Volume 8, Issue 15 (6-2017)                   Res Anim Prod 2017, 8(15): 149-154 | Back to browse issues page

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(2017). Genomic Evaluation of Threshold Traits with Different Genetic Architecture using Bayesian Approaches. Res Anim Prod. 8(15), 149-154. doi:10.29252/rap.8.15.149
URL: http://rap.sanru.ac.ir/article-1-761-en.html
Genomic Evaluation of Threshold Traits with Different Genetic Architecture using Bayesian Approaches
Abstract:   (5570 Views)

The current study was carried out to evaluate accuracy of some Bayesian methods for genomic breeding values prediction for threshold traits with different types of genetic architecture based on distribution of gene effect and QTL numbers. A genome consisted of 3 chromosomes of 100 CM with 2000 single nucleotide polymorphisms (SNP) was simulated. The QTL numbers were 0.01, 0.05 and 0.1 of total number of SNPs whose effects were simulated by uniform, normal and gamma distributions. The studied threshold traits were either one-threshold (survival) or two-threshold (litter size). Genomic estimated breeding values were predicted by five regression methods including Bayesian Ridge Regression (BRR), Bayes A, Bayes B, Bayes C, and Bayes LASSO. Comparison of prediction accuracy of these methods (correlation between real and estimated breeding value) showed that Bayesian methods are powerful for genomic evaluation and there were no significant differences among them. The proficiency of these methods for one-threshold trait was significantly higher compared to two-threshold trait. Non-significant and irregular variance was observed in accuracy of prediction these methods between different QTL numbers and statistical distributions. Also the results showed that increasing in distance (generation) between reference and target populations will lead to decline in accuracy of prediction due to breakdown of LD between QTL and marker.

Keywords: Bayesian methods, Genome, Genetic architecture, Threshold traits
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Type of Study: Research | Subject: Special
Received: 2017/06/17 | Accepted: 2017/06/17
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