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A statistical framework for joint eQTL analysis in multiple tissues.

ABSTRACT: Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR?=?0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

SUBMITTER: Flutre T

PROVIDER: S-EPMC3649995 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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A statistical framework for joint eQTL analysis in multiple tissues.

Flutre Timothée T Wen Xiaoquan X Pritchard Jonathan J Stephens Matthew M

PLoS genetics 20130509 5

Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical fr ...[more]

PMID: 23671422

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Project description:BackgroundDNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.ResultsOur method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeobox member gene (LHX9), which may have a role in the neural development.ConclusionsOur score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model's flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.

Dataset Information

A statistical framework for joint eQTL analysis in multiple tissues.

Publications

A statistical framework for joint eQTL analysis in multiple tissues.

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