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Title: 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.
Authors: Zheng, J.
Erzurumluoglu, A. Mesut
Elsworth, B. L.
Kemp, J. P.
Howe, L.
Haycock, P. C.
Hemani, G.
Tansey, K.
Laurin, C.
Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium
Pourcain, B. S.
Warrington, N. M.
Finucane, H. K.
Price, A. L.
Bulik-Sullivan, B. K.
Anttila, V.
Paternoster, L.
Gaunt, T. R.
Evans, D. M.
Neale, B. M.
First Published: 22-Sep-2016
Publisher: Oxford University Press (OUP)
Citation: Bioinformatics, 2016, 1–8
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 CONTACT: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
DOI Link: 10.1093/bioinformatics/btw613
ISSN: 1367-4803
eISSN: 1460-2059
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:Published Articles, Dept. of Health Sciences

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