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A deep convolutional neural network approach for predicting phenotypes from genotypes.

Wenlong Ma^1,2, Zhixu Qiu^1,3, Jie Song^1,2

¹State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, 712100, Shaanxi, China.

|August 14, 2018

Summary

This summary is machine-generated.

Deep learning accurately predicts plant traits from genetic data. Combining DeepGS with RR-BLUP enhances genomic selection for improved breeding strategies.

Keywords:

Deep learning Ensemble learning Genomic selection Genotypic marker High phenotypic values Machine learning

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Area of Science:

Plant breeding
Genomics
Bioinformatics

Background:

Genomic selection (GS) predicts plant phenotypes from genotypes.
Accurate prediction is crucial for selecting superior individuals.

Purpose of the Study:

Introduce DeepGS, a deep learning method for phenotype prediction from genotype data.
Evaluate DeepGS performance and its complementarity with existing methods.

Main Methods:

Utilized a deep convolutional neural network (DeepGS) with hidden variables.
Employed convolution, sampling, and dropout strategies to handle high-dimensional genotypic data.
Trained and compared DeepGS on a large genomic selection dataset.

Main Results:

DeepGS accurately predicts phenotypes from genotypes.
DeepGS complements the commonly used RR-BLUP method.
An ensemble learning approach combining DeepGS and RR-BLUP improves individual selection accuracy.

Conclusions:

Deep learning offers a powerful tool for genomic selection.
Ensemble methods enhance the accuracy of selecting individuals with high phenotypic values.
DeepGS and ensemble approaches are packaged for broad application in breeding programs.