Comparing genomic prediction models for genomic selection of traits with additive and dominance genetic architecture
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 148
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شناسه ملی سند علمی:
JR_KLST-11-1_008
تاریخ نمایه سازی: 20 خرداد 1402
چکیده مقاله:
ABSTRACTThe purpose of this research was to compare different statistical methods such as GBLUP, BayesA, BayesB, BayesC, BayesL, Ridge regression, Boosting and SVM for genomic evaluation of traits with additive and dominance genetic architecture. A genome consisting of ۵ chromosomes was simulated, with ۱۰۰۰ single nucleotide polymorphism markers (SNP) uniformly distributed on each chromosome. In two different scenarios, ۵۰ and ۵۰۰ quantitative trait loci (QTL) were considered and in each scenario of QTL number, ۰.۰۰, ۱۰, ۲۰, ۵۰ and ۱۰۰% of QTLs were given dominance genetic effect. The prediction accuracy, bias and reliability of genomic breeding values were used for analyzing the results and comparing the methods. The results showed that not separating the dominance effects from the additive effects lead to a decrease in the accuracy and reliability and an increase in the bias of the predicted genomic breeding values. In all examined scenarios of the QTL number and percentages of QTLs with dominance effect, the Bayesian methods had higher prediction accuracy and reliability and their predictions had the least bias. Boosting predicted the genomic breeding values with the lowest accuracy and reliability and highest bias. The performance of SVM and Ridge regression was better than Boosting, but lower than Bayesian methods and GBLUP. In terms of computing speed, GBLUP and Boosting were, respectively, the fastest and the slowest method. It can be concluded that to increase the efficiency of genomic selection, first, the dominance genetic effects need to be included in the model and, second, methods with the highest predictive performance should be used.
کلیدواژه ها:
نویسندگان
Seyed Javad Khorami
Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
Farhad Ghafouri-Kesbi
Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
Ahmad Ahmadi
Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
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