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Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

ABSTRACT: Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.

SUBMITTER: Zou J

PROVIDER: S-EPMC6952111 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

Zou Jennifer J Hormozdiari Farhad F Jew Brandon B Castel Stephane E SE Lappalainen Tuuli T Ernst Jason J Sul Jae Hoon JH Eskin Eleazar E

PLoS genetics 20191213 12

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as li ...[more]

PMID: 31834882

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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

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

Publications

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

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