Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Abstract

\textbf{Background:}Rhizomania counts as the most important disease in sugar beet (\textit{Beta vulgaris} L.) for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. \newline \textbf{Methods:} First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. \newline \textbf{Results:} From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. \newline \textbf{Conclusions:} Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.