Browsing by Author "Olsen, M."
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Item Comparison of non-overlapping maize populations of unequal sizes for resistance to maize lethal necrosis(Taylor & Francis Group, 2021) Awata, L.A.O.; Ifie, B.E.; Danquah, E.; Tongoona, P.; Suresh, L.M.; Gowda, M.; Marchelo-Dragga, P.W.; Sitonik, C.; Olsen, M.; Prasanna, B.M.; Jumbo, M.B.Contrast between marker-assisted backcross (MABC) and doubled haploid (DH) methods in transferring genes for resis tance to maize lethal necrosis (MLN) in maize (Zea mays L.) is not well understood. The MLN is caused by co-infection of maize plant by maize chlorotic mottle virus and sugarcane mosaic virus. Two maize panels consisting of four BC3F2 and six DH populations, separately developed through marker assisted selection from crosses between susceptible CIMMYT lines and MLN-resistant donor parent (KS23-6), were used in the current study. The two populations were of different popu lation structures with unequal sizes. Experiments were con ducted under artificial MLN inoculations for two seasons in 2018. Analyses of variance revealed significant variations among genotypes in both panels (p ≤ 0.001). Levene’s and Welch’s tests found that variances and means of the BC3F2 and DH populations were highly unequal (p ≤ 0.001). The study identified genotypes with reduced MLN infections in both populations; however, lower means for MLN severity and area under disease progress curve (AUDPC) values, and higher her itability estimates were obtained in the DH populations than in the BC3F2 populations. Additionally, the DH populations showed higher relative genetic gains for resistance to MLN compared with the BC3F2 populations. The current study detected superiority of DH over MABC populations for breed ing for resistance to MLN. Nevertheless, the results observed in the present study warrant further investigations using the same genetic materials with identical population sizes.Item Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations(genes, 2019-12-26) Awata, L.A.O.; Beyene, Y.; Gowda, M.; Suresh, L.M.; Jumbo, M.B.; Tongoona, P.; Danquah, E.; Ifie, B.E.; Marchelo-Dragga, P.W.; Olsen, M.; Ogugo, V.; Mugo, S.; Prasanna, B.M.Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F3 progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F3 progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.Item Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program(Springer, 2021) Atanda, S.A.; Olsen, M.; Burgueño, J.; Crossa, J.; Dzidzienyo, D.; Beyene, Y.; Gowda, M.; Dreher, K.; Zhang, X.; Prasanna, B.M.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.R.The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GPItem Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage(springer, 2021) Atanda, S.A.; Olsen, M.; Crossa, J.; Burgueño, J.; Rincent, R.; Dzidzienyo, D.; Beyene, Y.; Gowda, M.; Dreher, K.; Boddupalli, P.M.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.R.To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.Item Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage(Frontiers, 2021) Atanda, S.A.; Olsen, M.; Crossa, J.; Burgueño, J.; Rincent, R.; Dzidzienyo, D.K.; Beyene, Y.; Gowda, M.; Dreher, K.; Boddupalli, P.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.R.To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes