Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants.

ABSTRACT:

Background

Structural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.

Results

We used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC

SUBMITTER: Vourlaki IT 

PROVIDER: S-EPMC11316328 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature