Scientific ReportsGeneticsPeer-reviewed journal article
Genome-wide association mapping and genomic prediction unravels CBSD resistance in a Manihot esculenta breeding population
Siraj Ismail KayondoiD, Dunia Pino Del CarpioiD, Roberto Daniel LozanoiD, Alfred OzimatiiD, Marnin D. WolfeiD, Yona BagumaiD, Vernon GraceniD, S. K. OffeiiD, Morag Elizabeth FergusoniD, R. KawukiiD, Jean‐Luc JanninkiD
Abstract
Cassava (Manihot esculenta Crantz), a key carbohydrate dietary source for millions of people in Africa, faces severe yield loses due to two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD). The completion of the cassava genome sequence and the whole genome marker profiling of clones from African breeding programs (www.nextgencassava.org) provides cassava breeders the opportunity to deploy additional breeding strategies and develop superior varieties with both farmer and industry preferred traits. Here the identification of genomic segments associated with resistance to CBSD foliar symptoms and root necrosis as measured in two breeding panels at different growth stages and locations is reported. Using genome-wide association mapping and genomic prediction models we describe the genetic architecture for CBSD severity and identify loci strongly associated on chromosomes 4 and 11. Moreover, the significantly associated region on chromosome 4 colocalises with a Manihot glaziovii introgression segment and the significant SNP markers on chromosome 11 are situated within a cluster of nucleotide-binding site leucine-rich repeat (NBS-LRR) genes previously described in cassava. Overall, predictive accuracy values found in this study varied between CBSD severity traits and across GS models with Random Forest and RKHS showing the highest predictive accuracies for foliar and root CBSD severity scores.
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- research focus: genome assembly
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 88
- FWCI
- 6.59
- Field percentile
- top 2%
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