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Autor: Marta Lopes
Susanne Dreisigacker Marta Lopes Miguel Sanchez-Garcia (2023)
Artículo
Winter Wheat Panel Precision Phenology Effective Markers CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC DIVERSITY (AS RESOURCE) GENOME-WIDE ASSOCIATION STUDIES PHENOLOGY PHOTOPERIODICITY POPULATION STRUCTURE VERNALIZATION WINTER WHEAT
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
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
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