Búsqueda
Autor: DIEGO JARQUIN
Sivakumar Sukumaran Jose Crossa DIEGO JARQUIN Matthew Paul Reynolds (2016)
This study contains spring wheat yield data (1st, 2nd, and 3rd WYCYTs and 1st, 2nd, 3rd and 4th SATYNs) from 136 international environments that were used to evaluate the predictive ability of different models in diverse environments by modeling G×E using the pedigree-derived additive relationship matrix (A matrix).
Dataset
Sivakumar Sukumaran Jose Crossa DIEGO JARQUIN Matthew Paul Reynolds (2016)
This study contains spring wheat yield data (1st, 2nd, and 3rd WYCYTs and 1st, 2nd, 3rd and 4th SATYNs) from 136 international environments that were used to evaluate the predictive ability of different models in diverse environments by modeling G×E using the pedigree-derived additive relationship matrix (A matrix).
Dataset
Leonardo Abdiel Crespo Herrera Ravi Singh Suchismita Mondal Philomin Juliana DIEGO JARQUIN Jose Crossa (2021)
Sparse testing can be used in plant breeding and genome-based prediction. In sparse testing not all of the lines are sown in all environments. The phenotypic and genotypic data files provided in this dataset were used to execute an analysis of three general cases of the composition of the sparse testing allocation design for wheat breeding.
Dataset
Genomic-enabled prediction in maize using kernel models with genotype × environment interaction
Massaine e Sousa Jaime Cuevas Evellyn Couto Paulino Pérez-Rodríguez DIEGO JARQUIN Roberto Fritsche-Neto Juan Burgueño Jose Crossa (2017)
^^
Dataset
Osval Antonio Montesinos-Lopez Philomin Juliana Ravi Singh Jesse Poland Paulino Pérez-Rodríguez Jose Crossa DIEGO JARQUIN (2019)
In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information in two different validation schemes. All models included main effects, and others also considered interactions between the different types of covariates via Hadamard products of similarity structures. The pedigree models always gave better results predicting new lines in observed environments than the genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, markers and environments were included. When new lines were predicted in unobserved environments in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design of future breeding programs.
Dataset
Genomic-enabled prediction in maize using kernel models with genotype × environment interaction
Massaine e Sousa Jaime Cuevas Evellyn Couto Paulino Pérez-Rodríguez DIEGO JARQUIN Roberto Fritsche-Neto Juan Burgueño Jose Crossa (2017)
^^
Dataset
Replication Data for: Genomic Prediction of Gene Bank Wheat Landraces
Jose Crossa DIEGO JARQUIN Jorge Franco Paulino Pérez-Rodríguez Juan Burgueño Carolina Saint Pierre Prashant Vikram Carolina Sansaloni Cesar Petroli Deniz Akdemir Clay Sneller Matthew Paul Reynolds Thomas Payne Carlos Guzman Roberto Peña Peter Wenzl Sukhwinder Singh (2023)
Genomic prediction methods may be used to enhance efforts to rapidly introgress traits of interest from exotic germplasm into elite materials. This study examined the performance of different genomic prediction models using genotypic and phenotypic data related to 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in germplasm banks. The Mexican and Iranian collections were evaluated under optimal, drought, and heat conditions for several traits including the highly heritable traits, days to heading (DTH), and days to maturity (DTM). The results of the different analyses are reported in the accompanying journal article.
Dataset