Tag-SNP selection using Bayesian genomewide association study for growth traits in Hereford and Braford cattle.

The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density panels useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag-SNPs were selected with BayesB (π = 0.995) method to compose low-density panels. The number of Tag-single nucleotide polymorphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag-SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and postweaning gain (PWG345), respectively. For Tag-SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k-means and from 0.30 to 0.65 for random clustering of animals to compose reference and validation groups. Although genomic prediction accuracies were higher with the full marker panel, predictions with low-density panels retained on average 76% of the accuracy obtained with BayesB with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag-SNP panels may be useful for future fine mapping studies and for lower-cost commercial genomic prediction applications.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

Genetic variability among the wild boars (Sus scrofa scrofa), crossbred animals and pigs using microsatellite markers (STRs)

The aim of this work was to study the genetic variability among the wild boars, crossbred animals and pigs using microsatellite markers. Five genetic groups were studied. The fragments of three microsatellites developed for Sus scrofa domestica - IGF1, ACTG2 and TNFB - were amplified through PCR technique to evaluate the expected intra populacion variability (He) and observed (Ho) heterozygosity, and endogamy coefficient (F IS ) within each population and inter population variability F IS , testing relationship among five genetic groups to establish the genetic distance among them. The high level of observed heterozygosity values varied between 0.537 and 0.7871. Generally, F IS was low, suggesting that the endogamy did not exist between the tested animals.

Association between G316A growth hormone polymorphism and economic traits in pigs

The association between G316A growth hormone polymorphism and quantitative traits was investigated in an F2 population of pigs. Association analyses were performed using a statistical model that included genotype, sex, batch and sex by genotype interaction as fixed effects and sire as random effect. The polymorphism was associated with the number of right teats (p = 0.03), heart weight (p = 0.04), lung weight (p = 0.05), carcass length determined by the Brazilian carcass classification method (p = 0.04), picnic shoulder weight (p = 0.07), jowl weight (p = 0.01), pH 24 h after slaughtering (p = 0.03) and drip loss (p = 0.01). Interaction between genotype and sex was observed for six performance traits. The additive effect was significant (p < 0.10) for heart weight, jowl weight and pH 24 h after slaughtering. The effect of dominance was significant (p < 0.05) for number of right teats, heart weight, carcass length, picnic shoulder weight and pH 24 h after slaughtering. This study shows that the growth hormone gene is a potential candidate for investigating the phenotypic variation of quantitative traits in pigs, and suggests its possible application in breeding programs.