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      • Record: found
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      LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

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          Abstract

          Motivation

          LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.

          Results

          In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.

          Availability and Implementation

          The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references15

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          'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

          George Davey Smith, Shah Ebrahim (2003)
          Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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            GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.

            Cornelius Rietveld, Sarah E. Medland, Jaime Derringer … (2013)
            A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.
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              Parent-of-origin specific allelic associations among 106 genomic loci for age at menarche

              John RB Perry, Felix Day, Cathy E Elks … (2014)
              Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality 1 . Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation 2,3 , but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P<5×10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1/WDR25, MKRN3/MAGEL2 and KCNK9) demonstrating parent-of-origin specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and gamma-aminobutyric acid-B2 receptor signaling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.
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                Author and article information

                Contributors
                Jonathan Wren: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 January 2017
                Publication date (Electronic): 22 September 2016
                Publication date PMC-release: 22 September 2016
                Volume: 33
                Issue: 2
                Pages: 272-279
                Affiliations
                [1 ]MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK
                [2 ]Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
                [3 ]University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia
                [4 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [5 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [6 ]Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
                Author notes
                To whom correspondence should be addressed. Email: jie.zheng@ 123456bristol.ac.uk

                The authors wish it to be known that, in their opinion, the last two authors should be regarded as Joint Last Authors.

                Article
                Publisher ID: btw613
                DOI: 10.1093/bioinformatics/btw613
                PMC ID: 5542030
                PubMed ID: 27663502
                SO-VID: ab29f5b4-f790-4af5-bac4-6143f0bfafd8
                Copyright © © The Author 2016. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 05 May 2016
                Date revision received : 19 August 2016
                Date accepted : 20 September 2016
                Page count
                Pages: 8
                Funding
                Funded by: Medical Research Council 10.13039/501100000265
                Award ID: (MC_UU_12013/4 and MC_UU_12013/8)
                Award ID: 1R01MH101244-02
                Award ID: 1R01MH107649-01
                Funded by: Cancer Research UK programme
                Award ID: C18281/A19169
                Funded by: Cancer Research UK Population Research
                Award ID: (grant number C52724/A20138)
                Categories
                Subject: Original Papers
                Subject: Databases and Ontologies

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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