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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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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, Mesut; Elsworth, Benjamin; Howe, Laurence; Haycock, Philip; Hemani, Gibran; Tansey, Katherine; Laurin, Charles; St Pourcain, Beate; Warrington, Nicole M.; Finucane, Hilary; Price, Alkes L; Bulik-Sullivan, Brendan; Anttila, Verneri; Paternoster, Lavinia; Gaunt, Tom; Evans, David; Neale, Benjamin M.

In: Bioinformatics, Vol. 33, No. 2, 15.01.2017, p. 272-279.

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Zheng, Jie ; Erzurumluoglu, Mesut ; Elsworth, Benjamin ; Howe, Laurence ; Haycock, Philip ; Hemani, Gibran ; Tansey, Katherine ; Laurin, Charles ; St Pourcain, Beate ; Warrington, Nicole M. ; Finucane, Hilary ; Price, Alkes L ; Bulik-Sullivan, Brendan ; Anttila, Verneri ; Paternoster, Lavinia ; Gaunt, Tom ; Evans, David ; Neale, Benjamin M. / 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. In: Bioinformatics. 2017 ; Vol. 33, No. 2. pp. 272-279.

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@article{0ef68d1a62e9436192f32c36d8d1070f,
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",
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/",
keywords = "LD score regression, Genetic correlation, GWAS summary statistics, database",
author = "Jie Zheng and Mesut Erzurumluoglu and Benjamin Elsworth and Laurence Howe and Philip Haycock and Gibran Hemani and Katherine Tansey and Charles Laurin and {St Pourcain}, Beate and Warrington, {Nicole M.} and Hilary Finucane and Price, {Alkes L} and Brendan Bulik-Sullivan and Verneri Anttila and Lavinia Paternoster and Tom Gaunt and David Evans and Neale, {Benjamin M}",
year = "2017",
month = "1",
day = "15",
doi = "10.1101/051094",
language = "English",
volume = "33",
pages = "272--279",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "2",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - LD Hub

T2 - 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

AU - Zheng, Jie

AU - Erzurumluoglu, Mesut

AU - Elsworth, Benjamin

AU - Howe, Laurence

AU - Haycock, Philip

AU - Hemani, Gibran

AU - Tansey, Katherine

AU - Laurin, Charles

AU - St Pourcain, Beate

AU - Warrington, Nicole M.

AU - Finucane, Hilary

AU - Price, Alkes L

AU - Bulik-Sullivan, Brendan

AU - Anttila, Verneri

AU - Paternoster, Lavinia

AU - Gaunt, Tom

AU - Evans, David

AU - Neale, Benjamin M

PY - 2017/1/15

Y1 - 2017/1/15

N2 - 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/

AB - 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/

KW - LD score regression

KW - Genetic correlation

KW - GWAS summary statistics

KW - database

U2 - 10.1101/051094

DO - 10.1101/051094

M3 - Article

C2 - 27663502

VL - 33

SP - 272

EP - 279

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 2

ER -