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Autor: Matthew Paul Reynolds
Sivakumar Sukumaran Susanne Dreisigacker Marta Lopes Perla Noemi Chavez Dulanto Matthew Paul Reynolds (2017)
Genome-wide association study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009–2010 to 2012–2013). Raw data for grain yield, yield components and physiological traits are provided.
Dataset
Sivakumar Sukumaran Susanne Dreisigacker Marta Lopes Perla Noemi Chavez Dulanto Matthew Paul Reynolds (2017)
Genome-wide association study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009–2010 to 2012–2013). Raw data for grain yield, yield components and physiological traits are provided.
Dataset
Sivakumar Sukumaran Jose Crossa Carlos Jara Marta Lopes Matthew Paul Reynolds (2016)
Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.
Dataset
A 'wiring diagram' for source strength traits impacting wheat yield potential
Erik Murchie Matthew Paul Reynolds Gustavo Slafer John Foulkes Liana Acevedo-Siaca Lorna Mcausland Simon Griffiths A Elizabete Carmo-Silva (2023)
Artículo
Source-Sink Yield Physiology CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOMASS BREEDING PHOTOSYNTHESIS SOURCE SINK RELATIONS YIELDS PHYSIOLOGY
Sivakumar Sukumaran Jose Crossa Carlos Jara Marta Lopes Matthew Paul Reynolds (2016)
Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.
Dataset
Genetic and phenotypic data of Syn/Weebil recombinant inbred lines under drought and heat stresses
Caiyun Liu Sivakumar Sukumaran Carolina Sansaloni Susanne Dreisigacker Matthew Paul Reynolds (2019)
We studied a RIL population of 276 entries derived from a cross between SYN-D × Weebill 1. SYN-D (Croc 1/Aegilops Squarrosa (224)//Opata) is a synthetic derived hexaploid wheat with dark green broad leaves without wax. The RILs did not segregate for Rht-B1, Rht-D1, Ppd-A1, Ppd-D1, Vrn-A1, Vrn-A1, Vrn-D1, and Eps-D1 genes and showed a narrow range of phenology, which avoids the confounding effect of phenology to identify QTL that may otherwise be masked by crop development. The RILs population was phenotyped in a randomized lattice design with two replications under four environments -drought (2009-2010, D10), heat (2009-2010, H10), heat + drought (2011-2012 and 2012-2013, HD12 and HD13)- at the Campo Experimental Norman E. Borlaug (CENEB), CIMMYT’s experimental station at Ciudad Obregón, Sonora, Northwest Mexico (27.20°N, 109.54°W, 38 masl). Drought stress (D) was applied by normal planting (late November) with significantly reduced irrigation (total water supply < 200 mm); heat stress (H) was applied by late sowing (late February) with supplementary irrigation (total water supply > 700 mm) to avoid the effect of drought; the combined stress (H+D) was applied by delayed planting date (late February) with reduced irrigation (total water supply < 200 mm).
Dataset
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
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
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
Fatih Özdemir Mesut KESER Abdelfattah DABABAT Rajiv Sharma Najibeh Ataei SABER GOLKARI Mozaffar Roostaei BEYHAN AKIN Matthew Paul Reynolds ENES YAKI?IR Murat Küçükçongar Mustafa Önder (2019)
The study was conducted as part of International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) Project titled: “Improving food security by enhancing wheat production and its resilience to climate change through maintaining the diversity of currently grown landraces”. The project was successfully conducted in Afghanistan, Iran and Turkey in 2015-2019 and had the following objectives: 1. Participatory selection of drought and heat tolerant wheat landraces among the set of the germplasm recently collected from the farming communities in the target countries using modern phenotyping and genotyping tools in collaboration with farming communities, research institutions, NGOs and extension services. 2. Development of germplasm combining drought and heat tolerance with disease resistance (primarily yellow rust and common as well as leaf and stem rust) through crosses, marker assisted selection and backcrossing to the landraces. 3. Promotion of selected drought and heat tolerant landraces in the targeted regions through enhanced on-farm seed production and bulk selection, improved agronomic practices and large scale awareness campaign. 4. Training of farmers, extension services and local administration, policy-makers, NGOs and researchers on sustainable cultivation of wheat landraces and role of biodiversity in mitigation of adverse effects of climate change. Important part of the project activities was characterization of wheat 85 wheat landraces currently collected from Afghanistan, Iran and Turkey along with modern winter wheat germplasm adapted to irrigated and rainfed conditions and checks making the total 158 entries. The sets was thoroughly phenotyped for agronomic and physiological traits in Turkey (Konya, Ankara and Sakarya provinces) in 2018 and 2019, in Afghanistan (Kabul) in 2019 and in Iran (Maragheh) in 2019. The ITPGR requirement to the project was to make the data freely available through the Multilateral System. The phenotyping of the trial was supported by ITPGRFA Project No: W2B-PR-41-Turkey with funding from the European Union. CIMMYT-Turkey is supported by Ministry of Agriculture and Forestry of the Turkish Republic and CRP WHEAT. The file contained in this study provides both phenotypic and genotypic data for the landraces.
Dataset