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Autor: Carolina Rivera-Amado
Associations between endogenous spike cytokinins and grain-number traits in spring wheat genotypes
Gemma Molero Carolina Rivera-Amado Matthew Paul Reynolds John Foulkes (2024)
Artículo
Spike Cytokinins Grain Number Fruiting Efficiency Wheat Breeding CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SPIKES CYTOKININS GRAIN FRUITING HARVEST INDEX WHEAT PLANT BREEDING
Phenotypic data of HIBAP I panel under yield potential and heat stress conditions
Gemma Molero Benedict Coombes Ryan Joynson Francisco Pinto Francisco Javier Pinera-Chavez Carolina Rivera-Amado Anthony Hall Matthew Paul Reynolds (2022)
Phenotypic data of HIBAP I panel evaluated under yield potential and heat stress conditions during Obregon wheat seasons 2015-16 and 2016-17. Combined data across years per environment. The HIBAP I panel is comprised of 149 high biomass spring wheat lines of a variety of elite and exotic backgrounds. It was demonstrated how strategic integration of exotic material significantly increases yield under heat stress compared to elite lines, with no significant yield penalty under favourable conditions. Through genome wide association analysis three marker trait associations were revealed. The yield increase was associated with lower canopy temperature. An Aegilops tauschii introgression was identified as the most significant of these associations. Publicly available sequencing data used in this study is available at the European Nucleotide Archive (ENA). More information about the location of sequencing data can be found in the section 'Data availability' of the referenced manuscript at https://doi.org/10.1101/2022.02.09.479695.
Dataset
Replication Data for: Exotic alleles contribute to heat tolerance in wheat under field conditions
Gemma Molero Benedict Coombes Ryan Joynson Francisco Pinto Francisco Javier Pinera-Chavez Carolina Rivera-Amado Anthony Hall Matthew Paul Reynolds (2023)
Plant breediers must respond to the threats posed by climate change in order to help ensure global food security in the short and long term. A better understanding of the genetic underpinnings of heat tolerance can contribute to efforts to develop more resilient crops. The exome capture data from 149 spring wheat lines provided in this dataset were used to search for loci, including loci from an exotic wheat relative, that could contribute to enhanced heat tolerance. The HIBAP_Germplasm file provides a HIBAP_NUMBER that appears in name of the exome capture files to link them to specific germplasm.The methods and results of the study are described in the accompanying article.
Dataset
Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)
In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.
Dataset
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)
Artículo
Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION