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Jarquin, D.

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Jarquin
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Jarquin, D.
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0000-0002-5098-2060

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Author: Burgueño, J. × AGROVOC: GENOMICS ×

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Now showing 1 - 3 of 3
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    Genomic prediction enhanced sparse testing for multi-environment trials
    (Genetics Society of America, 2020) Jarquin, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R.A,; Grondona, M.; Wimmer, V.; Prasanna, B.M.
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    Genomic-enabled prediction in maize using kernel models with genotype x environment interaction
    (Genetics Society of America, 2017) Bandeira e Sousa, M.; Cuevas, J.; Couto, E.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J.
    Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
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    Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones
    (Nature Publishing Group, 2016) Saint Pierre, C.; Burgueño, J.; Fuentes Dávila, G.; Figueroa, P.; Solís Moya, E.; Ireta Moreno, J.; Hernández Muela, V.M.; Zamora Villa, V.; Vikram, P.; Mathews, K.L.; Sansaloni, C.; Sehgal, D.; Jarquin, D.; Wenzl, P.; Singh, S.; Crossa, J.
    Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.
    Publication