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

Zheng, Jie, Erzurumluoglu, A. Mesut, Elsworth, Benjamin L., Kemp, John P., Howe, Laurence, Haycock, Philip C., Hemani, Gibran, Tansey, Katherine, Laurin, Charles, Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, Pourcain BS, Warrington, Nicole M. , Finucane, Hilary K., Price, Alkes L., Bulik-Sullivan, Brendan K., Anttila, Verneri, Paternoster, Lavinia, Gaunt, Tom R. , Evans, David M. and Neale, Benjamin M. (2017) 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. Bioinformatics, 33 2: 272-279. doi:10.1093/bioinformatics/btw613


Author Zheng, Jie
Erzurumluoglu, A. Mesut
Elsworth, Benjamin L.
Kemp, John P.
Howe, Laurence
Haycock, Philip C.
Hemani, Gibran
Tansey, Katherine
Laurin, Charles
Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium
Pourcain BS
Warrington, Nicole M.
Finucane, Hilary K.
Price, Alkes L.
Bulik-Sullivan, Brendan K.
Anttila, Verneri
Paternoster, Lavinia
Gaunt, Tom R.
Evans, David M.
Neale, Benjamin M.
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
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
1367-4811
Publication date 2017-01-15
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btw613
Open Access Status Not yet assessed
Volume 33
Issue 2
Start page 272
End page 279
Total pages 8
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2018
Language eng
Formatted 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.
Keyword LD score regression
GWAS
Genome-wide association study
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
UQ Diamantina Institute Publications
 
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Created: Wed, 05 Apr 2017, 14:27:31 EST by Nicole Warrington on behalf of School of Biological Sciences