Genomic Prediction With Different Heritability, QTL, and SNP Panel Scenarios Using Artificial Neural Network

Sunday O Peters,Kadir Kizilkaya,Milton G Thomas,Mahmut Sinecen

doi:10.1109/access.2020.3015814

Sunday O Peters, Kadir Kizilkaya + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3015814

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Abstract

A simulation study was carried out to determine the genomic prediction performance of Artificial Neural Network model with 1 to 10 neurons (ANN-1-10) using 3361 SNP markers from BovineSNP50 (Infinium BeadChip, Illumina, San Diego, CA) on the first chromosome of Brangus beef cattle as a pilot study for two traits with heritabilities of 25% (h 2 T1 = 0.25) and 50% (h 2 T2 = 0.5) determined either by 50, 100, 250 or 500 QTL selected randomly from SNP markers. QTL effects were sampled from a multivariate normal distribution. Genomic predictions were carried out by Feed Forward Multi-Layer Perceptron ANN-1-10 with the back-propagation of errors algorithm employing the Levenberg-Marquardt algorithm to locate the optimal solution. Three sets of SNP panels were used for genomic prediction: only QTL (Panel-1), all SNP markers, including the QTL (Panel-2), and all SNP, excluding the QTL (Panel-3). Correlations between true genetic merits (breeding values) and predicted phenotypes from 10-fold disjoint cross-validation were used to assess predictive ability of ANN-1-10. Results indicated that an increase in heritability resulted in an increased predictive performance of ANN-1-10 for all scenarios. SNP Panels had a greater chance of including markers in LD with QTL, allowed the possibility of predicting the effect of each QTL from the collective action of several markers and performed better than the Panel including only QTL. In the other Panels, predictive performance of ANN-1-10 increased inconsistently with the number of neurons, which indicated that a few numbers of neurons were not be enough to learn specification of data and could cause the under fitting problem. Therefore, high number of neurons could be needed to learn relevant details of the data in the applications of ANN.

Highlights

Economic important traits such as milk/protein or grain yields, in animal and plant productions, respectively, are measured on continuous scales and assumed to be determined by large numbers of genes with small additive effects [1]
Genomic prediction for two traits with h2T1 = 0.25 and h2T2 = 0.5 determined either by QTL-50, QTL-100, QTL-250 or QTL-500 were studied by using artificial neural networks (ANN)-1-10 architecture with QTL and SINGLE NUCLEOTIDE POLYMORPHISM (SNP) markers through Panel-1, Panel-2 (QTL and SNP markers) and Panel-3 as input to the ANN
Results from ANN-1-10-neuron analysis with different QTL and SNP marker panels applied to two traits with heritabilities of 25% (h2T1 = 0.25) or 50% (h2T2 = 0.5) determined either by QTL-50, QTL-100, QTL-250 or QTL-500 scenarios suggest that:

Summary

Introduction

Economic important (complex quantitative) traits such as milk/protein or grain yields, in animal and plant productions, respectively, are measured on continuous scales and assumed to be determined by large numbers of genes with small additive effects [1]. The study of [2] pioneered the genomic selection which has been utilized to understand complexity of traits and to account for the effects that are contributed by these genes using SNP markers. For the application of genomic selection in plant and animal breeding, numerous parametric statistical methods were developed. The methods of BayesA and BayesB were developed by assuming Scaled-t and Scaled-t mixture distributions for SNP marker effects [2] and were extended by assuming Gaussian (called Bayesian ridge regression in [3]), Gaussian mixture

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