Enhancing tobacco (Nicotiana tabaccum L.) breeding efficiency utilizing GBLUP through SSR markers for superior parental selection based on leaf quality traits

Abstract

Tobacco (Nicotiana tabaccum L.) is considered to be an industrial and medicinal plant that plays an important role in the economies of most countries. The present study demonstrated how the genomic best linear unbiased predictor (GBLUP) method could determine the future breeding potential of a tobacco panel by means of 26 SSR fingerprinting data. A set of 71 genotypes of tobacco considering 11 agro-morphological and leaf chloride content of a qualitative character were assessed during two consecutive years under field conditions. Results revealed that GBLUP could efficiently predict the breeding value (BV) of studied characters. Considering the total ranks of each genotype across studied characters, genotypes, C.H.T.269-12e”, C.H.T.266-6, SS298-2, C.H.T.209.12e, Triumph, and Ohdaruma had the highest predicted BVs and, therefore, these genotypes are good candidates for parental selection. Based on BVs data, the studied characters were classified into groups whose chemical characteristics were distinguished from others. Cluster analysis of this tobacco panel based on BVs leads to four heterotic groups, and the combination of their information with the total ranks of each genotype across studied characters can guide tobacco breeders in selecting desirable and effective parents.