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Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 &plusmn, 0.022&ndash, 0.734 &plusmn, 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP++), and further improved the ethnicity-specific validations (0.759 &plusmn, 0.066&ndash, 0.791 &plusmn, 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Despite substantial premarket efforts, a significant portion of approved drugs has been withdrawn from the market for safety reasons. The deleterious impact of nonsynonymous substitutions predicted by the SIFT algorithm on structure and function of drug-related proteins was evaluated for 2504 personal genomes. Both withdrawn (n = 154) and precautionary (Beers criteria (n = 90), and US FDA pharmacogenomic biomarkers (n = 96)) drugs showed significantly lower genomic deleteriousness scores (P < 0.001) compared to others (n = 752). Furthermore, the rates of drug withdrawals and precautions correlated significantly with the deleteriousness scores of the drugs (P < 0.01); this trend was confirmed for all drugs included in the withdrawal and precaution lists by the United Nations, European Medicines Agency, DrugBank, Beers criteria, and US FDA. Our findings suggest that the person-to-person genome sequence variability is a strong independent predictor of drug withdrawals and precautions. We propose novel measures of drug safety based on personal genome sequence analysis.

Objective The increasing use of health self-tracking devices is making the integration of heterogeneous data and shared decision-making more challenging. Computational analysis of lifelog data has been hampered by the lack of semantic and syntactic consistency among lifelog terms and related ontologies. Medical lifelog ontology (MELLO) was developed by identifying lifelog concepts and relationships between concepts, and it provides clear definitions by following ontology development methods. MELLO aims to support the classification and semantic mapping of lifelog data from diverse health self-tracking devices. Methods MELLO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of lifelog data, (2) identifying lifelog concepts, (3) assigning relationships among MELLO concepts, (4) developing MELLO properties (e.g., synonyms, preferred terms, and definitions) for each MELLO concept, and (5) evaluating representative layers of the ontology content. An evaluation was performed by classifying 11 devices into 3 classes by subjects, and performing pairwise comparisons of lifelog terms among 5 devices in each class as measured using the Jaccard similarity index. Results MELLO represents a comprehensive knowledge base of 1998 lifelog concepts, with 4996 synonyms for 1211 (61%) concepts and 1395 definitions for 926 (46%) concepts. The MELLO Browser and MELLO Mapper provide convenient access and annotating non-standard proprietary terms with MELLO (http://mello.snubi.org/). MELLO covers 88.1% of lifelog terms from 11 health self-tracking devices and uses simple string matching to match semantically similar terms provided by various devices that are not yet integrated. The results from the comparisons of Jaccard similarities between simple string matching and MELLO matching revealed increases of 2.5, 2.2, and 5.7 folds for physical activity,body measure, and sleep classes, respectively. Conclusions MELLO is the first ontology for representing health-related lifelog data with rich contents including definitions, synonyms, and semantic relationships. MELLO fills the semantic gap between heterogeneous lifelog terms that are generated by diverse health self-tracking devices. The unified representation of lifelog terms facilitated by MELLO can help describe an individual's lifestyle and environmental factors, which can be included with user-generated data for clinical research and thereby enhance data integration and sharing.