Genome-Wide Regression and Prediction with the BGLR Statistical Package

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ID: 292417
2014
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Abstract
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.
Reference Key
openalex_W1970149620 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Paulino Pérez‐Rodríguez, Gustavo de los Campos
Journal current genetics
Year 2014
DOI
10.1534/genetics.114.164442
URL
https://doi.org/10.1534/genetics.114.164442
Keywords Keywords not found

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