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      Properties of human disease genes and the role of genes linked to Mendelian disorders in complex disease aetiology

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          Abstract

          Do genes presenting variation that has been linked to human disease have different biological properties than genes that have never been related to disease? What is the relationship between disease and fitness? Are the evolutionary pressures that affect genes linked to Mendelian diseases the same to those acting on genes whose variation contributes to complex disorders? The answers to these questions could shed light on the architecture of human genetic disorders and may have relevant implications when designing mapping strategies in future genetic studies. Here we show that, relative to non-disease genes, human disease (HD) genes have specific evolutionary profiles and protein network properties. Additionally, our results indicate that the mutation-selection balance renders an insufficient account of the evolutionary history of some HD genes and that adaptive selection could also contribute to shape their genetic architecture. Notably, several biological features of HD genes depend on the type of pathology (complex or Mendelian) with which they are related. For example, genes harbouring both causal variants for Mendelian disorders and risk factors for complex disease traits (Complex-Mendelian genes), tend to present higher functional relevance in the protein network and higher expression levels than genes associated only with complex disorders. Moreover, risk variants in Complex-Mendelian genes tend to present higher odds ratios than those on genes associated with the same complex disorders but with no link to Mendelian diseases. Taken together, our results suggest that genetic variation at genes linked to Mendelian disorders plays an important role in driving susceptibility to complex disease.

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          Most cited references51

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          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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            The BioGRID Interaction Database: 2011 update

            The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions.
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              Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"?

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                Author and article information

                Journal
                Hum Mol Genet
                Hum. Mol. Genet
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                01 February 2017
                04 January 2017
                04 January 2017
                : 26
                : 3
                : 489-500
                Affiliations
                [1 ]Institute of Evolutionary Biology (CSIC-UPF), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
                [2 ]National Institute for Bioinformatics (INB), Barcelona Biomedical Research Park (PRBB), Barcelona, Spain
                [3 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona Biomedical Research Park (PRBB), Barcelona, Spain
                [4 ]Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona Biomedical Research Park (PRBB), Barcelona, Spain
                Author notes
                [* ]To whom correspondence should be addressed at: Elena Bosch, Institute of Evolutionary Biology (CSIC-UPF), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Doctor Aiguader 88, 08003 Barcelona, Spain. Tel: +34 93 3160841; Fax: +34 93 3960901; Email: elena.bosch@ 123456upf.edu

                These authors contributed equally.

                Article
                ddw405
                10.1093/hmg/ddw405
                5409085
                28053046
                130902ea-91f5-4435-9f9f-1f5ac203341f
                © The Author 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 12 April 2016
                : 10 November 2016
                : 23 November 2016
                Page count
                Pages: 12
                Funding
                Funded by: Ministerio de Ciencia e Innovación, Spain
                Award ID: SAF2011-29239 to EB and BFU2012-38236
                Funded by: Direcció General de Recerca, Generalitat de Catalunya
                Award ID: 2014SGR1311 and 2014SGR866
                Funded by: Spanish National Institute of Bioinfomatics of the Instituto de Salud Carlos III
                Award ID: PT13/0001/0026
                Funded by: FEDER (Fondo Europeo de Desarrollo Regional)/FSE (Fondo Social Europeo)
                Categories
                Articles

                Genetics
                Genetics

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