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.
@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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