Adding genome-wide genotypic information to a tobacco (Nicotiana tabacum) breeding programme

Abstract

Large-scale genotypic information can be used to increase genetic gain in plant breeding programmes. In this research, we evaluated the following: (i) statistical models that could be useful in selection of superior tobacco genotypes in absence of phenotypic information; (ii) the applicability of genome-wide selection (GWS) for predicting tobacco hybrid performance, and (iii) correlations between genetic divergence of parental lines and F1 hybrid performance. We crossed 13 inbred lines of flue-cured Virginia tobacco crossed in a diallel scheme to generate 72 hybrid combinations and evaluated them in two field environments. Genotype by sequencing was used for single nucleotide polymorphism (SNP) marker generation, and prediction model validation was performed with different levels of missing information. Hybrid performance was predicted using only the genotypic and phenotypic information. We found genetic divergence among lines to be uncorrelated with hybrid performance or heterosis. Genotype × environment interaction affects GWS efficiency, however, and an index that incorporates both genotypic and phenotypic information improves selection accuracy. Tobacco hybrid prediction utilizing GWS data can be used as additional information to increase the response to selection.