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Int. J. Mol. Sci. 2014, 15(10), 17601-17621; doi:10.3390/ijms151017601

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

1
Instituto Nacional de Saúde Doutor Ricardo Jorge, Av. Padre Cruz, Lisboa 1649-016, Portugal
2
Centre for Biodiversity, Functional & Integrative Genomics, Faculty of Sciences, University of Lisboa, Lisboa 1749-016, Portugal
3
Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
*
Author to whom correspondence should be addressed.
Received: 30 April 2014 / Revised: 1 September 2014 / Accepted: 22 September 2014 / Published: 29 September 2014
(This article belongs to the Collection Human Single Nucleotide Polymorphisms and Disease Diagnostics)
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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. View Full-Text
Keywords: genome-wide association studies (GWAS); missing heritability; protein-protein interaction networks; functional coherence genome-wide association studies (GWAS); missing heritability; protein-protein interaction networks; functional coherence
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Correia, C.; Diekmann, Y.; Vicente, A.M.; Pereira-Leal, J.B. Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise. Int. J. Mol. Sci. 2014, 15, 17601-17621.

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