QTL mapping for pest and disease host plant resistance in cassava cultivars Kiroba and AR37-80 and coincidence of QTL with introgression regions from Manihot glaziovii

Abstract

Cassava (Manihot esulenta Crantz.) is a staple food crop for more than 800 million people worldwide. It is drought tolerant and offers a flexible harvesting regime since the roots can remain in the soil and be harvested when needed. It is a food security crop when cereal crops fail. Biotic and abiotic stresses including pest and diseases negate this potential. Its heterozygous nature, long growing cycle and low seed yield per pollination poses challenges in breeding. In addition, it is highly outcrossing making it difficult to develop an adequately sized F2 population hence limiting genetic studies to F1 progenies. Cassava brown streak disease (CBSD) has emerged to be the great threat to cassava production reducing useable roots or leading to total crop loss, and if not checked it can impact more than 200 million people in Africa who depend on the crop for their food and income generation.

The plausible approach to combat CBSD is to combine breeding for host plant resistance with sanitation measures and the planting of virus-free stakes. The breeding for host plant resistance should be performed as quickly and efficiently as possible, taking advantage of genomic, transformation and molecular marker technology. Conventional breeding, which does not use data generated from molecular tools, takes up to 10 years to deliver a new cultivar since the plants have to be grown for 12 months before selection can be made. If molecular markers were found to be associated with field resistance, then F1 progeny generated in a breeding program could be screened and selected at the seedling stage, thereby drastically reducing the breeding cycle and providing an accurate way of efficiently pyramiding resistance from different sources.

In this project, quantitative trait loci (QTL) mapping for resistance to CBSD, cassava Mosaic disease (CMD), and cassava green mites (CGM) was performed using an F1 mapping population developed between CBSD resistant Tanzanian landrace, Kiroba, and a susceptible breeding clone, AR37-80. This aimed to construct a SNP based linkage map using the segregating population and provide a tool to identify QTL. The study investigated the presence of genomic regions in Kiroba derived from M. glaziovii and their contribution to the field resistance observed in Kiroba. The introgression regions in Kiroba were compared to those from Namikonga, the well-known CBSD resistant cultivar and other genotypes of African origin to understand the source of their resistance.

The results show that only two QTL are linked to CBSD root necrosis and are located on chromosomes V and XII, while seven are associated with CBSD foliar symptoms only and are located on chromosomes IV, VI, XVII, and XVIII. The QTL on chromosomes XI and XV are linked with both CBSD foliar and root necrosis symptoms. Two QTL found on chromosome XII and XIV are linked to CMD, while two QTL located on chromosomes V and X are linked to CGM resistance.

The analysis of introgression regions in Kiroba revealed the existence of large Manihot glaziovii like regions on chromosome I, XVII, and XVIII. The introgression segments on chromosomes XVII and XVIII overlap with QTL associated with CBSD foliar symptoms. These regions contain domains associated with host plant disease resistance. The introgression region on chromosome I in Kiroba is of a different haplotype to the characteristic “Amani haplotype” found in the landrace Namikonga and other genotypes analyzed in this study. Kiroba also does not have a large introgression block on chromosome IV found in other genotypes. Kiroba is closely related to a sampled Tanzanian “tree cassava.” This supports the observation that some of the QTL associated with CBSD resistance in Kiroba are different to those observed in Namikonga.

This study provides an understanding of the genetic basis of the field resistance observed in the local cassava landraces, the genomic regions contributing to the resistance and the source of the resistance. This information is valuable in pyramiding QTL for host plant disease resistance.