Person: Jarquin, D.
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Jarquin
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D.
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Jarquin, D.
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0000-0002-5098-2060 9 results
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- Chapter 9. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction(Humana Press Inc., 2022) Crossa, J.; Montesinos-Lopez, O.A.; Pérez-Rodríguez, P.; Costa-Neto, G.; Fritsche-Neto, R.; Ortiz, R.; Martini, J.W.R.; Lillemo, M.; Montesinos-López, A.; Jarquin, D.; Breseghello, F.; Cuevas, J.; Rincent, R.
Publication - Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes (Nature, (2021), 599, 7886, (622-627), 10.1038/s41586-021-04066-1)(Nature Publishing Group, 2022) Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.D.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Abhishek Rathore; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquin, D.; Cubry, P.; Hickey, L.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu
Publication - A chickpea genetic variation map based on the sequencing of 3,366 genomes(Nature Publishing Group, 2021) Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.D.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Abhishek Rathore; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquin, D.; Cubry, P.; Hickey, L.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu
Publication - Genome-enabled prediction for sparse testing in multi-environmental wheat trials(CSSA, 2021) Crespo Herrera, L.A.; Howard, R.; Piepho, H.P.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Burgueño, J.; Singh, R.P.; Mondal, S.; Jarquin, D.; Crossa, J.
Publication - Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years(CIMMYT, [2020]) Dreisigacker, S.; Crossa, J.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Rosyara, U.; Juliana, P.; Mondal, S.; Crespo Herrera, L.A.; Jarquin, D.; Velu, G.; Singh, R.P.; Braun, H.J.
Publication - Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials(Frontiers, 2019) Crossa, J.; Martini, J.W.R.; Gianola, D.; Pérez-Rodríguez, P.; Jarquin, D.; Juliana, P.; Montesinos-Lopez, O.A.; Cuevas, J.
Publication - Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments(Genetics Society of America, 2019) Howard, R.; Gianola, D.; Montesinos-Lopez, O.A.; Juliana, P.; Singh, R.P.; Poland, J.; Shrestha, S.; Pérez-Rodríguez, P.; Crossa, J.; Jarquin, D.Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.
Publication - Increasing genetic gains in wheat through physiological genetics and breeding(CIMMYT, [2016]) Sukumaran, S.; Reynolds, M.P.; Crossa, J.; Lopes, M.; Jarquin, D.; Dreisigacker, S.; Molero, G.; Pinto Espinosa, F.; Piñera Chavez, F.JIn order to meet future wheat demand it is necessary to increase yield potential and develop stress adapted genotypes. To do so, research and breeding is conducted at CIMMYT through the International Wheat Yield Partnership (IWYP) platform combining physiology, genetics, and breeding. Physiological breeding focuses on understanding the physiology and genetics of key traits and conducting complementary crosses among them based on conceptual models to utilize the diversity present in the CIMMYT germplasm. Physiological breeding combined with genetic approaches (GWAS, QTLs, Genomic Selection) are used in the program to achieve genetic gains. (Reynolds and Langridge 2016 Current opinion in plant biology).
Publication - A general Bayesian estimation method of linear-bilinear models applied to plant breeding trials with genotype × environment interaction(Springer Verlag, 2012) Pérez-Elizalde, S.; Jarquin, D.; Crossa, J.Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises-Fisher distribution as a prior on the set of orthogonal matrices in a linear-bilinear model for studying and interpreting interaction in a two-way table. Simulated and empirical plant breeding data were used for illustration; the empirical data consist of a multi-environment trial established in two consecutive years. For the simulated data, vague but proper prior distributions were used, and for the real plant breeding data, observations from the first year were used to elicit a prior for parameters of the model for data of the second year trial. Bivariate Highest Posterior Density (HPD) regions for the posterior scores are shown in the biplots, and the significance of the bilinear terms was tested using the Bayes factor. Results of the plant breeding trials show the usefulness of this general Bayesian approach for breeding trials and for detecting groups of genotypes and environments that cause significant genotype × environment interaction. The present Bayes inference methodology is general and may be extended to other linear-bilinear models by fixing certain parameters equal to zero and relaxing some model constraints.
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