Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.4230/LIPIcs.WABI.2021.10 | WABI |
Keywords | DocType | Volume |
Applied computing → Bioinformatics,Applied computing → Computational genomics,Linear mixed models,Meta-analysis,multiple-context genetic association | Conference | 2016 |
ISSN | Citations | PageRank |
1868-8969 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brandon Jew | 1 | 0 | 0.34 |
| Jiajin Li | 2 | 0 | 0.34 |
| Sriram Sankararaman | 3 | 0 | 0.34 |
| Jae Hoon Sul | 4 | 0 | 0.34 |