plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909034355884032 |
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| author | Peter, Tabitha K. Reisetter, Anna C. Lu, Yujing Rysavy, Oscar A. Breheny, Patrick J. |
| author_facet | Peter, Tabitha K. Reisetter, Anna C. Lu, Yujing Rysavy, Oscar A. Breheny, Patrick J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure Peter, Tabitha K. Reisetter, Anna C. Lu, Yujing Rysavy, Oscar A. Breheny, Patrick J. Computation 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. |
| title | plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure |
| topic | Computation |
| url | https://arxiv.org/abs/2502.01577 |