Genomewide association study (GWAS) is an efficient tool for the detection of SNPs and candidate genes in quantitative traits.Growth rate is an important trait for increasing themeat production in sheep. A total of 96 Baluchi sheep were genotyped using Illumina OvineSNP50 BeadChip to run a GWAS for an average daily gain (ADG) and Kleiber ratio (KR) traits in different periods of age in sheep. Traits included were average daily gain from birth to three months (ADG0–3), from three months to six months (ADG_3–6), fromsix months to nine months(ADG_6–9), fromnine months to yearling (ADG_9–12), frombirth to six months (ADG_0–6),from three months to nine months (ADG_3–9), from threemonths to yearling (ADG_3–12) and corresponding Kleiber ratios (KR_0–3, KR_3–6, KR_6–9, KR_9–12, KR_0–6, KR_3–9 and KR_3–12, respectively). A total of 42,243 SNPs passed the quality-control filters and were analysed by PLINK software in a linear mixed model. Two SNPs were identified on two chromosomes at the 5% genomewidesignificance level for KR(3–9) and KR(6–9). Two candidate genes, namely MAGI1 and ZNF770, were identified correspondingly harbouring and close to these QTL. Also, a total of 21 SNPs were found on chromosomes 2, 3, 5, 6, 7, 10, 17, 19, 20 and 25 at the 5% chromosomewide significance level for ADG and KR traits. Thus, we suggest more studies to discover the causative variants for growth traits in sheep.

Access to dense panels of molecular markers has facilitated genomic selection in animal breeding. The purpose of this study was to compare the nonparametric (random forest and support vector machine), semiparametric reproducing kernel Hilbert spaces (RKHS), and parametric methods (ridge regression and Bayes A) in prediction of genomic breeding values for traits with different genetic architecture. The predictive performance of different methods was compared in different combinations of distribution of QTL effects (normal and uniform), two levels of QTL numbers (50 and 200), three levels of heritability (0.1, 0.3 and 0.5), and two levels of training set individuals (1000 and 2000). To do this, a genome containing four chromosomes each 100-cM long was simulated on which 500, 1000 and 2000 evenly spaced single-nucleotide markers were distributed. With an increase in heritability and the number of markers, all the methods showed an increase in prediction accuracy (P<0.05). By increasing the number of QTLs from 50 to 200, we found a significant decrease in the prediction accuracy of breeding value in all methods (P<0.05). Also, with the increase in the number of training set individuals, the prediction accuracy increased significantly in all statistical methods (P<0.05). In all the various simulation scenarios, parametric methods showed higher prediction accuracy than semiparametric and nonparametric methods. This superior mean value of prediction accuracy for parametric methods was not statistically significant compared to the semiparametric method, but it was statistically significant compared to the nonparametric method. Bayes A had the highest accuracy of prediction among all the tested methods and, is therefore, recommended for genomic evaluation.