| Autor: |
Piriyapongsa Jittima, Ngamphiw Chumpol, Intarapanich Apichart, Kulawonganunchai Supasak, Assawamakin Anunchai, Bootchai Chaiwat, Shaw Philip J, Tongsima Sissades |
| Jazyk: |
angličtina |
| Rok vydání: |
2012 |
| Předmět: |
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| Zdroj: |
BMC Genomics, Vol 13, Iss Suppl 7, p S2 (2012) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2164 |
| DOI: |
10.1186/1471-2164-13-S7-S2 |
| Popis: |
Abstract Background Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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