Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Autoren

Catarina Correia
Yoan Diekmann
Astrid Vicente
José Pereira-Leal

DOI

10.3390/ijms151017601

eISSN

1422-0067

Ausgabe der Veröffentlichung

10

Zeitschrift

International Journal of Molecular Sciences

Sprache

en

Online publication date

2014

Paginierung

17601 - 17621

Status

Published online

Herausgeber

MDPI AG

Herausgeber URL

http://dx.doi.org/10.3390/ijms151017601

Datum der Datenerfassung

2019

Titel

Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Ausgabe der Zeitschrift

15

Data source: Crossref

Abstract

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

Addresses

Instituto Nacional de Saúde Doutor Ricardo Jorge, Av. Padre Cruz, Lisboa 1649-016, Portugal. ccorreia@igc.gulbenkian.pt.

Autoren

Catarina Correia
Yoan Diekmann
Astrid M Vicente
José B Pereira-Leal

DOI

10.3390/ijms151017601

eISSN

1422-0067

Externe Identifier

PubMed Identifier: 25268625
PubMed Central ID: PMC4227180

Open access

true

ISSN

1422-0067

Ausgabe der Veröffentlichung

10

Zeitschrift

International journal of molecular sciences

Schlüsselwörter

Humans
Breast Neoplasms
Genetic Predisposition to Disease
Models, Statistical
Polymorphism, Single Nucleotide
Female
Genome-Wide Association Study
Protein Interaction Maps

Sprache

eng

Medium

Electronic

Online publication date

2014

Open access status

Open Access

Paginierung

17601 - 17621

Datum der Veröffentlichung

2014

Status

Published

Publisher licence

CC BY

Datum der Datenerfassung

2014

Titel

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.

Sub types

Research Support, Non-U.S. Gov't
research-article
Journal Article

Ausgabe der Zeitschrift

15

Files

https://www.mdpi.com/1422-0067/15/10/17601/pdf?version=1411982218 https://europepmc.org/articles/PMC4227180?pdf=render

Data source: Europe PubMed Central

Abstract

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

Date of acceptance

2014

Autoren

Catarina Correia
Yoan Diekmann
Astrid M Vicente
José B Pereira-Leal

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/25268625

DOI

10.3390/ijms151017601

eISSN

1422-0067

Externe Identifier

PubMed Central ID: PMC4227180

Ausgabe der Veröffentlichung

10

Zeitschrift

Int J Mol Sci

Schlüsselwörter

Breast Neoplasms
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Models, Statistical
Polymorphism, Single Nucleotide
Protein Interaction Maps

Sprache

eng

Country

Switzerland

Paginierung

17601 - 17621

PII

ijms151017601

Datum der Veröffentlichung

2014

Status

Published online

Datum, an dem der Datensatz öffentlich gemacht wurde

2015

Titel

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.

Sub types

Journal Article
Research Support, Non-U.S. Gov't

Ausgabe der Zeitschrift

15

Data source: PubMed

Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Files

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