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Autor: Leonardo Abdiel Crespo Herrera
Daniel Runcie Maria Itria Ibba Osval Antonio Montesinos-Lopez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2021)
Several different genome-based prediction models are available for the analysis of multi-trait data in genomic selection. The supplemental files included in this dataset provide six extensive multi-trait wheat datasets (quality and grain yield) that enable the comparison of performance of genomic-enabled-prediction when calculating the prediction accuracy using different methods. The related article describes the results of the analysis and reports that trait grain yield prediction performance is better under a multi-trait model as compared with the single-trait model.
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
Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023)
Artículo
New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY
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
Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program
Ravi Singh Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR Muhammad Imtiaz CAIXIA LAN sridhar bhavani JULIO HUERTA_ESPINO Xinyao He Mark Lucas Jesse Poland (2016)
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
Dataset
Jesse Poland Susanne Dreisigacker Sandesh Kumar Shrestha Ravi Singh Suchismita Mondal Philomin Juliana Jose Crossa BHOJA BASNET Leonardo Abdiel Crespo Herrera Trevor Rife Govindan Velu (2016)
Genetic profiling of wheat breeding lines from the CIMMYT bread wheat breeding program was carried out over several years.
Dataset
deepmala sehgal Suchismita Mondal Leonardo Abdiel Crespo Herrera Govindan Velu Philomin Juliana JULIO HUERTA_ESPINO Sandesh Kumar Shrestha Jesse Poland Ravi Singh Susanne Dreisigacker (2020)
Genetic architecture of grain yield (GY) has been extensively investigated in wheat using genome wide association study (GWAS) approach. However, most studies have used small panel sizes in combination with large genotypic data, typical examples of the so-called ‘large p small n’ or ‘short-fat data’ problem. Further, use of bi-allelic SNPs accentuated ‘missing heritability’ issues and therefore reported markers had limited impact in wheat breeding. We performed haplotype-based GWAS using 519 haplotype blocks on seven large cohorts of advanced CIMMYT spring bread wheat lines comprising overall 6,333 genotypes. In addition, epistatic interactions among the genome-wide haplotypes were investigated, an important aspect which has not yet been fully explored in wheat GWAS in order to address the missing heritability. Our results unveiled the intricate genetic architecture of GY controlled by both main and epistatic effects. The importance of these results from practical applications in the CIMMYT breeding program is discussed.
Dataset
Philomin Juliana Ravi Singh Jesse Poland Sandesh Kumar Shrestha JULIO HUERTA_ESPINO Govindan Velu Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR arun joshi Thomas Payne Pradeep Kumar Bhati Vipin Tomar (2021)
A large-scale genome-wide association study was carried out to dissect the genetic architecture of wheat grain yield potential and stress-resilience. Based on the findings, grain yield-associated marker profiles were generated for a large panel of 73,142 wheat lines and the grain-yield favorable allele frequencies were also determined. The marker profile data are presented in this dataset.
Dataset
Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program
Ravi Singh Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR Muhammad Imtiaz CAIXIA LAN Mandeep Randhawa sridhar bhavani Pawan Singh JULIO HUERTA_ESPINO Xinyao He Francisco Pinto Lorena González Pérez Philomin Juliana Daljit Singh Mark Lucas Jesse Poland (2016)
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
Dataset