Person: Semagn, K.
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Semagn
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K.
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Semagn, K.
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0000-0001-6486-5685 12 results
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- Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress(CSSA, 2015) Beyene, Y.; Semagn, K.; Mugo, S.N.; Tarekegne, A.T.; Babu, R.; Meisel, B.; Sehabiague, P.; Makumbi, D.; Magorokosho, C.; Oikeh, S.O.; Gakunga, J.; Vargas Hernández, M.; Olsen, M.; Prasanna, B.M.; Banziger, M.; Crossa, J.
Publication - Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations(Springer Verlag, 2018) Gowda, M.; Beyene, Y.; Makumbi, D.; Semagn, K.; Olsen, M.; Jumbo, M.B; Das, B.; Mugo, S.N.; Suresh, L.M.; Prasanna, B.M.In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.
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 - Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations(Frontiers, 2017) Ao Zhang; Hongwu Wang; Beyene, Y.; Semagn, K.; Yubo Liu; Shiliang Cao; Zhenhai Cui; Yanye Ruan; Burgueño, J.; San Vicente Garcia, F.M.; Olsen, M.; Prasanna, B.M.; Crossa, J.; Haiqiu Yu; Xuecai ZhangGenomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.
Publication - The development of drought tolerant maize germplasm in sub-Saharan Africa using marker-assisted recurrent selection and genomic selection(CIMMYT, 2016) Beyene, Y.; Semagn, K.; Mugo, S.N.; Meisel, B.; Oikeh, S.O.; Tarekegne, A.T.; Olsen, M.; Prasanna, B.M.; Crossa, J.
Publication - Genome-wide association for plant height and flowering time across 15 tropical maize populations under managed drought stress and well-watered conditions in Sub-Saharan Africa(Crop Science Society of America (CSSA), 2016) Wallace, J.; Xuecai Zhang; Beyene, Y.; Semagn, K.; Olsen, M.; Prasanna, B.M.; Buckler, E.Genotyping breeding materials is now relatively inexpensive but phenotyping costs have remained the same. One method to increase gene mapping power is to use genome-wide genetic markers to combine existing phenotype data for multiple populations into a unified analysis. We combined data from 15 biparental populations of maize (Zea mays L.) (>2500 individual lines) developed under the Water-Efficient Maize for Africa project to perform genome-wide association analysis. Each population was phenotyped in multilocation trials under water-stressed and well-watered environments and genotyped via genotyping-by-sequencing. We focused on flowering time and plant height and identified clear associations between known genomic regions and the traits of interest. Out of ~380,000 single- nucleotide polymorphisms (SNPs), we found 115 and 108 that were robustly associated with flowering time under well-watered and drought stress conditions, respectively, and 143 and 120 SNPs, respectively, associated with plant height. These SNPs explained 36 to 80% of the genetic variance, with higher accuracy under wellwatered conditions. The same set of SNPs had phenotypic prediction accuracies equivalent to genome-wide SNPs and were significantly better than an equivalent number of random SNPs, indicating that they captured most of the genetic variation for these phenotypes. These methods could potentially aid breeding efforts for maize in Sub-Saharan Africa and elsewhere. The methods will also help in mapping drought tolerance and related traits in this germplasm. We expect that analyses combining data across multiple populations will become more common and we call for the development of algorithms and software to enable routine analyses of this nature.
Publication - Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs(Springer Nature, 2015) Xuecai Zhang; Pérez-Rodríguez, P.; Semagn, K.; Beyene, Y.; Babu, R.; Lopez-Cruz, M.; San Vicente Garcia, F.M.; Olsen, M.; Buckler, E.; Jannink, J.L.; Prasanna, B.M.; Crossa, J.One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.
