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New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

Osval A Montesinos-López¹, Javier Martín-Vallejo², José Crossa³

¹Facultad de Telemática.

G3 (Bethesda, Md.)

|March 13, 2019

Summary

Deep learning models can analyze multiple traits with mixed phenotypes for genomic selection. While the multiple-trait deep learning with mixed phenotypes (MTDLMP) model showed modest gains for continuous traits, it did not improve accuracy for binary or ordinal traits compared to univariate models.

Keywords:

GenPred Genomic Prediction Shared Data Resources deep learning genomic selection mixed phenotypes (binary ordinal and continuous)multiple-trait plant breeding

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Area of Science:

Animal and Plant Breeding
Genetics
Machine Learning

Background:

Multiple-trait experiments with mixed phenotypes (binary, ordinal, continuous) are common in breeding programs.
Existing statistical models often fail to leverage correlations between mixed traits, limiting prediction accuracy in genomic selection (GS).
Breeders typically use univariate models, missing potential accuracy gains from multi-trait analyses.

Purpose of the Study:

To propose and evaluate deep learning models for analyzing multiple traits with mixed phenotypes in GS.
To compare the prediction accuracy of multiple-trait deep learning with mixed phenotypes (MTDLMP) against univariate deep learning (UDL) models.
To assess the impact of genotype × environment (G×E) interaction on prediction performance.

Main Methods:

Developed and applied MTDLMP models for simultaneous prediction of mixed phenotypes.
Compared MTDLMP with UDL models, both with and without genotype × environment (G×E) interaction terms.
Used Pearson's correlation for continuous traits and percentage of cases correctly classified (PCCC) for binary/ordinal traits to evaluate prediction accuracy.

Main Results:

A modest gain in prediction accuracy was observed for continuous traits using MTDLMP compared to UDL.
No significant difference in prediction accuracy was found between MTDLMP and UDL for binary and ordinal traits.
Both models demonstrated improved prediction performance when including the genotype × environment (G×E) interaction term (WI) versus excluding it (I).

Conclusions:

The MTDLMP model offers a viable alternative for simultaneous prediction of mixed phenotypes in GS.
While MTDLMP shows promise for continuous traits, its advantage over univariate models for binary and ordinal traits in this study was limited.
Incorporating genotype × environment (G×E) interaction generally enhances prediction performance in both univariate and multiple-trait deep learning approaches.