License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2021.10
URN: urn:nbn:de:0030-drops-143632
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14363/
Jew, Brandon ;
Li, Jiajin ;
Sankararaman, Sriram ;
Sul, Jae Hoon
An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts
Abstract
Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale.
BibTeX - Entry
@InProceedings{jew_et_al:LIPIcs.WABI.2021.10,
author = {Jew, Brandon and Li, Jiajin and Sankararaman, Sriram and Sul, Jae Hoon},
title = {{An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts}},
booktitle = {21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
pages = {10:1--10:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-200-6},
ISSN = {1868-8969},
year = {2021},
volume = {201},
editor = {Carbone, Alessandra and El-Kebir, Mohammed},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2021/14363},
URN = {urn:nbn:de:0030-drops-143632},
doi = {10.4230/LIPIcs.WABI.2021.10},
annote = {Keywords: Meta-analysis, Linear mixed models, multiple-context genetic association}
}
Keywords: |
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Meta-analysis, Linear mixed models, multiple-context genetic association |
Collection: |
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21st International Workshop on Algorithms in Bioinformatics (WABI 2021) |
Issue Date: |
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2021 |
Date of publication: |
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22.07.2021 |
Supplementary Material: |
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mcLMM is available as an R package: |