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      Partitioning heritability by functional annotation using genome-wide association summary statistics.

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          Abstract

          Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.

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          An Integrated Encyclopedia of DNA Elements in the Human Genome

          Iakes Ezkurdia (2016)
          Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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            GCTA: a tool for genome-wide complex trait analysis.

            Jian Yang, S. Lee, Michael E Goddard … (2011)
            For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Hilary Finucane, Stephan Ripke, Nick Patterson … (2015)
              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
              Article 0 comments Cited 1547 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nat. Genet.
                Title: Nature genetics
                Publisher: Springer Nature
                ISSN (Electronic): 1546-1718
                ISSN (Print): 1061-4036
                Publication date (Electronic): Nov 2015
                Volume: 47
                Issue: 11
                Affiliations
                [1 ] Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
                [2 ] Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
                [3 ] Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
                [4 ] Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                [5 ] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
                [6 ] Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
                [7 ] Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA.
                [8 ] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                [9 ] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.
                [10 ] Department of Computer Science, Harvard University, Cambridge, Massachusetts, USA.
                [11 ] Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
                [12 ] Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                [13 ] Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.
                [14 ] Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA.
                [15 ] Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
                [16 ] Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
                [17 ] Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK.
                Article
                Publisher Item ID: ng.3404 Mid ID: NIHMS718020
                DOI: 10.1038/ng.3404
                PMC ID: 4626285
                PubMed ID: 26414678
                SO-VID: 676cba6b-68b3-4a2d-9c38-98b07372c79b
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