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      A Bayesian framework for multiple trait colocalization from summary association statistics

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          Abstract

          Most genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g. expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work, we propose multiple-trait-coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci.

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          Most cited references16

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          Joint analysis of functional genomic data and genome-wide association studies of 18 human traits.

          Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWASs). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. I describe a statistical model that uses association statistics computed across the genome to identify classes of genomic elements that are enriched with or depleted of loci influencing a trait. The model naturally incorporates multiple types of annotations. I applied the model to GWASs of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, body mass index, and Crohn disease. For each trait, I used the model to evaluate the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over 100 tissues and cell lines. The fraction of phenotype-associated SNPs influencing protein sequence ranged from around 2% (for platelet volume) up to around 20% (for low-density lipoprotein cholesterol), repressed chromatin was significantly depleted for SNPs associated with several traits, and cell-type-specific DNase-I hypersensitive sites were enriched with SNPs associated with several traits (for example, the spleen in platelet volume). Finally, reweighting each GWAS by using information from functional genomics increased the number of loci with high-confidence associations by around 5%. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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            Mapping DNA methylation across development, genotype, and schizophrenia in the human frontal cortex

            DNA methylation (DNAm) is important in brain development, and potentially in schizophrenia. We characterized DNAm in prefrontal cortex from 335 non-psychiatric controls across the lifespan and 191 patients with schizophrenia, and identified widespread changes in the transition from prenatal to postnatal life. These DNAm changes manifest in the transcriptome, correlate strongly with a shifting cellular landscape, and overlap regions of genetic risk for schizophrenia. A quarter of published GWAS-suggestive loci (4,208/15,930, p<10−100) manifest as significant methylation quantitative trait loci (meQTLs), including 59.6% of GWAS-positive schizophrenia loci. We identified 2,104 CpGs that differ between schizophrenia patients and controls, enriched for genes related to development and neurodifferentiation. The schizophrenia-associated CpGs strongly correlate with changes related to the prenatal-postnatal transition and show slight enrichment for GWAS risk loci, while not corresponding to CpGs differentiating adolescence from later adult life. These data implicate an epigenetic component to the developmental origins of this disorder.
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              Bayesian statistical methods for genetic association studies.

              Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.
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                Author and article information

                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                August 01 2018
                August 01 2018
                March 19 2018
                August 01 2018
                August 01 2018
                March 19 2018
                : 34
                : 15
                : 2538-2545
                Affiliations
                [1 ]Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA
                [2 ]Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
                [3 ]New York Genome Center, New York, NY, USA
                [4 ]Department of Computational Biology and Genomics, Biogen, Cambridge, MA, USA
                [5 ]Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [6 ]Department of Biomedicine, The Lundbeck Foundation Initiative of Integrative Psychiatric Research (iPSYCH), Aarhus University, Aarhus, Denmark
                [7 ]Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
                [8 ]Departments of Mental Health and Biostatistics, Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
                [9 ]Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [10 ]Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
                Article
                10.1093/bioinformatics/bty147
                6061859
                29579179
                0f233a3e-0df8-4c03-9120-bb388ac0a95b
                © 2018

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