Bambara groundnut (Vigna subterranea (L.) Verdc.) is an indigenous underutilized legume that has the potential to improve food security in semi-arid Africa. So far, there are a lack of reports of controlled breeding populations that could be used for variety development and genetic studies. We report here the construction of the first genetic linkage map of bambara groundnut using a F3 population derived from a "narrow" cross between two domesticated landraces (Tiga Nicuru and DipC) with marked divergence in phenotypic traits. The map consists of 238 DArT array and SSR based markers in 21 linkage groups with a total genetic distance of 608.3 cM. In addition, phenotypic traits were evaluated for a quantitative trait loci (QTL) analysis over two generations. A total of 36 significant QTLs were detected for 19 traits. The phenotypic effect explained by a single QTL ranged from 11.6% to 49.9%. Two stable QTLs were mapped for internode length and growth habit. The identified QTLs could be useful for marker-assisted selection in bambara groundnut breeding programmes.
Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.