用于跨多种背景的荟萃分析关联研究的高效线性混合模型框架.

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. 然而, traditional approaches to fitting
these models can be computationally intractable. 这里, 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.