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DOI: 10.4230/LIPIcs.WABI.2021.10

An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

Authors: Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul

Published in: LIPIcs, Volume 201, 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)

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.

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Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@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/entities/document/10.4230/LIPIcs.WABI.2021.10},
  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}
}

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An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

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