Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.

Wide variation for morphological traits exists in Brassica rapa and the genetic basis of this morphological variation is largely unknown. Here is a report on quantitative trait loci (QTL) analysis of flowering time, seed and pod traits, growth-related traits, leaf morphology, and turnip formation in B. rapa using multiple populations. The populations resulted from crosses between the following accessions: Rapid cycling, Chinese cabbage, Yellow sarson, Pak choi, and a Japanese vegetable turnip variety. A total of 27 QTL affecting 20 morphological traits were detected, including eight QTL for flowering time, six for seed traits, three for growth-related traits and 10 for leaf traits. One major QTL was found for turnip formation. Principal component analysis and co-localization of QTL indicated that some loci controlling leaf and seed-related traits and those for flowering time and turnip formation might be the same. The major flowering time QTL detected in all populations on linkage group R02 co-localized with BrFLC2. One major QTL, controlling turnip formation, was also mapped at this locus. The genes that may underly this QTL and comparative analyses between the four populations and with Arabidopsis thaliana are discussed.

Cassava (Manihot esculenta Crantz) is a crucial, under-researched crop feeding millions worldwide, especially in Africa. Cassava mosaic disease (CMD) has plagued production in Africa for over a century. Biparental mapping studies suggest primarily a single major gene mediates resistance. To investigate this genetic architecture, we conducted the first genome-wide association mapping study in cassava with up to 6128 genotyping-by-sequenced African breeding lines and 42,113 reference genome-mapped single-nucleotide polymorphism (SNP) markers. We found a single region on chromosome 8 that accounts for 30 to 66% of genetic resistance in the African cassava germplasm. Thirteen additional regions with small effects were also identified. Further dissection of the major quantitative trait locus (QTL) on chromosome 8 revealed the presence of two possibly epistatic loci and/or multiple resistance alleles, which may account for the difference between moderate and strong disease resistances in the germplasm. Search of potential candidate genes in the major QTL region identified two peroxidases and one thioredoxin. Finally, we found genomic prediction accuracy of 0.53 to 0.58 suggesting that genomic selection (GS) will be effective both for improving resistance in breeding populations and identifying highly resistant clones as varieties.

Cassava (Manihot esculenta) is a crucial, under-researched crop feeding millions worldwide, especially in Africa. Cassava mosaic disease (CMD) has plagued production in Africa for over a century. Bi-parental mapping studies suggest primarily a single major gene mediates resistance. To be certain and to potentially identify new loci we conducted the first genome-wide association mapping study in cassava with 6128 African breeding lines. We also assessed the accuracy of genomic selection to improve CMD resistance. We found a single region on chromosome 8 accounts for most resistance but also identified 13 small effect regions. We found evidence that two epistatic loci and/or alternatively multiple resistance alleles exist at major QTL. We identified two peroxidases and one thioredoxin as candidate genes. Genomic prediction of additive and total genetic merit was accurate for CMD and will be effective both for selecting parents and identifying highly resistant clones as varieties.

Cassava (Manihot esculenta Crantz) is an important security crop that faces severe yield loses due to cassava brown streak disease (CBSD). Motivated by the slow progress of conventional breeding, genetic improvement of cassava is undergoing rapid change due to the implementation of quantitative trait loci mapping, Genome-wide association mapping (GWAS), and genomic selection (GS). In this study, two breeding panels were genotyped for SNP markers using genotyping by sequencing and phenotyped for foliar and CBSD root symptoms at five locations in Uganda. Our GWAS study found two regions associated to CBSD, one on chromosome 4 which co-localizes with a Manihot glaziovii introgression segment and one on chromosome 11, which contains a cluster of nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. We evaluated the potential of GS to improve CBSD resistance by assessing the accuracy of seven prediction models. Predictive accuracy values varied between CBSD foliar severity traits at 3 months after planting (MAP) (0.27–0.32), 6 MAP (0.40–0.42) and root severity (0.31–0.42). For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. Our results provide an insight into the genetics of CBSD resistance to guide CBSD marker-assisted breeding and highlight the potential of GS to improve cassava breeding.