Journal article
plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure
Briefings in bioinformatics, Vol.27(1), bbaf672
01/01/2026
DOI: 10.1093/bib/bbaf672
PMID: 41619213
Abstract
Correlation among the observations in high-dimensional regression modeling can be a major source of confounding. We present a new open-source package, plmmr, to implement penalized linear mixed models in R. This R package estimates correlation among observations in high-dimensional data and uses those estimates to improve prediction with the best linear unbiased predictor. The package uses memory mapping so that genome-scale data can be analyzed on ordinary machines even if the size of data exceeds random-access memory. We present here the methods, workflow, and file-backing approach upon which plmmr is built, and we demonstrate its computational capabilities with two examples from real genome-wide association studies data.
Details
- Title: Subtitle
- plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure
- Creators
- Tabitha K Peter - University of IowaAnna C Reisetter - University of IowaYujing Lu - University of IowaOscar A Rysavy - University of IowaPatrick J Breheny - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Briefings in bioinformatics, Vol.27(1), bbaf672
- DOI
- 10.1093/bib/bbaf672
- PMID
- 41619213
- NLM abbreviation
- Brief Bioinform
- ISSN
- 1467-5463
- eISSN
- 1477-4054
- Publisher
- Oxford Publishing Limited (England)
- Language
- English
- Date published
- 01/01/2026
- Academic Unit
- Biostatistics
- Record Identifier
- 9985132083002771
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