Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits.

Jennifer M Whitehead Pavlides,Jian Yang,Jacob Gratten,Allan F Mcrae,Naomi R Wray,Zhihong Zhu

doi:10.1186/s13073-016-0338-4

Jennifer M Whitehead Pavlides, Jian Yang + Show 4 more

Open Access

https://doi.org/10.1186/s13073-016-0338-4

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Journal: Genome medicine	Publication Date: Aug 9, 2016
Citations: 89	License type: cc-by

Affiliation: University of Queensland

Abstract

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex traits and diseases. However, elucidating the causal genes underlying GWAS hits remains challenging. We applied the summary data-based Mendelian randomization (SMR) method to 28 GWAS summary datasets to identify genes whose expression levels were associated with traits and diseases due to pleiotropy or causality (the expression level of a gene and the trait are affected by the same causal variant at a locus). We identified 71 genes, of which 17 are novel associations (no GWAS hit within 1 Mb distance of the genes). We integrated all the results in an online database (http://www.cnsgenomics/shiny/SMRdb/), providing important resources to prioritize genes for further follow-up, for example in functional studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0338-4) contains supplementary material, which is available to authorized users.

Highlights

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with various complex traits, disorders, and diseases [1, 2]
The GWAS paradigm exploits the linkage disequilibrium (LD) correlation structure of the genome, which means that the majority of the variation in the genome can be captured in a cost-effective way by genotyping only a few hundred thousand variants, followed by imputation of non-genotyped variants using a densely genotyped reference panel [3]
The LD structure means that identified associations frequently point to genomic regions that harbor many genes, and it is extremely difficult to prioritize among these genes to identify the most functionally relevant genes using GWAS data alone

Summary

Introduction

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with various complex traits, disorders, and diseases [1, 2]. The GWAS paradigm exploits the linkage disequilibrium (LD) correlation structure of the genome, which means that the majority of the variation in the genome can be captured in a cost-effective way by genotyping only a few hundred thousand variants, followed by imputation of non-genotyped variants using a densely genotyped reference panel [3]. The LD structure means that identified associations frequently point to genomic regions that harbor many genes, and it is extremely difficult to prioritize among these genes to identify the most functionally relevant genes using GWAS data alone. Laboratory-based follow-up of the associated regions is costly and prohibitive given the number of putatively causal variants in a typical genome-wide significant locus. Several recent methods [7,8,9,10,11] have

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