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Autor: Sivakumar Sukumaran
Sivakumar Sukumaran Matthew Paul Reynolds Carolina Sansaloni (2017)
GWAS identifies common loci under stressed and non-stressed environments.
Dataset
Sivakumar Sukumaran Matthew Paul Reynolds Carolina Sansaloni (2017)
GWAS identifies common loci under stressed and non-stressed environments.
Dataset
Genetic data and the linkage map of Seri/Babax population
Caiyun Liu Sivakumar Sukumaran Matthew Paul Reynolds (2019)
These datasets include an updated linkage map for the Seri/Babax RIL population and the raw genotype data we used to construct the map. (1) We updated the genetic map of Seri/Babax population with 1748 non-redundant markers (1165 90K SNPs, 207 DArTseq SNPs, 183 AFLP, 111 DArT array, and 82 SSR). The updated map was 5576.5 cM in length with 31 linkage groups covering the 21 wheat chromosomes. The updated map has a better genome coverage, especially in D genome, and higher density than in earlier maps; and also shows a good collinearity with the IWGSC RefSeq v1.0 genome. The new linkage map can provide a useful genomic resource for further genetic analyses of important traits in the Seri/Babax population. (2) We also uploaded the raw genotyping data of 6470 markers (5386 90K SNPs, 609 DArTseq SNPs, 211 AFLP, 120 SSR, 144 DArT) we used to construct the map. Partners can download the raw data and reconstruct the map by adding new genotyping data, or through different methods and software for better improvement of the Seri/Babax genetic map.
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
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 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 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
Johannes Martini Fernando Henrique Toledo Carolina Sansaloni Jose Crossa Jaime Cuevas Sivakumar Sukumaran (2020)
The genetic diversity housed in germplasm banks may provide valuable contributions to breeding efforts. It is important to understand the best way to introduce this diversity into elite breeding materials. This files in this dataset provide phenotypic and genotypic data used to compare genomic prediction approaches and different cross-validation scenarios on a set of wheat families obtained from crosses between elite materials and diverse germplasm bank accessions. The linked top cross population (LTP) materials analyzed in the study were screened under yield potential, drought, and heat stress conditions.
Dataset