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Gene Prioritization in Type 2 Diabetes Using Domain Interactions And Sharma et al. BMC Genomics 2010, 11:84 http://www.biomedcentral.com/1471-2164/11/84 RESEARCH ARTICLE Open Access Gene prioritization in Type 2 Diabetes using domain interactions and network analysis Amitabh Sharma1†, Sreenivas Chavali1†, Rubina Tabassum1, Nikhil Tandon2, Dwaipayan Bharadwaj1* Abstract Background: Identification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes. Results: Our approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis. Conclusions: Prioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D. Background diabetes that are not captured in GWA studies. Hence, Type 2 Diabetes (T2D) is a complex disease, encompass- all these approaches have yielded very limited success, ing various metabolic abnormalities influenced by both warranting new approaches, complementing the existing gene-environment and gene-gene interactions. Meth- ones for disease gene discovery. Linkage analyses loca- odologies involving linkage analysis and/or association lize disease linked markers onto chromosomal regions studies have been extensively exploited to identify the that may correspond up to 30 Mb harboring several underlying genetic factors. Recent, advances using gen- hundred genes [2]. Ideally, the information obtained ome wide association (GWA) studies undertaken in through large number of genome-wide linkage studies large sample sets have provided a few susceptibility has to be utilized to search T2D genes using a positional genes [1]. The success of GWA studies relies on the dis- candidate based association studies. But, it is cost covery of common variants of common diseases, how- expensive and labor intensive apart from being time ever rare variants may also influence the risk of type 2 consuming to screen for each gene in the T2D linked region. Therefore, it is necessary to prioritize positional * Correspondence: [email protected] genes located in the linked chromosomal regions that † Contributed equally would facilitate and expedite the identification of disease 1Functional Genomics Unit, Institute of Genomics and Integrative Biology, genes. Previously, a study used meta-analysis approach CSIR, Delhi, India © 2010 Sharma et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Sharma et al. BMC Genomics 2010, 11:84 Page 2 of 10 http://www.biomedcentral.com/1471-2164/11/84 to prioritize disease genes from T2D and obesity linked positional candidates were first ranked by formulating a regions based on consensus gene prioritization methods scoring method that assigned Weight value (Wv) to [3]. However, consensus methods are not always appro- each candidate. Further, we exploited network measure priate as they are primarily based on keyword similarity to prioritize HWEs (candidates with a Wv > 0.5) or phenotypes like biomedical text searches and asso- referred to as HRC method (high weight elements ciated pathological conditions for prediction [4]. ranked by clustering coefficient). Recently, protein-protein interaction data have also been Comparison with other known prioritization methods extensively exploited for candidate gene prioritization in which are based on sequence analysis as well as protein- monogenetic as well as complex diseases [4-6]. protein interactions showed that our method had a gene Previously, we demonstrated that T2D may result enrichment ratio of 6.8 which is better than the other from disturbed protein-protein interactions resulting methods compared. We also predicted the functional through disrupted modular domain interactions [7]. processes brought about by positional candidates with From this lead, here we have developed systems biology special emphasis on those with unknown functions. This approach based on domain-domain interactions to was done by constructing network motifs from the prioritize positional candidates located in T2D linked domain interaction network. These motifs represent spe- regions (Fig.1). The method assumes that the functional cific biological functions and hence provide an insight relationship of protein-protein interactions is primarily regarding the physiological role of particular candidate. facilitated by domains of the interacting proteins [8]. We believe such an approach which amalgamates func- Information regarding the functional, positional and net- tional, genetic and network properties could prove to be work properties of disease related genes are utilized by immensely helpful in ranking the positional candidates this method. Network measures like node connectivity involved in disease. and clustering coefficient values of molecular interaction networks have been extensively used to study the topo- Results logical properties of disease genes [9,10]. Recently, it has Here, we integrated Wv method and domain interaction been shown that disease genes avoid ‘dense clustering network property (HRC method) to predict positional neighborhoods’ and have an average clustering coeffi- candidates with a high likelihood to be involved in dis- cient of 0.015 [9]. Based on these properties, the ease etiology. The calculated Wv for proteins ranged Figure 1 Elucidation of the components used for the ranking the positional candidates in T2D. Sharma et al. BMC Genomics 2010, 11:84 Page 3 of 10 http://www.biomedcentral.com/1471-2164/11/84 from 0-1 with the magnitude indicating relevance to coordinately downregulated in T2D, Q6MZN5 might T2D. The candidates with Wv > 0.5 were prioritized as attain significance [14]. high weight elements (HWEs). From the dataset of 4052 Performance of the methods proteins, encoded by genes located within 10 Mb region The benchmarking dataset used to evaluate the perfor- encompassing 108 markers linked with T2D (LOD score mance of our methods, comprised of 19 genes known to ≥ 2), 995 candidates were classified as HWEs. be associated with T2D from GWA studies [1,15] and Weassumedthatmostlikelydiseasegenesamong 353 non-T2D genes lying within 10 Mb regions of 12 HWEs should also share the network property. A recent chromosomal positions those are never shown to be study of network properties of disease genes has linked with any disease, as control data set. We demonstrated that the disease genes avoid dense clus- observed sensitivity of Wv method to be 0.73 and speci- tering neighborhood compared to essential genes and ficity to be 0.96. For HRC method, sensitivity and speci- have an average clustering coefficient of 0.015 [9]. ficity were 0.32 and 0.98 respectively. Overall data Hence we used average Ci of 0.015 as a cut-off to prior- enrichment of 9 fold and 6.8 fold was observed for the itize HWEs. Ranking of HWEs based on their clustering two methods respectively. Using the benchmarking data- coefficient (Ci < 0.015) in the domain interaction net- set, we compared both Wv and HRC predictions with work (HRC method) resulted in 435 most likely disease other known methods of candidate gene prioritization candidates termed as HWEc (Additional file 1). The (Table 2). The receiver operating characteristic (ROC) topological features of the interactions derived here analysis of the two methods, Wv and HRC confirms from domain interactions include an average degree of their better performance (Fig. 2), with only PROSPECTR connectivity (k) of 6.5, an average clustering coefficient [16] having a higher sensitivity (0.90) than Wv and (Ci) of 0.17 and the shortest path length of 4.5. Among HRC. Where, G2D method gave accuracy and sensitivity the HWEs, 44% had Ci < 0.015 (HWEc) whereas among of 0.73 and 0.53 respectively with the benchmarking
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