Articles written in Journal of Genetics

    • Genomewide association study to detect QTL for twinning rate in Baluchi sheep

      Mohsen Gholizadeh Ghodrat Rahimi-Mianji Ardeshir Nejati-Javaremi Dirk Jan De Koning Elisabeth Jonas

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    • Genomewide association study of body weight traits in Baluchi sheep

      Mohsen Gholizadeh Ghodrat Rahimi-Mianji Ardeshir Nejati-Javaremi

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    • A genome scan for quantitative trait loci affecting average daily gain and Kleiber ratio in Baluchi Sheep


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      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 (ADG3–6), fromsix months to nine months(ADG6–9), fromnine months to yearling (ADG9–12), frombirth to six months (ADG0–6),from three months to nine months (ADG3–9), from threemonths to yearling (ADG3–12) and corresponding Kleiber ratios (KR0–3, KR3–6, KR6–9, KR9–12, KR0–6, KR3–9 and KR3–12, 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.

    • Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation


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      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.

    • Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction


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      The present study aimed to evaluate the predictive performance of bootstrap aggregating sampling technique (Bagging) in the context of genomic best linear unbiased prediction (GBLUP) method versus GBLUP and Bayesian least absolute shrinkage and selection operator (LASSO), in genomic prediction of livestock populations in different genetic architectures. For this purpose, different combinations of heritability (0.1 and 0.5), number of quantitative trait loci (QTL) (100 and 500) and distribution of QTL effects (normal, gamma, beta, Weibull and uniform) were considered. Also, a genome containing six chromosomes, 1 Morgan each, was simulated along which 1500 single-nucleotide polymorphism markers were evenly distributed. The prediction accuracies of the statistical models were obtained using the correlations between true (simulated) and predicted genomic breeding values. Results showed that, in different scenarios, the prediction accuracy using the GBLUP method was higher than that of the Bagging method (P>0.05). When the heritability of the trait, the number of QTL and the distribution of QTL effects were 0.1, 500 and gamma, respectively, the prediction accuracy of Bagging and GBLUP indicated the highest similarity (P = 0.995). With low heritability (0.1) and low number of QTL (100), the maximum superiority of the Bagging method compared to the Bayesian LASSO method was obtained, which was statistically significant only when the distributionof QTL effects followed a gamma distribution (P<0.05). For all three methods, the prediction accuracies decreased as the generation distance between the test and the reference generation increased (P<0.001). In high heritability and when the QTL effects followed the Weibull distribution, all the three methods showed the highest prediction accuracy. In scenario of low heritability (0.1), low number of QTL (100) and gamma distribution for QTL effects, the difference between GBLUP and Bayesian LASSO methods as well as Bagging and Bayesian LASSO methods were statistically significant (P<0.05. No significant differences were observed between the studied methods in other scenarios (P>0.05). The results suggest that when the data are stable, the parametric (GBLUP and Bayesian LASSO) methods provide high prediction accuracy and it is not recommended to use the resampling (Bagging) method.

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