Distribution of single nucleotide polymorphism markers towards tagging sources of resistance to cassava brown streak disease in cassava

Abstract

Cassava roots represent the future of food and income generation for over 800 million people in the world however, its production is threatened by virus disease; Cassava brown streak disease (CBSD). Biotechnology approaches are fast and powerful methodologies in cassava improvement and breeding. Construction of high-density and high quality genetic map in cassava would be of great benefit in combating CBSD. Conventional study was conducted in Naliendele, Kibaha and Dodoma for Tanzania and Molecular work performed at the International Livestock Research Institute, Kenya and University of Berkeley in United States of America. This study involved assessing the integrity of F1 population from a cross between AR40-6 x Albert cassava cultivars using simple sequence repeat (SSR) polymorphisms. An F1 population of 156 individuals were developed. Population evaluation resulted into 72% individuals as true F1 hybrids, 18.7% were non hybrids and 8.2% were selfed individual plants. Evaluation of F1 population validated SSR markers to be useful and efficient tools in identification of true F1 hybrids in controlled crosses. On the other hand, the true F1hybrids obtained were used to construct high dense SNP based linkage map using high throughput genotyping by sequencing (GBS) approach. The GBS is simple, low cost and de novo sequencing that makes an attractive option for large number of markers and individuals. Linkage analysis resulted into comprehensive genetic map with 19 linkage groups with a total of 4784 SNP markers: 2159 of these were mapped to the female genetic map, 2169 to the male map, and 3449 SNP markers to the integrated genetic map. Comprehensive genetic map encompassed 4250cM with mean distance of 1.26cM between the markers. This high density SNP based genetic linkage map of cassava can be used as base in locating genes controlling resistance to cassava brown streak disease and other genomic studies such as QTL detection, sequence assembly and genome comparison of the crop.