Person: Gowda, M.
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Gowda
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M.
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Gowda, M.
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0000-0003-4434-63648 results
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Now showing 1 - 8 of 8
- Genomic loci associated with grain yield under well-watered and water-stressed conditions in multiple bi-parental maize populations(Frontiers, 2024) Ndlovu, N.; Gowda, M.; Beyene, Y.; Chaikam, V.; Nzuve, F.M.; Makumbi, D.; McKeown, P.C.; Spillane, C.; Prasanna, B.M.
Publication - Silage maize as a potent candidate for sustainable animal husbandry development—perspectives and strategies for genetic enhancement(Frontiers Media S.A., 2023) Karnatam, K.S.; Mythri, B.; Wajhat Un Nisa; Sharma, H.; Kumar, T.; Rana, P.; Vikal, Y.; Gowda, M.; Dhillon, B.S.; Sandhu, S.
Publication - Editorial: Model organisms in plant science: Maize(Frontiers Media S.A., 2023) Butron, A.; Santiago, R.; Gowda, M.
Publication - Fighting death for living: Recent advances in molecular and genetic mechanisms underlying maize lethal necrosis disease resistance(MDPI, 2022) Johnmark, O.; Indieka, S.; Liu, G.; Gowda, M.; Suresh, L.M.; Zhang, W.; Gao, X.
Publication - Scalable sparse testing genomic selection strategy for early yield testing stage(Frontiers, 2021) Atanda, A.S.; Olsen, M.; Crossa, J.; Burgueño, J.; Rincent, R.; Dzidzienyo, D.; Beyene, Y.; Gowda, M.; Dreher, K.; Prasanna, B.M.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.
Publication - Application of genomic selection at the early stage of breeding pipeline in tropical maize(Frontiers, 2021) Beyene, Y.; Gowda, M.; Pérez-Rodríguez, P.; Olsen, M.; Robbins, K.; Burgueño, J.; Prasanna, B.M.; Crossa, J.
Publication - Genome‑wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm(Springer, 2015) Gowda, M.; Das, B.; Makumbi, D.; Babu, R.; Semagn, K.; Mahuku, G.; Olsen, M.; Jumbo, M.B; Beyene, Y.; Prasanna, B.M.The maize lethal necrosis disease (MLND) caused by synergistic interaction of Maize chlorotic mottle virus and Sugarcane mosaic virus, and has emerged as a serious threat to maize production in eastern Africa since 2011. Our objective was to gain insights into the genetic architecture underlying the resistance to MLND by genome-wide association study (GWAS) and genomic selection. We used two association mapping (AM) panels comprising a total of 615 diverse tropical/subtropical maize inbred lines. All the lines were evaluated against MLND under artificial inoculation. Both the panels were genotyped using genotyping-by-sequencing. Phenotypic variation for MLND resistance was significant and heritability was moderately high in both the panels. Few promising lines with high resistance to MLND were identified to be used as potential donors. GWAS revealed 24 SNPs that were significantly associated (P < 3 × 10−5) with MLND resistance. These SNPs are located within or adjacent to 20 putative candidate genes that are associated with plant disease resistance. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed higher prediction accuracy for IMAS-AM panel (0.56) over DTMA-AM (0.36) panel. The prediction accuracy for both within and across panels is promising; inclusion of MLND resistance associated SNPs into the prediction model further improved the accuracy. Overall, the study revealed that resistance to MLND is controlled by multiple loci with small to medium effects and the SNPs identified by GWAS can be used as potential candidates in MLND resistance breeding program.
Publication - Genetic architecture is more complex for resistance to Septoria tritici blotch than to Fusarium head blight in Central European winter wheat(BioMed Central, 2015) Mirdita, V.; Guozheng Liu; Yusheng Zhao; Miedaner, T.; Longin, C.F.H.; Gowda, M.; Florian Mette, M.; Reif, J.C.Fusarium head blight (FHB) and Septoria tritici blotch (STB) severely impair wheat production. With the aim to further elucidate the genetic architecture underlying FHB and STB resistance, we phenotyped 1604 European wheat hybrids and their 135 parental lines for FHB and STB disease severities and determined genotypes at 17,372 single-nucleotide polymorphic loci. Results: Cross-validated association mapping revealed the absence of large effect QTL for both traits. Genomic selection showed a three times higher prediction accuracy for FHB than STB disease severity for test sets largely unrelated to the training sets. Conclusions: Our findings suggest that the genetic architecture is less complex and, hence, can be more properly tackled to perform accurate prediction for FHB than STB disease severity. Consequently, FHB disease severity is an interesting model trait to fine-tune genomic selection models exploiting beyond relatedness also knowledge of the genetic architecture.
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