Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods.

ABSTRACT: The analysis of large genomic data is hampered by issues such as a small number of observations and a large number of predictive variables (commonly known as "large P small N"), high dimensionality or highly correlated data structures. Machine learning methods are renowned for dealing with these problems. To date machine learning methods have been applied in Genome-Wide Association Studies for identification of candidate genes, epistasis detection, gene network pathway analyses and genomic prediction of phenotypic values. However, the utility of two machine learning methods, Gradient Boosting Machine (GBM) and Extreme Gradient Boosting Method (XgBoost), in identifying a subset of SNP makers for genomic prediction of breeding values has never been explored before. In this study, using 38,08

SUBMITTER: Li B 

PROVIDER: S-EPMC6039760 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature