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Author: XUECAI ZHANG
CIMMYT Maize Line genotypic profiles generated through genotyping-by-sequencing
XUECAI ZHANG (2016)
CIMMYT Maize Lines (CMLs) are the elite inbred lines released by CIMMYT to collaborators around the world and to the general puiblic each year. Genetic fingerprints of 538 CML lines were generated by genotyping-by-sequencing at the Genomic Diversity Facility at Cornell University along with the fingerprints of 6 elite temperate lines (Mo17, Oh43, B37, B73, B84, and C103).
Dataset
XUECAI ZHANG (2016)
Replication data for: Identification of QTL for early vigor and stay-green conferring tolerance to drought in two connected advanced backcross populations in tropical maize (Zea mays L.) We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC1F2:3 backcross populations (LPSpop and DTPpop) under well-watered (4 environments; WW) and drought stressed (6; DS) conditions to facilitate breeding efforts towards drought tolerant maize. Out of the 105 detected QTL, 53 were overdominant indicative of strong heterosis. For 14 out of 18 detected vigor QTL, as well as for eight flowering time QTL the trait increasing allele was derived from CML491. Improving drought tolerance while at the same time maintaining yield potential could be achieved by combining alleles conferring early vigor from the recurrent parent with alleles advancing flowering from the donor. The highest yielding ten entries for all population-by-irrigation treatment combination (except LPSpop WW) used in this study outyielded the best check (CML312/CML444) by 32.5% (DTPpop WW) to 60% (DTPpop DS). Moreover three entries (((CML491/DTPWC9F104)//CML491)B2/CML503; ((CML491/LPSC7F64)//CML491)B154/CML503; ((CML491/LPSC7F64)//CML491)B218/CML503) ranked within the top ten across irrigation treatments. Best performing entries identified here under drought can therefore be used as new trait donor using phenotypic and/or molecular selection.
Dataset
XUECAI ZHANG Felix San Vicente Garcia (2021)
Article
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE TRANSCRIPTION FACTORS DROUGHT TOLERANCE
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
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
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
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
Edna Mageto Jose Crossa Paulino Pérez-Rodríguez Thanda Dhliwayo natalia palacios rojas XUECAI ZHANG (2020)
The Zinc association mapping (ZAM) panel is a set of 923 elite inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) biofortification breeding program. The panel represented wide genetic diversity for kernel Zn and is comprised of several lines with tolerance/resistance to an array of abiotic and biotic stresses commonly affecting maize production in the tropics, improved nitrogen use efficiency, and grain nutritional quality. The ZAM panel_923_LINES_GENO and Zinc association mapping (ZAM) panel_phenotypic data are two files with GBS and phenotypic data for zinc (Zn) from this population. From the ZAM panel, four inbred lines (two with high-Zn and two with low-Zn) were selected and used to form the bi-parental populations, namely DH population1 and DH population2. Genotypic and phenotypic data corresponding to these populations are DH populations1&2_255_LINES_GENO and DH population1_phenotypic data and DH population2_phenotypic data
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
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