Browsing by Author "Ibanda, Angele"

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    Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils
    (Plants, 2021) Badji, Arfang; Machida, Lewis; Kwemoi, Daniel Bomet; Kumi, Frank; Okii, Dennis; Mwila, Natasha; Agbahoungba, Symphorien; Ibanda, Angele; Bararyenya, Astere; Ndapewa Nghituwamhata, Selma; Odong, Thomas; Wasswa, Peter; Otim, Michael; Ochwo-Ssemakula, Mildred; Talwana, Herbert; Asea, Godfrey; Kyamanywa, Samuel; Rubaihayo, Patrick
    Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAWresistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.
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    Genetic diversity and population structure of Peronosclerospora sorghi isolates of Sorghum in Uganda
    (International Journal of Environment, Agriculture and Biotechnology (IJEAB), 2018) Kumi, Frank; Agbahoungba, Symphorien; Badji, Arfang; Mwila, Natasha; Ibanda, Angele; Anokye, Michael; Odong, Thomas; Wasswa, Peter; Ochwo- Ssemakula, Mildred; Tusiime, Geoffrey; Biruma, Moses; Kassim, Sadik; Rubaihayo, Patrick
    Sorghum is the third most important staple cereal crop in Uganda after maize and millet. Downy mildew disease is one of the most devastating fungal diseases which limits the production and productivity of the crop. The disease is caused by an obligate fungus, Peronosclerospora sorghi (Weston & Uppal) with varying symptoms. Information on the genetic diversity and population structure of P.sorghi in sorghum is imperative for the screening and selection for resistant genotypes and further monitoring possible mutant(s) of the pathogen. Isolates of P. sorghi infecting sorghum are difficult to discriminate when morphological descriptors are used. The use of molecular markers is efficient, and reliably precise for characterizing P. sorghi isolates. This study was undertaken to assess the level of genetic diversity and population structure that exist in P. sorghi isolates in Uganda.
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    Maize Combined Insect Resistance Genomic Regions and Their Co-localization With Cell Wall Constituents Revealed by Tissue-Specific QTL Meta-Analyses
    (Plant Science, 2018) Badji, Arfang; Otim, Michael; Machida, Lewis; Odong, Thomas; Bomet Kwemoi, Daniel; Okii, Dennis; Agbahoungba, Symphorien; Mwila, Natasha; Kumi, Frank; Ibanda, Angele; Mugo, Stephen; Kyamanywa, Samuel; Rubaihayo, Patrick
    Combinatorial insect attacks on maize leaves, stems, and kernels cause significant yield losses and mycotoxin contaminations. Several small effect quantitative trait loci (QTL) control maize resistance to stem borers and storage pests and are correlated withsecondary metabolites. However, efficient use of QTL in molecular breeding requires a synthesis of the available resistance information. In this study, separate meta-analyses of QTL of maize response to stem borers and storage pests feeding on leaves, stems, and kernels along with maize cell wall constituents discovered in these tissues generated 24 leaf (LIR), 42 stem (SIR), and 20 kernel (KIR) insect resistance meta-QTL (MQTL) of a diverse genetic and geographical background. Most of these MQTL involved resistance to several insect species, therefore, generating a significant interest for multiple-insect resistance breeding. Some of the LIR MQTL such as LIR4, 17, and 22 involve resistance to European corn borer, sugarcane borer, and southwestern corn borer.