Person:
Saint Pierre, C.

Loading...
Profile Picture
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Saint Pierre
First Name
C.
Name
Saint Pierre, C.

Search Results

Now showing 1 - 3 of 3
  • 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
  • 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
  • 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