Dataset Information

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

ABSTRACT: Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

SUBMITTER: Hao X

PROVIDER: S-EPMC5805369 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Hao Xingjie X Zeng Ping P Zhang Shujun S Zhou Xiang X

PLoS genetics 20180129 1

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-rel ...[more]

PMID: 29377896

Similar Datasets

Project description:BACKGROUND:In order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants. RESULTS:Our approach has 1) a distinct computational edge, and 2) comparable performance in terms of statistical power over other currently existing joint modeling approaches such as MetaTissue eQTL and eQTL-BMA. Using simulations, we show that our method increases the power to detect eQTLs when compared to a tissue-by-tissue approach and can exceed the performance, in terms of computational speed, of MetaTissue eQTL and eQTL-BMA. We apply our method to two publicly available expression datasets from normal human brains, one comprised of four brain regions from 150 neuropathologically normal samples and another comprised of ten brain regions from 134 neuropathologically normal samples, and show that by using our method and jointly analyzing multiple brain regions, we identify eQTLs within more genes when compared to three often used existing methods. CONCLUSIONS:Since we employ a score test-based approach, there is no need for parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Our method also accommodates the analysis of next- generation sequencing data. As an example, by modeling gene transcripts in an analogous fashion to tissues in our current formulation one would be able to test for both a variant overall effect across all isoforms of a gene as well as transcript-specific effects. We implement our approach within the R package JAGUAR, which is now available at the Comprehensive R Archive Network repository.

Project description:BackgroundUnderstanding gene regulation is important but difficult. Elucidating tissue-specific gene regulation mechanism is even more challenging and requires gene co-expression network assembled from protein-protein interaction, transcription factor and gene binding, and post-transcriptional regulation (e.g., miRNA targeting) information. The miRNA binding affinity could therefore be changed by SNP(s) located at the 3' untranslated regions (3'UTR) of the target messenger RNA (mRNA) which miRNA(s) interacts with. Genome-wide association study (GWAS) has reported significant numbers of loci hosting SNPs associated with many traits. The goal of this study is to pinpoint GWAS functional variants located in 3'UTRs and elucidate if the genes harboring these variants along with their targeting miRNAs are associated with genetic traits relevant to certain tissues.MethodsBy applying MIGWAS, CoCoNet, ANNOVAR, and DAVID bioinformatics software and utilizing the gene expression database (e.g. GTEx data) to study GWAS summary statistics for 43 traits from 28 GWAS studies, we have identified a list of miRNAs and targeted genes harboring 3'UTR variants, which could contribute to trait-relevant tissue over miRNA-target gene network.ResultsOur result demonstrated that strong association between traits and tissues exists, and in particular, the Primary Biliary Cirrhosis (PBC) trait has the most significant p-value for all 180 tissues among all 43 traits used for this study. We reported SNPs located in 3'UTR regions of genes (SFMBT2, ZC3HAV1, and UGT3A1) targeted by miRNAs for PBC trait and its tissue association network. After employing Gene Ontology (GO) analysis for PBC trait, we have also identified a very important miRNA targeted gene over miRNA-target gene network, PFKL, which encodes the liver subunit of an enzyme.ConclusionsThe non-coding variants identified from GWAS studies are casually assumed to be not critical to translated protein product. However, 3' untranslated regions (3'UTRs) of genes harbor variants can often change the binding affinity of targeting miRNAs playing important roles in protein translation degree. Our study has shown that GWAS variants could play important roles on miRNA-target gene networks by contributing the association between traits and tissues. Our analysis expands our knowledge on trait-relevant tissue network and paves way for future human disease studies.

Dataset Information

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Publications

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets