Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare

Abstract

Hordeum vulgare, like many other crops, suffers from the reduction of genetic diversity caused by climate changes. Therefore, it is necessary to improve the performance of its breeding. Nowadays, the area of interest in current research focuses on indirect selection methods based on computational prediction modeling. This study deals with dynamic metabolomic prediction based on genomic data consisting of 33,005 single nucleotide polymorphisms. Metabolomic data include 128 metabolites belonging to 25 Halle exotic barley families. The main goal of this study is creating dynamic metabolomic predictions using different approaches chosen upon various publications. Our created models will be helpful for the prediction of phenotype or for revealing important traits of Hordeum vulgare.