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Saint Pierre, C.

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Saint Pierre
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Saint Pierre, C.
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0000-0003-1291-7468

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AGROVOC: FORECASTING × Type: Article ×

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Now showing 1 - 4 of 4
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    Deep learning methods improve genomic prediction of wheat breeding
    (Frontiers Media S.A., 2024) Montesinos-Lopez, A.; Crespo Herrera, L.A.; Dreisigacker, S.; Gerard, G.S.; Vitale, P.; Saint Pierre, C.; Velu, G.; Tarekegn, Z.T.; Chavira-Flores, M.; Pérez-Rodríguez, P.; Ramos-Pulido, S.; Lillemo, M.; Huihui Li; Montesinos-Lopez, O.A.; Crossa, J.
    Publication
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    Integrating parental phenotypic data enhances prediction accuracy of hybrids in wheat traits
    (MDPI, 2023) Montesinos-Lopez, O.A.; Bentley, A.R.; Saint Pierre, C.; Crespo Herrera, L.A.; Salinas Ruiz, J.; Valladares-Celis, P.C.; Montesinos-López, A.; Crossa, J.
    Publication
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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.
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    Genomic prediction of gene bank wheat landraces
    (Genetics Society of America, 2016) Crossa, J.; Jarquin, D.; Franco, J.; Pérez-Rodríguez, P.; Burgueño, J.; Saint Pierre, C.; Vikram, P.; Sansaloni, C.; Petroli, C.; Akdemir, D.; Sneller, C.; Reynolds, M.P.; Tattaris, M.; Payne, T.S.; Guzman, C.; Peña, Roberto; Wenzl, P.; Singh, S.
    This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH) and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G×E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections were formed. Accounting for population structure decreased prediction accuracy by 15%-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections but slightly lower for Iranian collections. Prediction accuracy when incorporating G×E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G×E term. For Iranian landraces, accuracies were 0.55 for the G×E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.
    Publication