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Autor: Carolina Saint Pierre
Prashant Vikram Carolina Saint Pierre Thomas Payne Juan Burgueño Carolina Sansaloni (2017)
The Linked Topcross Population (LTP) was generated to introgress useful traits from wheat germplasm bank accessions, including synthetic hexaploids and landraces, into elite wheat varieties. In addition to generating pre-breeding materials selected for important traits such as heat and drought tolerance, this population has been used to generate data that can be useful for several applications, including genome-wide association studies.
Dataset
Prashant Vikram Carolina Saint Pierre Thomas Payne Juan Burgueño Carolina Sansaloni (2017)
The Linked Topcross Population (LTP) was generated to introgress useful traits from wheat germplasm bank accessions, including synthetic hexaploids and landraces, into elite wheat varieties. In addition to generating pre-breeding materials selected for important traits such as heat and drought tolerance, this population has been used to generate data that can be useful for several applications, including genome-wide association studies.
Dataset
Efficacy of plant breeding using genomic information
Osval Antonio Montesinos-Lopez Alison Bentley Carolina Saint Pierre Leonardo Abdiel Crespo Herrera Morten Lillemo Jose Crossa (2023)
Artículo
Genomic Selection Genomic Prediction Genomic Best Linear Unbiased Predictor CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT BREEDING GENOMICS MARKER-ASSISTED SELECTION ENVIRONMENT
Replication Data for: Optimizing sparse testing for genomic prediction of plant breeding crops
Osval Antonio Montesinos-Lopez Carolina Saint Pierre Brandon Alejandro Mosqueda González Alison Bentley Yoseph Beyene Manje Gowda Leonardo Abdiel Crespo Herrera Jose Crossa (2022)
In plant breeding, sparse testing methods have been suggested to improve the efficiency of the genomic selection methodology. The data provided in this dataset were used to evaluate four methods for allocating lines to environments for sparse testing in multi-environment trials. The analysis was conducted using a multi-trait and uni-trait framework. The accompanying article describes the results of the evaluation as well as a cost-benefit analysis to identify the benefits that can be obtained using sparse testing methods.
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
Carolina Sansaloni Jorge Franco Bruno Santos Lawrence Percival-Alwyn Cesar Petroli Jaime Campos Kate Dreher Thomas Payne David Marshall Benjamin Kilian Iain Milne Sebastian Raubach Paul Shaw Gordon Stephen Carolina Saint Pierre Juan Burgueño Jose Crossa Huihui Li Andrzej Kilian Peter Wenzl Ahmed Amri Cristobal Uauy Marianne Bänziger Mario Caccamo Kevin Pixley (2020)
A diverse panel of domesticated hexaploid and tetraploid wheat lines and their tetraploid and diploid wild relatives were genotyped using the DArtSeq technology and characterized in a global wheat diversity analysis.
Dataset