Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Genome-wide prediction of discrete traits using bayesian regressions and machine learning | González-Recio and Forni Genetics Selection Evolution 2011 43 7 http content 43 1 7 GSE Ge n et i cs Selection Evolution RESEARCH Open Access Genome-wide prediction of discrete traits using bayesian regressions and machine learning Oscar Gonzalez-Recio 1 Selma Forni2 Abstract Background Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p number of covariates small n number of observations problem have dealt only with continuous traits but there are many important traits in livestock that are recorded in a discrete fashion . pregnancy outcome disease resistance . It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods This study shows two threshold versions of Bayesian regressions Bayes A and Bayesian LASSO and two machine learning algorithms boosting and random forest to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models predictive ability. Results The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals phi correlation was up to 81 greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions The .
