GWAB

A web server for the network-based boosting of human genome-wide association data

Jung Eun Shim, Changbae Bang, Sunmo Yang, Tak Lee, Sohyun Hwang, Chan Yeong Kim, U. Martin Singh-Blom, Edward M. Marcotte, In suk Lee

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10 â '8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

Original languageEnglish
Pages (from-to)W154-W161
JournalNucleic acids research
Volume45
Issue numberW1
DOIs
Publication statusPublished - 2017 Jul 3

Fingerprint

Genome-Wide Association Study
Human Genome
Genes
Medical Genetics
Inborn Genetic Diseases
Gene Regulatory Networks
Sample Size
Single Nucleotide Polymorphism
Coronary Artery Disease
Costs and Cost Analysis

All Science Journal Classification (ASJC) codes

  • Genetics

Cite this

Shim, Jung Eun ; Bang, Changbae ; Yang, Sunmo ; Lee, Tak ; Hwang, Sohyun ; Kim, Chan Yeong ; Singh-Blom, U. Martin ; Marcotte, Edward M. ; Lee, In suk. / GWAB : A web server for the network-based boosting of human genome-wide association data. In: Nucleic acids research. 2017 ; Vol. 45, No. W1. pp. W154-W161.
@article{5b19044a661b464bb97b201aa4c60e83,
title = "GWAB: A web server for the network-based boosting of human genome-wide association data",
abstract = "During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10 {\^a} '8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.",
author = "Shim, {Jung Eun} and Changbae Bang and Sunmo Yang and Tak Lee and Sohyun Hwang and Kim, {Chan Yeong} and Singh-Blom, {U. Martin} and Marcotte, {Edward M.} and Lee, {In suk}",
year = "2017",
month = "7",
day = "3",
doi = "10.1093/nar/gkx284",
language = "English",
volume = "45",
pages = "W154--W161",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "W1",

}

Shim, JE, Bang, C, Yang, S, Lee, T, Hwang, S, Kim, CY, Singh-Blom, UM, Marcotte, EM & Lee, IS 2017, 'GWAB: A web server for the network-based boosting of human genome-wide association data', Nucleic acids research, vol. 45, no. W1, pp. W154-W161. https://doi.org/10.1093/nar/gkx284

GWAB : A web server for the network-based boosting of human genome-wide association data. / Shim, Jung Eun; Bang, Changbae; Yang, Sunmo; Lee, Tak; Hwang, Sohyun; Kim, Chan Yeong; Singh-Blom, U. Martin; Marcotte, Edward M.; Lee, In suk.

In: Nucleic acids research, Vol. 45, No. W1, 03.07.2017, p. W154-W161.

Research output: Contribution to journalArticle

TY - JOUR

T1 - GWAB

T2 - A web server for the network-based boosting of human genome-wide association data

AU - Shim, Jung Eun

AU - Bang, Changbae

AU - Yang, Sunmo

AU - Lee, Tak

AU - Hwang, Sohyun

AU - Kim, Chan Yeong

AU - Singh-Blom, U. Martin

AU - Marcotte, Edward M.

AU - Lee, In suk

PY - 2017/7/3

Y1 - 2017/7/3

N2 - During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10 â '8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

AB - During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10 â '8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

UR - http://www.scopus.com/inward/record.url?scp=85023205647&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85023205647&partnerID=8YFLogxK

U2 - 10.1093/nar/gkx284

DO - 10.1093/nar/gkx284

M3 - Article

VL - 45

SP - W154-W161

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - W1

ER -