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plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation
Tabitha K Peter1, Anna C Reisetter1, Yujing Lu1
1Department of Biostatistics, University of Iowa, 145 N Riverside Dr, Iowa 52242, United States.
None:
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.
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