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Author: Michael Olsen
Hugo De Groote Jill Cairns Michael Olsen (2021)
In sub-Saharan Africa, maize is a staple crop but yields remain sub-optimal. A novel hybrid seed technology offers the opportunity to reduce seed production costs and increase yields. This dataset contains data from on-farm and on-station trials collected in 2017 to 2019 in South Africa, Kenya and Zimbabwe to assess this hybrid seed production technology. The results of the analysis are presented in the accompanying article.
Dataset
Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)
Significant grain yield losses and poor grain quality can be caused by Common rust a major foliar disease in maize. This dataset provides the genotypic and phenotypic data that were used to perform genome-wide association studies (GWAS) and linkage analysis mapping to dissect the architecture of common rust resistance.
Dataset
Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)
Tar spot complex (TSC) is an important foliar disease for tropical maize. The data provided in this dataset were used to estimate the effectiveness of genomic selection for improving TSC resistance. The results of the analysis are reported in the accompanying journal article
Dataset
Thanda Dhliwayo Edna Mageto Michael Olsen Jose Crossa Prasanna Boddupalli XUECAI ZHANG (2020)
An association-mapping panel (DTMA) and two DH populations (DH1 and DH2) were used in the current study, which in total includes 487 materials. The dataset includes three types of files. One is the genotype of 487 lines sequenced by GbS, named DTMA_DH2_DH3-955690.hmp.txt; one is the genotype of 487 lines sequenced by rAmpSeq named genotype-rAmpSeq.csv; and the third type of files are the phenotypic data files named DH1-phenotype.csv, DH2-phenotype.csv and DTMA-phenotype.csv.
Dataset
Thanda Dhliwayo Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)
Article
Common Rust Quantitative Resistance Genome-Wide Association Study Linkage Mapping Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE RUSTS GENOMES CHROMOSOME MAPPING
Ao Zhang Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)
Article
Tar Spot Complex Genomic Prediction Genomic Selection Prediction Accuracy Genotyping by Sequencing CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE SPOTS MARKER-ASSISTED SELECTION GENOTYPING
Application of genomic selection at the early stage of breeding pipeline in tropical maize
Yoseph Beyene Manje Gowda Paulino Pérez-Rodríguez Michael Olsen Kelly Robbins Juan Burgueño Prasanna Boddupalli Jose Crossa (2021)
Article
Early-Stage Testing Genomic Selection Prediction Accuracy Tropical Maize GBLUP CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TESTING MARKER-ASSISTED SELECTION MAIZE
Beatrice Ifie Eric Danquah MacDonald Jumbo Suresh L.M. Manje Gowda Michael Olsen Prasanna Boddupalli Pangirayi Bernard Tongoona (2021)
Article
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BACKCROSSING PCR ALLELES PLANT DISEASES INTROGRESSION
Sudha Nair Biswanath Das MacDonald Jumbo Dan Makumbi Suresh L.M. Yoseph Beyene Michael Olsen Prasanna Boddupalli Manje Gowda (2020)
Gray leaf spot (GLS) is a major maize foliar disease in sub-Saharan Africa that can substantially reduce yields for farmers. This dataset contains supporting phenotypic and genotypic data used in the analysis of the genetic architecture of GLS resistance in maize. The data in this paper relate to several different sets of germplasm: (1) The IMAS (Improved Maize for Africa Soils) panel with 430 lines assembled from diverse breeding programs from Africa, Asia and Latin America. (2) A DH population developed from crossing CML550 with CML494; (3) A DH population developed from crossing CML550 with CML504; (4) A DH population developed from crossing CML550 with CML511; and (5) several F3 populations The IMAS panel is a good source for genetic studies on resistance for several maize diseases and also for abiotic stress like low N conditions. The DH populations are a good source for low N discovery studies as well as for MLN and other disease resistance studies.
Dataset
Jill Cairns Raman Babu Manje Gowda Dan Makumbi Cosmos Magorokosho Michael Olsen Prasanna Boddupalli Yanli Lu XUECAI ZHANG (2018)
Drought stress, heat stress, and combination of drought stress and heat stress have been recognized as the major abiotic constraints to maize yields in the main production regions. The phenotypic data used in the current study had been published by Jill E. Cairns et al in 2013 in the journal of Crop Science 53 :1335–1346 ( https://dx.doi.org/10.2135/cropsci2012.09.0545). In this study, the association mapping and genomic prediction 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, drought stress, heat stress, and combined drought and heat stress conditions. 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 helpful accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection or genomic selection.
Dataset