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

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

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Author Details

Brandon Jew
  • Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
Jiajin Li
  • Department of Human Genetics, University of California, Los Angeles, CA, USA
Sriram Sankararaman
  • Department of Human Genetics, University of California, Los Angeles, CA, USA
  • Department of Computer Science, University of California, Los Angeles, CA, USA
  • Department of Computational Medicine, University of California, Los Angeles, CA, USA
Jae Hoon Sul
  • Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA

Funding

  • Jew, Brandon: National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650604.
  • Sankararaman, Sriram: NIH R35GM125055, Alfred P. Sloan Research Fellowship, NSF III-1705121, CAREER 1943497.
  • Sul, Jae Hoon: National Institute of Environmental Health Sciences (NIEHS) [K01 ES028064]; the National Science Foundation grant [#1705197]; the National Institute of Neurological Disorders and Stroke (NINDS) [R01 NS102371]; and NINDS [R03 HL150604].

Cite As Get BibTex

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) https://doi.org/10.4230/LIPIcs.WABI.2021.10

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Applied computing → Computational genomics
Keywords
  • Meta-analysis
  • Linear mixed models
  • multiple-context genetic association

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Supplementary Materials

References

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