Performance evaluation of Boosting and Bayes A methods by different challenges of genomic architectures in discrete and continue trai

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NADERI Y.

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Journal: JOURNAL OF RUMINANT RESEARCH Year:2019 | Volume:7 | Issue:1 Page(s): 105-120

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Title

Performance evaluation of Boosting and Bayes A methods by different challenges of genomic architectures in discrete and continue trai

Author(s)

NADERI Y. | Issue Writer Certificate

Keywords

Threshold traitsQ1

Genomic predictionQ1

HeritabilityQ2

Machine learningQ1

Linkage disequilibriumQ1

Abstract

Background and objectives: Genomic selection using different statistical methods played an important role in increasing economic efficiency and the genetic improvement of the discrete and continuous traits. In this study, performance of Boosting and Bayes A methods were compared to evaluate genomic breeding values for binary threshold and continuous traits for different marker densities using different genomic architectures. Materials and methods: Genomic data were simulated by QMSim software to reflect variations in Heritability (h2 = 0. 1 and 0. 3), Linkage disequilibrium (LD=low and high), number of QTL (QTL=150 and 450) and marker densities (10k and 50k) for 30 chromosomes. To create discrete threshold phenotypes in training set, individuals per generation were ranked inascending order according to continuous phenotypes of QMSim output. Then, depending on average simulated population, the threshold phenotype of individuals was defined ascode 0 (higher than average trait) and code 1 (lower than average trait). Eventually, genomic estimated breeding values were calculated using Bayes A and Boosting methods to evaluate accuracy of Genomic prediction for threshold and continuous traits. Results: Compare to Bayes A, Boosting algorithm showed a wide range of genomic accuracies to changes inmarker density. Compare to threshold Bayes A, Boosting algorithm demonstrated an increase of 6. 3 and 7. 3% on genomic accuracy of Threshold traits for 10k and 50k SNPs panels, respectively. For traits with continuous phenotypic distribution, performance of Bayes A was much more than Boosting, especially when the sparse panels were used. The structure of genomic architecture including Heritability, number of QTLs and LD were the most important factors affecting the accuracy of Genomic prediction using Bayes A and Boosting methods. In this regard, impact of Heritability on performance of each of these models was more evident. Overall, genomic accuracies of Bayes A and Boosting methods were more sensitive to QTL and LD fluctuations, respectively. For Threshold traits with high density marker panels, the highest and the lowest genomic accuracy were obtained using Boosting (0. 598) and Bayes A (0. 510) methods, respectively, when the data set containing a lot of QTLs was applied. For continuous traits, the highest and the lowest genomic accuracy were obtained using Bayes A (0. 702) and Boosting (0. 569) methods, respectively, when the data set containing a few QTLs was used. Positive effect of increase LD on accuracies of Genomic prediction of Boosting and Bayes A for the sparse panels was much more noticeable than high density panels. Conclusion: Generally, this study indicated that Boosting and Bayes A methods showed their best performance for threshold and continuous traits, respectively.

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APA: Copy

NADERI, Y.. (2019). Performance evaluation of Boosting and Bayes A methods by different challenges of genomic architectures in discrete and continue trai. JOURNAL OF RUMINANT RESEARCH, 7(1 ), 105-120. SID. https://sid.ir/paper/244068/en

Vancouver: Copy

NADERI Y.. Performance evaluation of Boosting and Bayes A methods by different challenges of genomic architectures in discrete and continue trai. JOURNAL OF RUMINANT RESEARCH[Internet]. 2019;7(1 ):105-120. Available from: https://sid.ir/paper/244068/en

IEEE: Copy

Y. NADERI, “Performance evaluation of Boosting and Bayes A methods by different challenges of genomic architectures in discrete and continue trai,” JOURNAL OF RUMINANT RESEARCH, vol. 7, no. 1 , pp. 105–120, 2019, [Online]. Available: https://sid.ir/paper/244068/en

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