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

Abstract

BackgroundAccording to the Genetic Analysis Workshops (GAW), hundreds of thousands of SNPs have been tested for association with rheumatoid arthritis. Traditional genome-wide association studies (GWAS) have been developed to identify susceptibility genes using a "most significant SNPs/genes" model. However, many minor- or modest-risk genes are likely to be missed after adjustment of multiple testing. This screening process uses a strict selection of statistical thresholds that aim to identify susceptibility genes based only on statistical model, without considering multi-dimensional biological similarities in sequence arrangement, crystal structure, or functional categories/biological pathways between candidate and known disease genes.MethodsMultidimensional screening approaches combined with traditional statistical genetics methods can consider multiple biological backgrounds of genetic mutation, structural, and functional annotations. Here we introduce a newly developed multidimensional screening approach for rheumatoid arthritis candidate genes that considers all SNPs with nominal evidence of Bayesian association (BFLn > 0), and structural and functional similarities of corresponding genes or proteins.ResultsOur 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.ConclusionsOur 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.

A genetic algorithm approach to active subnetwork search applied to GWAS data

“…Focal adhesions serve as mechanical linkages or signalling molecules. This term is among the most significant terms in [24], [28] and [29]. This pathway is also experimentally shown to play a critical role in cellular processes such as osteoclass pathology and angiogenesis, which are known to be important for RA [30].…”

“…Known RA related genes used for comparison are collected from the OMIM database and NCBI database, and presented in [28]. RA related works that we compared our findings are the works by Wu et al [29], Zhang et al [28] and Bakir-Gungor and Sezerman [24]. Our comparative evaluation of the identified pathways can be found in Table II.…”

A New Methodology to Associate SNPs with Human Diseases According to Their Pathway Related Context

“…Here it is important to note that these associations are obtained by different methods on different datasets. For example, while Wu et al utilizes text-mining [61] , Martin et al mines GWAS data from WTCCC and NARAC studies (including variations on more cases and controls) [64] , and Zhang et al applies their methodology on GAW16 (Genetic Analysis Workshop) data [65] . PANOGA identifies previously found KEGG pathway terms with high statistical significance (terms shown in italic format for former computational identification, in italic and bold for both computational and experimental identification).…”

“…Cytokine-cytokine receptor interaction pathway has been previously identified by two other studies as RA associated and included in the KEGG functional enrichment of known disease genes [64] , [65] . Even though this term has not been found as significant in our highest scoring sub-network, it has been identified in the functional enrichment of our third highest scoring sub-network.…”

Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer

“…A Bayesian model [46] , [47] was used to screen the differential genes. Bayesian approaches compare the probability of an association between a gene expression and a disease to the probability given no such association.…”