the total genetic variance, a residual polygenic effect was included in the model, accounting for 88% of the additive genetic variance.…”

A Candidate Gene Association Study for Economically Important Traits in Czech Dairy Goat Breeds

“…Including genetic effects and relationships among these heritable biomarkers may improve the model efficiency, genetic parameters, and breeding values for milk yield and composition; this inclusion could also help optimise selection practices and profitability for components where technological application may be especially relevant for the cheese-making dairy sector [ 15 ]. In addition to the study of candidate loci, the genomic approach in the genome-wide association study has also been applied for the detection of genetic regions of interest [ 16 ]. They found a total of 43 genome-wide significant SNPs for lactation yields of milk (MY), fat (FY), protein (PY), and somatic cell score (SCS).…”