plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure

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.

• Publication year

  2025

• Venue

  Briefings in Bioinformatics

• Publication date

  2025-02-03

• Fields of study

  Biology, Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1093/bib/bbaf672arXiv 2502.01577PMID 41619213PMCID 12860386
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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• ©2023 by the Authors. This Article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/

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