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 13 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 - Molecular diversity and selective sweeps in maize inbred lines adapted to African highlands(Nature Publishing Group, 2019) Dagne Wegary Gissa; Chere, A.T.; Prasanna, B.M.; Tadesse, B.; Alachiotis, N.; Negera, D.; Awas, G.; Abakemal, D.; Ogugo, V.; Gowda, M.; Semagn, K.Little is known on maize germplasm adapted to the African highland agro-ecologies. In this study, we analyzed high-density genotyping by sequencing (GBS) data of 298 African highland adapted maize inbred lines to (i) assess the extent of genetic purity, genetic relatedness, and population structure, and (ii) identify genomic regions that have undergone selection (selective sweeps) in response to adaptation to highland environments. Nearly 91% of the pairs of inbred lines differed by 30–36% of the scored alleles, but only 32% of the pairs of the inbred lines had relative kinship coefficient <0.050, which suggests the presence of substantial redundancy in allelic composition that may be due to repeated use of fewer genetic backgrounds (source germplasm) during line development. Results from different genetic relatedness and population structure analyses revealed three different groups, which generally agrees with pedigree information and breeding history, but less so by heterotic groups and endosperm modification. We identified 944 single nucleotide polymorphic (SNP) markers that fell within 22 selective sweeps that harbored 265 protein-coding candidate genes of which some of the candidate genes had known functions. Details of the candidate genes with known functions and differences in nucleotide diversity among groups predicted based on multivariate methods have been discussed.
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 - Genetic diversity among selected elite CIMMYT maize hybrids in East and Southern Africa(Crop Science Society of America, 2017) Masuka, B.; Biljon, A.; Cairns, J.E.; Das, B.; Labuschagne, M.; MacRobert, J.; Makumbi, D.; Magorokosho, C.; Zaman-Allah, M.; Ogugo, V.; Olsen, M.; Prasanna, B.M.; Tarekegne, A.T.; Semagn, K.Genetic gain within the CIMMYT Eastern and Southern Africa (ESA) hybrid maize (Zea mays L.) breeding program from 2000 to 2010 was recently estimated at 0.85 to 2.2% yr−1 under various environmental conditions. Over 100 varieties were disseminated from CIMMYT to farmers in ESA, hence the need to check genetic diversity and frequency of use of parents to avoid potential narrowing down of the genetic base. Fifty-five parents from CIMMYT ESA used in the hybrids were fingerprinted using genotyping-by-sequencing. Data analysis in TASSEL and MEGA6 generated pairwise genetic distances between parents of 0.004 to 0.4005. Unweighted pair group method with arithmetic mean (UPGMA) analysis produced two clusters (I and II) with two subclusters each (A and B) and two sub-subclusters (IAi and IAii). Principal coordinate analysis produced three clusters where IAi and IIA from the UPGMA analysis formed independent clusters while IAii, IB, and IIB clustered together. Lines were separated by pedigree and origin. Ninety-five percent frequency of pairwise genetic distances ranged between 0.2001 and 0.4000. However, only four of the 55 parents (CML444, CML395, CML312, and CML442) were each used in 15 to 30 of the 52 hybrids evaluated in the genetic gain study. The remaining 51 were used in one to four hybrids. Frequent use of the four parents gave 29 to 58% of the hybrids a narrow genetic base, posing risk in case of pest or disease outbreaks. Parents evaluated do not represent the genetic base of CIMMYT ESA but parents of the best-performing hybrids selected from 2000 to 2010. Breeders should ensure a wide genetic base for released varieties to avoid breakdown in case of pest or disease outbreaks.
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 - Molecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs(Springer, 2016) Yongsheng Wu; Kaijian Huang; Costich, D.E.; Semagn, K.; Babu, R.; San Vicente Garcia, F.M.; Prasanna, B.M.; Nair, S.K.; Olsen, M.; Dhliwayo, T.; Xuecai ZhangCIMMYT maize inbred lines (CMLs) have been widely used all over the world and have contributed greatly to both tropical and temperate maize improvement. Genetic diversity and population structure of the current CML collection and of six temperate inbred lines were assessed and relationships among all lines were determined with genotyping-by-sequencing SNPs. Results indicated that: (1) wider genetic distance and low kinship coefficients among most pairs of lines reflected the uniqueness of most lines in the current CML collection; (2) the population structure and genetic divergence between the Temperate subgroup and Tropical subgroups were clear; three major environmental adaptation groups (Lowland Tropical, Subtropical/Mid-altitude and Highland Tropical subgroups) were clearly present in the current CML collection; (3) the genetic diversity of the three Tropical subgroups was similar and greater than that of the Temperate subgroup; the average genetic distance between the Temperate and Tropical subgroups was greater than among Tropical subgroups; and (4) heterotic patterns in each environmental adaptation group estimated using GBS SNPs were only partially consistent with patterns estimated based on combining ability tests and pedigree information. Combining current heterotic information based on combining ability tests and the genetic relationships inferred from molecular marker analyses may be the best strategy to define heterotic groups for future tropical maize improvement. Information resulting from this research will help breeders to better understand how to utilize all the CMLs to select parental lines, replace testers, assign heterotic groups and create a core set of breeding germplasm.
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.
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