Dataset Information

EQTL mapping identifies insertion- and deletion-specific eQTLs in multiple tissues.

ABSTRACT: Genome-wide gene expression quantitative trait loci (eQTL) mapping have been focused on single-nucleotide polymorphisms and have helped interpret findings from diseases mapping studies. The functional effect of structure variants, especially short insertions and deletions (indel) has not been well investigated. Here we impute 1,380,133 indels based on the latest 1,000 Genomes Project panel into three eQTL data sets from multiple tissues. Imputation of indels increased 9.9% power and identifies indel-specific eQTLs for 325 genes. We find introns and vicinities of UTRs are more enriched of indel eQTLs and 3.6 (single-tissue)-9.2%(multi-tissue) of previous identified eSNPs were taggers of eindels. Functional analyses identifies epigenetics marks, gene ontology categories and disease GWAS loci affected by SNPs and indels eQTLs showing tissue-consistent or tissue-specific effects. This study provides new insights into the underlying genetic architecture of gene expression across tissues and new resource to interpret function of diseases and traits associated structure variants.

SUBMITTER: Huang J

PROVIDER: S-EPMC4929061 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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eQTL mapping identifies insertion- and deletion-specific eQTLs in multiple tissues.

Huang Jinyan J Chen Jun J Esparza Jorge J Ding Jun J Elder James T JT Abecasis Goncalo R GR Lee Young-Ae YA Mark Lathrop G G Moffatt Miriam F MF Cookson William O C WO Liang Liming L

Nature communications 20150508

Genome-wide gene expression quantitative trait loci (eQTL) mapping have been focused on single-nucleotide polymorphisms and have helped interpret findings from diseases mapping studies. The functional effect of structure variants, especially short insertions and deletions (indel) has not been well investigated. Here we impute 1,380,133 indels based on the latest 1,000 Genomes Project panel into three eQTL data sets from multiple tissues. Imputation of indels increased 9.9% power and identifies i ...[more]

PMID: 25951796

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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

EQTL mapping identifies insertion- and deletion-specific eQTLs in multiple tissues.

Publications

eQTL mapping identifies insertion- and deletion-specific eQTLs in multiple tissues.

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