0), and structural and functional similarities of corresponding genes or proteins. Our multidimensional screening approach extracted all risk genes (BFLn > 0) by odd ratios of hypothesis H1 to H0, and determined whether a particular group of genes shared underlying biological similarities with known disease genes. Using this method, we found 6614 risk SNPs in our Bayesian screen result set. Finally, we identified 146 likely causal genes for rheumatoid arthritis, including CD4, FGFR1, and KDR, which have been reported as high risk factors by recent studies. We must denote that 790 (96.1%) of genes identified by GWAS could not easily be classified into related functional categories or biological processes associated with the disease, while our candidate genes shared underlying biological similarities (e.g. were in the same pathway or GO term) and contributed to disease etiology, but where common variations in each of these genes make modest contributions to disease risk. We also found 6141 risk SNPs that were too minor to be detected by conventional approaches, and associations between 58 candidate genes and rheumatoid arthritis were verified by literature retrieved from the NCBI PubMed module. Our proposed approach to the analysis of GAW16 data for rheumatoid arthritis was based on an underlying biological similarities-based method applied to candidate and known disease genes. Application of our method could identify likely causal candidate disease genes of rheumatoid arthritis, and could yield biological insights that not detected when focusing only on genes that give the strongest evidence by multiple testing. We hope that our proposed method complements the "most significant SNPs/genes" model, and provides additional insights into the pathogenesis of rheumatoid arthritis and other diseases, when searching datasets for hundreds of genetic variances."/> Skip to content

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BMC Medical Genomics

Open Access
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A towards-multidimensional screening approach to predict candidate genes of rheumatoid arthritis based on SNP, structural and functional annotations

BMC Medical Genomics20103:38

https://doi.org/10.1186/1755-8794-3-38

Received: 3 December 2009

Accepted: 20 August 2010

Published: 20 August 2010

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Open Peer Review reports

Pre-publication versions of this article and author comments to reviewers are available by contacting info@biomedcentral.com.

Original Submission
3 Dec 2009 Submitted Original manuscript
2 Feb 2010 Reviewed Reviewer Report - Euijung Ryu
25 Feb 2010 Reviewed Reviewer Report - Nora L. Nock
Resubmission - Version 2
Submitted Manuscript version 2
14 Apr 2010 Author responded Author comments - Lina Chen
Resubmission - Version 3
14 Apr 2010 Submitted Manuscript version 3
3 May 2010 Reviewed Reviewer Report - Euijung Ryu
12 May 2010 Reviewed Reviewer Report - Nora L. Nock
Resubmission - Version 4
Submitted Manuscript version 4
1 Jun 2010 Author responded Author comments - Lina Chen
Resubmission - Version 5
1 Jun 2010 Submitted Manuscript version 5
30 Jun 2010 Reviewed Reviewer Report - Nora L. Nock
31 Jul 2010 Author responded Author comments - Lina Chen
Resubmission - Version 6
31 Jul 2010 Submitted Manuscript version 6
20 Aug 2010 Author responded Author comments - Lina Chen
Resubmission - Version 7
20 Aug 2010 Submitted Manuscript version 7
Resubmission - Version 8
Submitted Manuscript version 8
20 Aug 2010 Author responded Author comments - Lina Chen
Resubmission - Version 9
20 Aug 2010 Submitted Manuscript version 9
Publishing
20 Aug 2010 Editorially accepted
20 Aug 2010 Article published 10.1186/1755-8794-3-38

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Authors’ Affiliations

(1)
Department of Biophysics, College of Bioinformatics Science and Technology, Harbin Medical University

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