Genetic analysis and QTL mapping for multiple biotic stress resistance in cassava

Abstract

In Sub-Saharan Africa cassava (Manihot esculenta Crantz) is one of the most important food crops where more than 40% of the population relies on it as their staple carbohydrate source. Biotic constraints such as viral diseases, mainly Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD), and arthropod pests, particularly Cassava Green Mite (CGM), are major constraints to the realization of cassava's full production potential in Africa. To address these problems, we aimed to map the quantitative trait loci (QTL) associated with resistance to CBSD foliar and root necrosis symptoms, foliar CMD and CGM symptoms in a full-sib mapping population derived from the genotypes AR40-6 and Albert. A high-density linkage map was constructed with 2,125 SNP markers using a genotyping-by-sequencing approach. For phenotyping, clonal evaluation trials were conducted with 120 F 1 individuals for two consecutive field seasons using an alpha-lattice design at Chambezi and Naliendele, Tanzania. Previously identified QTL for resistance to CBSD foliar symptoms were corroborated, and a new putative QTL for CBSD root necrosis identified (qCBSDRNc14AR) from AR40-6. Two QTL were identified within the region of the previously recognized CMD2 locus from this population in which both parents are thought to possess the CMD2 locus. Interestingly, a minor but consistent QTL, qCGM18AR, for CGM resistance at 3 months after planting stage was also detected and co-localized with a previously identified SSR marker, NS346, linked with CGM resistance. Markers underlying these QTL may be used to increase efficiencies in cassava breeding programs.

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

“… Masumba et al (2017) detected two highly significant CMD resistance QTLs on chromosome 12 defining the CMD2 locus, as well as additional putative QTLs on other chromosomes. Similar observations were made by Garcia-Oliveira et al (2020) who found two closely linked loci on chromosome 12 and additional QTLs on chromosomes 9 and 10. Further dissection of the major QTL on chromosome 12 ( Manihot esculenta v5.1 and 6.1 genome assembly) revealed the presence of two possible epistatic loci and/or multiple resistance alleles, which may account for the difference between moderate and strong disease resistance in the germplasm ( Masumba et al, 2017 ; Nzuki et al, 2017 ; Garcia-Oliveira et al, 2020 ; Table 2 ).…”

“…Similar observations were made by Garcia-Oliveira et al (2020) who found two closely linked loci on chromosome 12 and additional QTLs on chromosomes 9 and 10. Further dissection of the major QTL on chromosome 12 ( Manihot esculenta v5.1 and 6.1 genome assembly) revealed the presence of two possible epistatic loci and/or multiple resistance alleles, which may account for the difference between moderate and strong disease resistance in the germplasm ( Masumba et al, 2017 ; Nzuki et al, 2017 ; Garcia-Oliveira et al, 2020 ; Table 2 ). Gaps in the pseudochromosome 12 region containing the CMD2 loci caused by highly repetitive DNA might explain the two separate loci ( Kuon et al, 2019 ).…”

“…Further additional putative QTLs were detected on other chromosomes (3, 4, 5, 6, 7, 10, 12, 15, and 16). In addition to QTLs found on chromosome 8 and 18, new putative QTL for CBSD root necrosis was identified on chromosome 14 ( Garcia-Oliveira et al, 2020 ). Seven QTLs associated with CBSD foliar symptoms were found on chromosomes 4, 6, 15, 17, and 18.…”

“…However, this is subjective and dependent upon personal perceptions. On the other hand, QTL mapping or other applications may require greater resolution or more accuracy ( Garcia-Oliveira et al, 2020 ). Accurate evaluation and novel approaches for cassava disease detection are needed to efficiently assess disease severity ( Chiang et al, 2016 ).…”

“…Root necrosis indexes for CBSD evaluation, which account for the root sample size or their economic value, have been proposed to replace traditional based evaluation ( Kawuki et al, 2019 ). Image analysis has similarly been used ( Garcia-Oliveira et al, 2020 ; Nakatumba-Nabende et al, 2020 ). Analysis of field images combined with various algorithms, including K mean clustering algorithms ( Anderson et al, 2015 ), artificial neutral network ( Abdullakasim et al, 2011 ), and more recently, machine learning techniques and convolutional neutral network ( Owomugisha and Mwebaze, 2016 ; Sambasivam and Opiyo, 2020 ) have provided a more accurate and objective assessment of disease severity and incidence.…”