Estudo genômico em caprinos leiteiros da raça saanen

Abstract

Affordable commercial panels containing thousands of single nucleotide polymorphisms (SNPs) markers have revolutionized genetic studies in livestock, mainly through genomic selection and genome-wide association analysis. Genomic selection has a practical aspect, as it is directly applied to breeding programs, which may allow increase of the accuracy of genetic evaluations for quantitative traits. Based on that, the objective of this study was to calculate the linkage disequilibrium (LD) of chromosomal segments in the Saanen goats genome and to compare different models for genomic evaluation, with different a priori distributions for the effect of markers. Thus, the LD was evaluated and the effective population size was estimated through markers of an Axiom_OviCap (Caprine) panel, and array customized by Affimetrix with 62,557 SNPs. Twenty-four male and 916 were genotyped which also participated in the genomic evaluations. The average LD value, expressed by the estimate of r2, between adjacent markers (~ 52 kb), was 0.04 ± 0.06. Moderate levels of LD (r2>0.20), on a scale of 0 to 1, were observed in genetic distance classes up to 20 kb. The range of values between the chromosomes varied from 0.03 ± 0.06 to 0.05 ± 0.08. The panel density was not sufficient to provide LD between chromosomal segments to predict genomic genetic values. The estimates of effective population size decreased over time, ranging from 42 animals, 19 generations in the past, to 15 animals in the current generation. The levels of genetic diversity in this Saanen population represent a threat and should be routinely monitored to ensure viability for long-term selection. The genomic evaluations for lactation length, average daily milk yield, milk, fat, protein, total dry extract and lactose yields up to 305 days of lactation, as well as somatic cell count up to 305 days of lactation were performed by the methods genomic BLUP (GBLUP), Bayes Cπ and Bayesian LASSO (BLASSO). The estimated breeding values (EBV) and deregressed estimated breeding values (dEBV) were used as response variables for genomic predictions. The averages of prediction accuracies of all traits when EBV was used as a response variable, were approximately 0.682, 0.676 and 0.674 for GBLUP, Bayes Cπ and BLASSO, respectively. When the response variable was dEBV, the averages of prediction accuracies were approximately 0.501, 0.499 and 0.500 for GBLUP, Bayes Cπ and BLASSO, respectively. None of the methods stood out in terms of prediction ability. However, the GBLUP method was the most appropriate, for presenting the lowest computational cost.