Predicting phenotypes from novel genomic markers using deep learning.

ABSTRACT: Genomic selection (GS) models use single nucleotide polymorphism (SNP) markers to predict phenotypes. However, these predictive models face challenges due to the high dimensionality of genome-wide SNP marker data. Thanks to recent breakthroughs in DNA sequencing and decreased sequencing cost, the study of novel genomic variants such as structural variations (SVs) and transposable elements (TEs) become increasingly prevalent. In this article, we develop a deep convolutional neural network model, NovGMDeep, to predict phenotypes using SVs and TEs markers for GS. The proposed model is trained and tested on samples of Arabidopsis thaliana and Oryza sativa using k-fold cross-validation. The prediction accuracy is evaluated using Pearson's Correlation Coefficient (PCC), mean

SUBMITTER: Sehrawat S 

PROVIDER: S-EPMC10132579 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature