Browsing by Author "Machida, L."

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    Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization
    (Genes, 2020) Badji, A.; Kwemoi, D. B.; Machida, L.; Okii, D.; Mwila, N.; Agbahoungba, S.; Kumi, F.; Ibanda, A.; Bararyenya, A.; Solemanegy, M.; Odong, T.; Wasswa, P.; Otim, M.; Asea, G.; Ochwo-Ssemakula, M.; Talwana, H.; Kyamanywa, S.; Rubaihayo, P.
    Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAWandMW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and di erentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.
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    Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination
    (ResearchGate, 2020) Badji, A.; Machida, L.; Kwemoi, D. B.; Kumi, F.; Okii, D.; Mwila, N.; Agbahoungba, S.; Ibanda, A.; Bararyenya, A.; Nghituwamhata, S. N.; Odong, T.; Wasswa, P.; Otim, M.; Ochwo-Ssemakula, M.; Talwana, H.; Asea, G.; Kyamanywa, S.; Rubaihayo, P.
    Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the degree of kinship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.
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    Maize resistance to stem borers and storage pests: The need for new genetic and functional genomics approaches in future research
    (African Journal of Rural Development, 2017) Badji, A.; Otim, M.; Machida, L.; Odong, T.L.; Kyamanywa, S.; Rubaihayo, P.
    Insect pests are primary constraints in maize (Zea mays) production in many places in sub-Saharan Africa. Stem borers and storage pests are responsible for severe yield losses and health hazards due to mycotoxin contamination. Integrated pest management (IPM) strategies have moved from control methods and transgenic resistance to recognizing the necessity of host plant resistance (HPR) especially in the context of an ever changing climate and its forecasted negative consequences. For that, a wealth of scientific knowledge has been generated over the years although the goals are far to be reached. Here, we first review current literature on maize resistance mechanisms as regards to insect herbivory. We show that there are numerous insect species that feed on maize before narrowing down to stem borers and storage pests. We also look at the basis of maize resistance in terms of its biochemical components and analyze the progress of genetic studies in terms of QTL mapping and trait genes identification. Finally, we highlight the usefulness of new genetic and functional genomic approaches in underpinning the genetic basis of maize resistance to insect pests in general and particularly stem borers and storage pests.