دورية أكاديمية
Gene prioritization in Type 2 Diabetes using domain interactions and network analysis
| العنوان: | Gene prioritization in Type 2 Diabetes using domain interactions and network analysis |
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| المؤلفون: | Tandon Nikhil, Tabassum Rubina, Chavali Sreenivas, Sharma Amitabh, Bharadwaj Dwaipayan |
| المصدر: | BMC Genomics, Vol 11, Iss 1, p 84 (2010) |
| بيانات النشر: | BMC, 2010. |
| سنة النشر: | 2010 |
| المجموعة: | LCC:Biotechnology LCC:Genetics |
| مصطلحات موضوعية: | Biotechnology, TP248.13-248.65, Genetics, QH426-470 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2164 |
| العلاقة: | http://www.biomedcentral.com/1471-2164/11/84Test; https://doaj.org/toc/1471-2164Test |
| DOI: | 10.1186/1471-2164-11-84 |
| الوصول الحر: | https://doaj.org/article/dc6ac525730b47759c567e2198ed8eb6Test |
| رقم الانضمام: | edsdoj.6ac525730b47759c567e2198ed8eb6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14712164 |
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| DOI: | 10.1186/1471-2164-11-84 |