Interpretation of personal genome sequencing data in terms of disease ranks based on mutual information. Issue 2 (December 2015)

• Record Type: Journal Article
• Authors: Na, Young-Ji; Sohn, Kyung-Ah; Kim, Ju
• Is Part Of: BMC medical genomics. Volume 8:Issue 2(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Next-generation sequencing -- MeSH tree structure -- disease risk -- personal genome; Medical genetics -- Periodicals; Genomics -- Periodicals; 616.042
• DOI: 10.1186/1755-8794-8-S2-S4
• Languages: English
• ISSNs: 1755-8794

Abstract

Abstract Background The rapid advances in genome sequencing technologies have resulted in an unprecedented number of genome variations being discovered in humans. However, there has been very limited coverage of interpretation of the personal genome sequencing data in terms of diseases. Methods In this paper we present the first computational analysis scheme for interpreting personal genome data by simultaneously considering the functional impact of damaging variants and curated disease-gene association data. This method is based on mutual information as a measure of the relative closeness between the personal genome and diseases. We hypothesize that a higher mutual information score implies that the personal genome is more susceptible to a particular disease than other diseases. Results The method was applied to the sequencing data of 50 acute myeloid leukemia (AML) patients in The Cancer Genome Atlas. The utility of associations between a disease and the personal genome was explored using data of healthy (control) people obtained from the 1000 Genomes Project. The ranks of the disease terms in the AML patient group were compared with those in the healthy control group using "Leukemia, Myeloid, Acute" (C04.557.337.539.550) as the corresponding MeSH disease term. The mutual information rank of the disease term was substantially higher in the AML patient group than in the healthy control group, which demonstrates that the proposed methodology can be successfully applied to infer associations between the personal genome and diseases. Conclusions Overall, the area under the receiver operating characteristics curve was significantly larger for the AML patient data than for the healthy controls. This methodology could contribute to consequential discoveries and explanations for mining personal genome sequencing data in terms of diseases, and have versatility with respect to genomic-based knowledge such as drug-gene and environmental-factor-gene interactions.