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

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          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.

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          The online version of this article (doi:10.1186/s13073-016-0338-4) contains supplementary material, which is available to authorized users.

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          From genome to function by studying eQTLs.

          Genome-wide association studies (GWASs) have shown a large number of genetic variants to be associated with complex diseases. The identification of the causal variant within an associated locus can sometimes be difficult because of the linkage disequilibrium between the associated variants and because most GWAS loci contain multiple genes, or no genes at all. Expression quantitative trait locus (eQTL) mapping is a method used to determine the effects of genetic variants on gene expression levels. eQTL mapping studies have enabled the prioritization of genetic variants within GWAS loci, and have shown that trait-associated single nucleotide polymorphisms (SNPs) often function in a tissue- or cell type-specific manner, sometimes having downstream effects on completely different chromosomes. Furthermore, recent RNA-sequencing (RNA-seq) studies have shown that a large repertoire of transcripts is available in cells, which are actively regulated by (trait-associated) variants. Future eQTL mapping studies will focus on broadening the range of available tissues and cell types, in order to determine the key tissues and cell types involved in complex traits. Finally, large meta-analyses will be able to pinpoint the causal variants within the trait-associated loci and determine their downstream effects in greater detail. This article is part of a Special Issue entitled: From Genome to Function. Copyright © 2014 Elsevier B.V. All rights reserved.
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            Two-Sample Instrumental Variables Estimators

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              Mendelian randomization studies: a review of the approaches used and the quality of reporting.

              Mendelian randomization (MR) studies investigate the effect of genetic variation in levels of an exposure on an outcome, thereby using genetic variation as an instrumental variable (IV). We provide a meta-epidemiological overview of the methodological approaches used in MR studies, and evaluate the discussion of MR assumptions and reporting of statistical methods.
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                Author and article information

                Contributors
                j.pavlides@uq.edu.au
                z.zhu1@uq.edu.au
                j.gratten1@uq.edu.au
                a.mcrae@uq.edu.au
                naomi.wray@uq.edu.au
                jian.yang@uq.edu.au
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                9 August 2016
                9 August 2016
                2016
                : 8
                Affiliations
                Queensland Brain Institute, University of Queensland, Brisbane, Queensland Australia
                Article
                338
                10.1186/s13073-016-0338-4
                4979185
                27506385
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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