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Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials

Cuevas, J.; Granato, I.; Fritsche-Neto, R.; Montesinos-Lopez, O.A.; Burgueño, J.; Bandeira e Sousa, M.; Crossa, J. (Genetics Society of America, 2018)

Bayesian regularized quantile regression: a robust alternative for genome-based prediction of skewed data

Perez-Rodriguez, P.; Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J. (Elsevier, 2020)

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Universidade Estadual Paulista (Unesp); Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); University of Wisconsin-Madison; Universidade Federal de Goiás (UFG); National Association of Breeders and Researchers (2021-01-01)

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. ...

Genome-enabled prediction models for yield related traits in chickpea

Roorkiwal, M.; Rathore, A.; Das, R.R.; Muneendra K. Singh; Ankit Jain; Samineni Srinivasan; Pooran M. Gaur; Chellapilla Bharadwaj; Shailesh Tripathi; Yongle Li; Hickey, J.M.; Lorenz, A.J.; Sutton, T.; Crossa, J.; Jannink, J.L.; Varshney, R.K. (Frontiers, 2021)

Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling

Ortiz, R.; Crossa, J.; Reslow, F.; Perez-Rodriguez, P.; Cuevas, J. (Frontiers, 2022)

A new deep learning calibration method enhances genome-based prediction of continuous crop traits

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Mosqueda-Gonzalez, B.A.; Bentley, A.R.; Lillemo, M.; Varshney, R.K.; Crossa, J. (Frontiers, 2022)

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Montesinos-López, J.C.; Singh, P.K.; Juliana, P.; Salinas Ruiz. J. (Genetics Society of America, 2017)

Comparison of models and whole-genome profiling approaches for genomic-enabled prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and tan spot resistance in wheat

Juliana, P.; Singh, R.P.; Singh, P.K.; Crossa, J.; Rutkoski, J.; Poland, J; Bergstrom, G.C.; Sorrells, M.E. (Crop Science Society of America, 2018)

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Montesinos-Lopez, A.; Runcie, D.E.; Ibba, M.I.; Perez-Rodriguez, P.; Montesinos-Lopez, O.A.; Crespo Herrera, L.A.; Bentley, A.R.; Crossa, J. (Genetics Society of America, 2021)

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies

Biscarini, Filippo; Nazzicari, Nelson; Bink, Marco; Arús, Pere; Aranzana, Maria José; Verde, Ignazio; Micali, Sabrina; Pascal, Thierry; Quilot-Turion, Benedicte; Lambert, Patrick; Da Silva Linge, Cassia; Pacheco, Igor; Bassi, Daniele; Stella, Alessandra; Rossini, Laura (BioMed Central Ltd., 2017)

© 2017 The Author(s). Background: Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and ...

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