• español
    • English
  • English 
    • español
    • English
  • Login
baner

CIMMYT Publications Repository

Seeding innovation ... Nourishing hope

Search 
  •   DSpace Home
  • CIMMYT
  • Search
  •   DSpace Home
  • CIMMYT
  • Search
JavaScript is disabled for your browser. Some features of this site may not work without it.

Search

Show Advanced FiltersHide Advanced Filters

Filters

Use filters to refine the search results.

Now showing items 1-10 of 10

  • Sort Options:
  • Relevance
  • Title Asc
  • Title Desc
  • Issue Date Asc
  • Issue Date Desc
  • Type Asc
  • Type Desc
  • Results Per Page:
  • 5
  • 10
  • 20
  • 40
  • 60
  • 80
  • 100
Article
Thumbnail

Genomic prediction in CIMMYT maize and wheat breeding programs 

Crossa, J.; Perez, P.; Hickey, J.; Burgueño, J.; Ornella, L.; Ceron-Rojas, J.; Zhang, X.; Dreisigacker, S.; Babu, R.; Li, Y.; Bonnett, D.; Mathews, K. (Springer Nature, 2014)
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending ...
Article
Thumbnail

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)
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...
Article
Thumbnail

Genomic-enabled prediction with classification algorithms 

Ornella, L.; Pérez, P.; Tapia, E.; González-Camacho, J.M.; Burgueño, J.; Zhang, X.; Singh, S.; Vicente, F.S.; Bonnett, D.; Dreisigacker, S.; Singh, R.; Long, N.; Crossa, J. (Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
Article
Thumbnail

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction 

Bandeira e Sousa, M.; Cuevas, J.; De Oliveira Couto, E.G.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J. (Genetics Society of America, 2017)
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: ...
Article
Thumbnail

A bayesian genomic regression model with skew normal random errors 

Pérez-Rodríguez, P.; Acosta-Pech, R.; Perez-Elizalde, S.; Velasco Cruz, C.; Suarez Espinosa, J.; Crossa, J. (Genetics Society of America, 2018)
Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be ...
Article
Thumbnail

A genomic bayesian multi-trait and multi-environment model 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, J.; Crossa, J. (Genetics Society of America, 2016)
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive ...
Article
Thumbnail

Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model 

Lopez-Cruz, M.; Poland, J.; Jannink, J.L.; De los Campos, G.; Crossa, J.; Singh, R.P.; Dreisigacker, S.; Bonnett, D.; Autrique, E. (Genetics Society of America, 2015)
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...
Article
Thumbnail

Genomic selection for quantitative adult plant stem rust resistance in wheat 

Rutkoski, J.; Poland, J.E; Singh, R.P.; Huerta-Espino, J.; Bhavani, S.; Barbier, H.; Rouse, M.N.; Jannink, J.L.; Sorrells, M.E. (Crop Science Society of America, 2014)
Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the ...
Article
Thumbnail

Genomic prediction in maize breeding populations with genotyping-by sequencing 

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; Campos, G. de los; Burgueño, J.; Windhausen, V.S.; Buckler, E.S.; Jannink, J.L.; Lopez Cruz, M.A.; Babu, R. (Genetics Society of America, 2013)
Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, ...
Article
Thumbnail

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding 

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...

Collections

Genetic ResourcesInstitutionalIntegrated DevelopmentMaizeSocioeconomicsSustainable IntensificationWheat

All of DSpace
Communities & CollectionsAuthorsTitlesSubjectsBy YearConferenceJournal
This Community
AuthorsTitlesSubjectsBy YearConferenceJournal

CIMMYT staff access

Login

Discover

Author
Crossa, J. (9)Pérez-Rodríguez, Paulino (5)Burgueño, J. (4)Singh, R.P. (4)Bonnett, D. (3)Dreisigacker, S. (3)Jannink, Jean-Luc (3)Montesinos-Lopez, Osval Antonio (3)Babu, R. (2)De Los Campos, Gustavo (2)... View More
Date Issued
2019 (1)2018 (1)2017 (2)2016 (1)2015 (1)2014 (3)2013 (1)
Type
Article (10)
Agrovoc
STATISTICAL METHODS (10)
BAYESIAN THEORY (6)ARTIFICIAL SELECTION (4)DATA ANALYSIS (4)FORECASTING (3)GENOTYPE ENVIRONMENT INTERACTION (3)CROP FORECASTING (2)GENOMICS (2)MARKER-ASSISTED SELECTION (2)WHEAT (2)... View More
Keywords
Genomic Selection (10)
GenPred (7)Shared Data Resources (7)GBLUP (3)Multi-Trait Multi-Environment (2)Support Vector Machine (2)APR (1)Assymetric Distributions (1)Bayesian Estimation (1)Bayesian Genomic Enabled Prediction (1)... View More


baner
 

 


DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback