Waldmann, Patrik
- Department of Animal Biosciences, Swedish University of Agricultural Sciences
Abstract
Genome-wide prediction (GWP) has become the state-of-the art method in artificial selection. Data sets often comprise number of genomic markers and individuals in ranges from a few thousands to millions. Hence, computational efficiency is important and various machine learning methods have successfully been used in GWP. Neural networks (NN) and deep learning (DL) are very flexible methods that usually show outstanding prediction properties on complex structured data, but their use in GWP is nevertheless rare and debated. This study describes a powerful NN method for genomic marker data that can easily be extended. It is shown that a one-dimensional convolutional neural network (CNN) can be used to incorporate the ordinal information between markers and, together with pooling and l (1)-norm regularization, provides a sparse and computationally efficient approach for GWP. The method, denoted CNNGWP, is implemented in the deep learning software Keras, and hyper-parameters of the NN are tuned with Bayesian optimization. Model averaged ensemble predictions further reduce prediction error. Evaluations show that CNNGWP improves prediction error by more than 25% on simulated data and around 3% on real pig data compared with results obtained with GBLUP and the LASSO. In conclusion, the CNNGWP provides a promising approach for GWP, but the magnitude of improvement depends on the genetic architecture and the heritability.
Keywords
genomic selection; machine learning; deep learning; dominance; QTL; livestock breeding
Published in
Frontiers in Genetics
2020, volume: 11, article number: 25
Publisher: FRONTIERS MEDIA SA
SLU Authors
UKÄ Subject classification
Genetics and Breeding in Agricultural Sciences
Bioinformatics (Computational Biology)
Publication identifier
- DOI: https://doi.org/10.3389/fgene.2020.00025
Permanent link to this page (URI)
https://res.slu.se/id/publ/105121