Person:
Gowda, M.

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Gowda
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Gowda, M.

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Now showing 1 - 10 of 10
  • Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation
    (Oxford University Press, 2024) Kimutai, J.J.C.; Makumbi, D.; Burgueño, J.; Perez-Rodriguez, P.; Crossa, J.; Gowda, M.; Menkir, A.; Pacheco Gil, R.A.; Ifie, B.E.; Tongoona, P.B.; Danquah, E.; Prasanna, B.M.
    Publication
  • Linkage mapping and genomic prediction of grain quality traits in tropical maize (Zea mays L.)
    (Frontiers Media S.A., 2024) Ndlovu, N.; Kachapur, R.M.; Beyene, Y.; Das, B.; Ogugo, V.; Makumbi, D.; Spillane, C.; McKeown, P.C.; Prasanna, B.M.; Gowda, M.
    Publication
  • A marker weighting approach for enhancing within-family accuracy in genomic prediction
    (Genetics Society of America, 2024) Montesinos-Lopez, O.A.; Crespo Herrera, L.A.; Xavier, A.; Gowda, M.; Beyene, Y.; Saint Pierre, C.; Rosa-Santamaria, R. de la; Salinas Ruiz, J.; Gerard, G.S.; Vitale, P.; Dreisigacker, S.; Lillemo, M.; Grignola, F.; Sarinelli, M.; Pozzo, E.; Quiroga, M.; Montesinos-Lopez, A.; Crossa, J.
    Publication
  • Optimizing sparse testing for genomic prediction of plant breeding crops
    (MDPI, 2023) Montesinos-Lopez, O.A.; Saint Pierre, C.; Gezan, S.A.; Bentley, A.R.; Mosqueda-Gonzalez, B.A.; Montesinos-López, A.; Van Eeuwijk, F.A.; Beyene, Y.; Gowda, M.; Gardner, K.A.; Gerard, G.S.; Crespo Herrera, L.A.; Crossa, J.
    Publication
  • Identification of genomic regions associated with agronomic and disease resistance traits in a large set of multiple DH populations
    (MDPI, 2022) Sadessa, K.; Beyene, Y.; Ifie, B.E.; Suresh, L.M.; Olsen, M.; Ogugo, V.; Dagne Wegary Gissa; Tongoona, P.; Danquah, E.Y.; Offei, S.K.; Prasanna, B.M.; Gowda, M.
    Publication
  • Combination of linkage mapping, GWAS, and GP to dissect the genetic basis of common rust resistance in tropical maize germplasm
    (MDPI, 2020) Kibe, M.; Nyaga, C.; Nair, S.K.; Beyene, Y.; Das, B.; Suresh, L.M.; Jumbo, M.B; Makumbi, D.; Kinyua, J.; Olsen, M.; Prasanna, B.M.; Gowda, M.
    Publication
  • Diallelic analysis of tropical maize germplasm response to spontaneous chromosomal doubling
    (MDPI, 2020) Chaikam, V.; Gowda, M.; Martinez, L.; Alvarado Beltrán, G.; Xuecai Zhang; Prasanna, B.M.
    Publication
  • Genome-wide association study to identify genomic regions influencing spontaneous fertility in maize haploids
    (Springer, 2019) Chaikam, V.; Gowda, M.; Nair, S.K.; Melchinger, A.E.; Prasanna, B.M.
    Efficient production and use of doubled haploid lines can greatly accelerate genetic gains in maize breeding programs. One of the critical steps in standard doubled haploid line production is doubling the haploid genome using toxic and costly mitosis-inhibiting chemicals to achieve fertility in haploids. Alternatively, fertility may be spontaneously restored by natural chromosomal doubling, although generally at a rate too low for practical applications in most germplasm. This is the first large-scale genome-wise association study to analyze spontaneous chromosome doubling in haploids derived from tropical maize inbred lines. Induction crosses between tropicalized haploid inducers and 400 inbred lines were made, and the resulting haploid plants were assessed for haploid male fertility which refers to pollen production and haploid fertility which refers to seed production upon self-fertilization. A small number of genotypes were highly fertile and these fertility traits were highly heritable. Agronomic traits like plant height, ear height and tassel branch number were positively correlated with fertility traits. In contrast, haploid induction rate of the source germplasm and plant aspect were not correlated to fertility traits. Several genomic regions and candidate genes were identified that may control spontaneous fertility restoration. Overall, the study revealed the presence of large variation for both haploid male fertility and haploid fertility which can be potentially exploited for improving the efficiency of doubled haploid derivation in tropical maize germplasm.
    Publication
  • Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize
    (Blackwell Verlag, 2019) Yibing Yuan; Cairns, J.E.; Babu, R.; Gowda, M.; Makumbi, D.; Magorokosho, C.; Ao Zhang; Yubo Liu; Nan Wang; Zhuanfang Hao; San Vicente Garcia, F.M.; Olsen, M.; Prasanna, B.M.; Yanli Lu; Xuecai Zhang
    Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection.
    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