The importance of disease incidence rate on accuracy of genomic selection methhods
Publish place: 7 th International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICSDA07_408
تاریخ نمایه سازی: 10 اردیبهشت 1403
Abstract:
In order to o predict genomiic accuracy of binary traits based on different rates of disease incidence, two machine learning algorithms including Boosting and Random Forest (RF) as well as threshold BayesA (TBBA) as well as genomic genomic best linear unbiased prediction (GBLUP) were applied. For this purpose, the predictive performance of each of these models was evaluated in different co mbinationsof genomic architecture using imputed (۹۵, ۷۵ and ۵۰% missing rate) and their original ۵۰k genotypes.The highest predictive ability belonged to GBLUP, RF, Boosting and TBA methods were when the rate of disease incidence into the training set was ۱۶ percent.In general, for different genomic architectures, the Boosting method performed better than the TBA, GBLUP and RF methods for the accuracy of genomic prediction under all scenarios and proportions of the markers set being imputed
Keywords:
Discrete traits;Dissease susceptibility;Machine learning
Authors
Yousef Naderi
Associate Professor, Department of Animal Science, Astara Branch, Islamic Azad University, Astara,Iran