Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.18/2446 |
Resumo: | 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. |
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Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical NoiseGenome-wide Association Studies (GWAS)Missing HeritabilityProtein-proteinInteraction NetworksFunctional CoherenceHundreds 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.Molecular Diversity Preservation International (MDPI)Repositório Científico do Instituto Nacional de SaúdeCorreia, C.Diekmann, Y.Vicente, A.M.Pereira-Leal, J.B.2014-11-07T14:19:57Z2014-092014-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.18/2446engInt J Mol Sci. 2014 Sep 29;15(10):17601-21. doi: 10.3390/ijms151017601.1422-0067doi:10.3390/ijms151017601info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-20T15:39:17Zoai:repositorio.insa.pt:10400.18/2446Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:37:28.151702Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
title |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
spellingShingle |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise Correia, C. Genome-wide Association Studies (GWAS) Missing Heritability Protein-protein Interaction Networks Functional Coherence |
title_short |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
title_full |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
title_fullStr |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
title_full_unstemmed |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
title_sort |
Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise |
author |
Correia, C. |
author_facet |
Correia, C. Diekmann, Y. Vicente, A.M. Pereira-Leal, J.B. |
author_role |
author |
author2 |
Diekmann, Y. Vicente, A.M. Pereira-Leal, J.B. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Nacional de Saúde |
dc.contributor.author.fl_str_mv |
Correia, C. Diekmann, Y. Vicente, A.M. Pereira-Leal, J.B. |
dc.subject.por.fl_str_mv |
Genome-wide Association Studies (GWAS) Missing Heritability Protein-protein Interaction Networks Functional Coherence |
topic |
Genome-wide Association Studies (GWAS) Missing Heritability Protein-protein Interaction Networks Functional Coherence |
description |
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. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-11-07T14:19:57Z 2014-09 2014-09-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.18/2446 |
url |
http://hdl.handle.net/10400.18/2446 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Int J Mol Sci. 2014 Sep 29;15(10):17601-21. doi: 10.3390/ijms151017601. 1422-0067 doi:10.3390/ijms151017601 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Molecular Diversity Preservation International (MDPI) |
publisher.none.fl_str_mv |
Molecular Diversity Preservation International (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799132108770246656 |