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

TL;DR

The paper introduces plmmr, an R package for fitting penalized linear mixed models to high-dimensional genome-wide association data, effectively estimating complex correlations and enabling analysis of large datasets on standard computers.

Contribution

It provides a novel open-source R package that estimates correlations in high-dimensional data and improves prediction in GWAS using memory-mapping for large datasets.

Findings

Successfully analyzed genome-scale data exceeding RAM capacity

Improved prediction accuracy in GWAS datasets

Demonstrated computational efficiency with real data examples

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 RAM. 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 GWAS data.