Cowpea is an important legume crop widely grown in sub-Saharan Africa for food and feed. However, it is largely challenged by bruchid, a serious storage pest resulting in losses in quantity and quality of grains. Therefore, this research was designed to contribute to the breeding of cowpea resistance to bruchid through the identification of candidate genes associated with resistance to bruchid. A total of 217 mini-core cowpea accessions were genotyped and phenotyped for their reactions to bruchid. To determine the genomic regions linked with bruchid resistance, 41,948 polymorphic SNP markers were used. Genome-wide association study identified 11 SNPs linked to the average number of eggs, holes, insect emergence and development period and Dobie susceptibility index. Gene search via Phytozome identified six candidate genes (

Soybean (Glycine max (L.) Merrill) is among the major food and industrial crops grown globally for its high protein and oil content. Lately, in Uganda, soybean reportedly faces challenges with a storage pest, Callosobruchus chinensis. This study was carried out to quantify the damage caused by the pest and identify the sources of resistance in the germplasm in Uganda. The study was conducted at Makerere University Agricultural Research Institute, Kabanyolo (MUARIK) in Uganda, during 2015 and 2016. Callosobruchus chinensis was used to challenge 498 soybean lines under no choice condition, in the laboratory. Results showed no significant differences in eggs laid amongst the different genotypes; however the genotypes performed significantly different (P< 0.05) for adult insect emergence, median development period (MDP), Dobie susceptibility index (DSI), growth index (GI), insect percent emergence (% IE) and seed weight loss (%WL). Genotype AVRDC G8527 had the lowest % IE (6.31), DSI (0.7), % WL (0.02) and GI (0.07), suggesting high resistance. Weight loss of up to 27% was recorded in genotype USA 7. There was a strong positive correlation between number of adults that emerged with DSI (r=0.87), eggs (r=0.88), % weight loss (r=0.73), and growth index (r=0.996). Cluster analysis revealed that AVRDC G8527, a resistant genotype was closely related to S-line 13.2A, a moderate resistant genotype. Regression analysis, revealed that adult bruchid emergence explain seed weight loss with 62% coefficient of determination; while seed colour could be used to determine genotype DSI with up to 74% coefficient of determination. Genotypes AVRDC G8527 and G89 were identified as the most resistant genotypes based on levels of DSI.

Adzuki bean bruchid (Callosobruchus chinensis) is a significant pest of soybean in Uganda. To sustainably manage this pest, utilization of resistant soybean varieties is the key solution. Development of resistant varieties needs knowledge on modes of inheritance which is crucial in selection of parent materials. To identify parents, a study was initiated to determine the gene action and mode of inheritance of resistance to bruchids in soybean. Nine parental lines were crossed in a full-diallel at Makerere University Agricultural Institute, Uganda. The generated F1s were advanced to F2 and seeds were evaluated for response to bruchid infestation in a randomised complete block design. Ten seeds were infested with 10 randomly selected unsexed 1-3 day old bruchids. Genotypes showed significant differences in seed weight loss (swl), adult bruchid emergence (ABE) and Dobie susceptibility index (DSI) indicating that these parameters could be used to screen genotypes in genetic analysis. Mean squares of general combining ability (GCA) were significant (P &lt; 0.05) for swl, DSI and number of ABE from the F2 seeds indicating additive gene action. Susceptibility parameters ABE and DSI showed significant specific combining ability (SCA) indicating non-additive gene action. Resistance was influenced by maternal effects indicating that direction of the cross was important. Genotypes S-Line 9.2 and S-Line 13.2A showed negative significant GCA effects for at least two of the susceptibility parameters indicating that they were the best parents for bruchid resistance breeding. The study established that additive, non additive and maternal effects governed the gene expression in soybean resistance to bruchids.

Online databases containing genetic information are crucial to extract new candidate genes from existing data and web‐based resources. The objective of this study was, therefore, to predict putative candidate genes associated with resistance to SBR in line UG‐5 and understand their functions using different bioinformatics tools from the online available databases. The physical positions for the flanking markers of the identified putative QTLs were searched on the SoyBase database genome browser based on Glyma 1.01 assembly. The putative candidate genes and annotated functions of the surrounding genes were discovered in the vicinity using SoyBase and Phytozome databases. A total of 18 putative candidate genes were predicted on approximately 482.7 kb region of QTL‐3 (chromosome 18), among which, six putative candidate genes were found to encode leucine‐rich repeat (LRR), Ser/Thr protein phosphatase, leucine‐rich repeat receptor‐like protein kinase (LRR‐RLK) and chitinase‐related proteins, which are associated with plant defence signalling pathways. Moreover, F‐box and leucine‐rich repeat, glycosyltransferase family member and serine/threonine‐protein phosphatase 2A catalytic subunit coding genes were predicted on the novel putative QTL detected on chromosome 9. This information could, therefore, be used for further prediction and annotation of candidate genes from sequenced regions of line UG‐5 as these putative candidate genes were predicted from the Glyma 1.01 assembly.

Soybean rust, Phakopsora pachyrhizi, is one of the most serious and widespread foliar diseases of soybean causing high yield losses worldwide. The objective of this study was to identify and map quantitative trait loci (QTLs) resistant to soybean rust in genotype UG 5. Ninety-seven F 2 mapping plants, obtained from a cross between Wondersoya and UG 5, were used for this study. Quantitative trait locus analysis using QTL IciMapping software identified three putative QTLs associated with soybean rust (SBR) on chromosomes 6, 9 and 18 with logarithms of odds (LOD) scores ranging from 3.47 to 8.23 and phenotypic variance explained by the QTLs ranging from 18.3 to 25.6%. The putative QTL detected on chromosome 9 is novel and has not been reported elsewhere. The putative QTLs identified in this study could help to facilitate SBR resistance breeding towards efficient markerassisted selection approach and gene pyramiding leading to the development of durable resistance.