Home About Browse Search

Sparse Convolutional Neural Networks for Genome-Wide Prediction

Waldmann, Patrik and Pfeiffer, Christina and Meszaros, Gabor (2020). Sparse Convolutional Neural Networks for Genome-Wide Prediction. Frontiers in Genetics. 11 , 1-9
[Research article]

[img] PDF - Published Version
Available under License Creative Commons Attribution.



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.

Authors/Creators:Waldmann, Patrik and Pfeiffer, Christina and Meszaros, Gabor
Title:Sparse Convolutional Neural Networks for Genome-Wide Prediction
Series Name/Journal:Frontiers in Genetics
Year of publishing :2020
Page range:1-9
Number of Pages:9
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610)
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
Keywords:genomic selection, machine learning, deep learning, dominance, QTL, livestock breeding
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000517400800001
ID Code:16870
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:05 May 2020 08:51
Metadata Last Modified:15 Jan 2021 19:47

Repository Staff Only: item control page


Downloads per year (since September 2012)

View more statistics