Cargando…
Genome-Wide Regression and Prediction with the BGLR Statistical Package
Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confront...
Autores principales: | Pérez, Paulino, de los Campos, Gustavo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4196607/ https://www.ncbi.nlm.nih.gov/pubmed/25009151 http://dx.doi.org/10.1534/genetics.114.164442 |
Ejemplares similares
-
Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package
por: Pérez-Rodríguez, Paulino, et al.
Publicado: (2022) -
Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat
por: Pérez-Rodríguez, Paulino, et al.
Publicado: (2012) -
FW: An R Package for Finlay–Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments
por: Lian, Lian, et al.
Publicado: (2015) -
Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions
por: de los Campos, Gustavo, et al.
Publicado: (2015) -
Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions
por: Funkhouser, Scott A., et al.
Publicado: (2020)