Publication - Performance and grain yield stability of maize populations developed using marker-assisted recurrent selection and pedigree selection procedures(Springer, 2016) Beyene, Y.; Semagn, K.; Mugo, S.N.; Prasanna, B.M.; Tarekegne, A.T.; Gakunga, J.; Sehabiague, P.; Meisel, B.; Oikeh, S.O.; Olsen, M.; Crossa, J.A marker-assisted recurrent selection (MARS) program was undertaken in sub-Saharan Africa to improve grain yield under drought-stress in 10 biparental tropical maize populations. The objectives of the present study were to evaluate the performance of C1S2-derived hybrids obtained after three MARS cycles (one cycle of recombination (C1), followed by two generations of selfing (S2), and to study yield stability under both drought-stress (DS) and well-watered (WW) conditions. For each of the 10 populations, we evaluated hybrids developed by crossing 47–74 C1S2 lines advanced through MARS, the best five S5 lines developed through pedigree selection, and the founder parents with a single-cross tester from a complementary heterotic group. The hybrids and five commercial checks were evaluated in Kenya under 1–3 DS and 3–5 WW conditions with two replications. Combined across DS locations, the top 10 C1S2-derived hybrids from each of the 10 biparental populations produced 0.5–46.3 and 11.1–55.1 % higher mean grain yields than hybrids developed using pedigree selection and the commercial checks, respectively. Across WW locations, the best 10 hybrids derived from C1S2 of each population produced 3.4–13.3 and 7.9–36.5 % higher grain yields than hybrids derived using conventional pedigree breeding and the commercial checks, respectively. Mean days to anthesis of the best 10 C1S2 hybrids were comparable to those of hybrids developed using the pedigree method, the founder parents and the commercial checks, with a maximum difference of 3.5 days among the different groups. However, plant height was significantly (P < 0.01) different in most pairwise comparisons. Our results showed the superiority of MARS over pedigree selection for improving diverse tropical maize populations as sources of improved lines for stress-prone environments and thus MARS can be effectively integrated into mainstream maize breeding programs.
Publication - Improving maize grain yield under drought stress and non-stress environments in Sub-Saharan Africa using marker-assisted recurrent selection(Crop Science Society of America (CSSA), 2016) Beyene, Y.; Semagn, K.; Crossa, J.; Mugo, S.N.; Atlin, G.; Tarekegne, A.T.; Meisel, B.; Sehabiague, P.; Vivek, B.; Oikeh, S.O.; Alvarado Beltrán, G.; Machida, L.; Olsen, M.; Prasanna, B.M.; Banziger, M.In marker-assisted recurrent selection (MARS), a subset of molecular markers significantly associated with target traits of interest are used to predict the breeding value of individual plants, followed by rapid recombination and selfing. This study estimated genetic gains in grain yield (GY) using MARS in 10 biparental tropical maize (Zea may L.) populations. In each population, 148 to 184 F2:3 (defined as C0) progenies were derived, crossed with a single-cross tester, and evaluated under water-stressed (WS) and well-watered (WW) environments in sub- Saharan Africa (SSA). The C0 populations were genotyped with 190 to 225 single-nucleotide polymorphism (SNP) markers. A selection index based on marker data and phenotypic data was used for selecting the best C0 families for recombination. Individual plants from selected families were genotyped using 55 to 87 SNPs tagging specific quantitative trait loci (QTL), and the best individuals from each cycle were either intercrossed (to form C1) or selfed (to form C1S1 and C1S2). A genetic gain study was conducted using test crosses of lines from the different cycles F1 and founder parents. Test crosses, along with five commercial hybrid checks were evaluated under four WS and four WW environments. The overall gain for GY using MARS across the 10 populations was 105 kg ha−1 yr−1 under WW and 51 kg ha−1 yr−1 under WS. Across WW environments, GY of C1S2–derived hybrids were 8.7, 5.9, and 16.2% significantly greater than those of C0, founder parents, and commercial checks, respectively. Results demonstrate the potential of MARS for increasing genetic gain under both drought and optimum environments in SSA.
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.; Bright, J.M.; 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.
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