NDLI: Genome-Wide Regression and Prediction with the BGLR Statistical Package

Content Provider	PubMed Central
Author	Pérez, Paulino Campos, Gustavo De Los
Copyright Year	2014
Abstract	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 confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.
Related Links	http://dx.doi.org/10.1534/genetics.114.164442
Ending Page	495
Page Count	13
Starting Page	483
File Format	PDF
ISSN	19432631
e-ISSN	19432631
Journal	Genetics
Issue Number	2
Volume Number	198
Language	English
Publisher	Genetics Society of America
Publisher Date	2014-10-01
Access Restriction	Open
Rights Holder	Genetics Society of America
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics

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