2026-07-27T09:00:59Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14363 2024-03-06T10:53:59Z ddc:004 open_access

An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts Jew, Brandon Li, Jiajin Sankararaman, Sriram Sul, Jae Hoon Meta-analysis Linear mixed models multiple-context genetic association 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Brandon Jew and Jiajin Li and Sriram Sankararaman and Jae Hoon Sul 2021 Is Part Of LIPIcs, Volume 201, 21st International Workshop on Algorithms in Bioinformatics (WABI 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2021.10 urn:nbn:de:0030-drops-143632 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2021.10 eng https://creativecommons.org/licenses/by/4.0/legalcode