Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species.

ABSTRACT: Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in

SUBMITTER: Zingaretti LM 

PROVIDER: S-EPMC7015897 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature