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Autor: Xu Zhang
Fuai Sun XUECAI ZHANG Haoqiang Yu (2022)
Artículo
BZR1s CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ARABIDOPSIS DNA BINDING PROTEINS PLANT PROTEIN TRANSCRIPTION FACTORS DROUGHT GENE EXPRESSION REGULATION GENETICS MAIZE METABOLISM TRANSGENIC PLANTS ABIOTIC STRESS
Xu Zhang Jian Hua Ravi Singh (2022)
Artículo
LysM PRR Haynaldia villosa CERK1-V CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FUNGAL DISEASES DASYPYRUM VILLOSUM WHEAT DISEASE RESISTANCE
Enhancement of plant variety protection and regulation using molecular marker technology
Yunbi Xu Jian Zhang Jiansheng LI (2022)
Artículo
Plant Variety Protection Distinctness-Uniformity-Stability Essentially Derived Variety Molecular Markers Molecular Diagnostics Genetic Similarity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS GENETIC MARKERS PLANT BREEDING VARIETIES
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
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
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