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BackgroundTo implement personalized medicine, we established a large-scale patient cohort, BioBank Japan, in 2003. BioBank Japan contains DNA, serum, and clinical information derived from approximately 200,000 patients with 47 diseases. Serum and clinical information were collected annually until 2012.MethodsWe analyzed clinical information of participants at enrollment, including age, sex, body mass index, hypertension, and smoking and drinking status, across 47 diseases, and compared the results with the Japanese database on Patient Survey and National Health and Nutrition Survey. We conducted multivariate logistic regression analysis, adjusting for sex and age, to assess the association between family history and disease development.ResultsDistribution of age at enrollment reflected the typical age of disease onset. Analysis of the clinical information revealed strong associations between smoking and chronic obstructive pulmonary disease, drinking and esophageal cancer, high body mass index and metabolic disease, and hypertension and cardiovascular disease. Logistic regression analysis showed that individuals with a family history of keloid exhibited a higher odds ratio than those without a family history, highlighting the strong impact of host genetic factor(s) on disease onset.ConclusionsCross-sectional analysis of the clinical information of participants at enrollment revealed characteristics of the present cohort. Analysis of family history revealed the impact of host genetic factors on each disease. BioBank Japan, by publicly distributing DNA, serum, and clinical information, could be a fundamental infrastructure for the implementation of personalized medicine.

Warfarin is a commonly used anticoagulant, whose dose needs to be determined for each individual patient owing to large inter-individual variability in its therapeutic dose. Although several clinical and genetic variables influencing warfarin dose have been identified, uncovering additional factors are critically important for safer use of warfarin. Through a genome-wide association study, we identified single-nucleotide polymorphism (SNP) rs2108622 [cytochrome P450, family 4, subfamily F, polypeptide 2 (CYP4F2)] as a genetic determinant of warfarin responsiveness for Japanese. Stratifying subjects who have been pre-classified according to the genotypes of SNP rs10509680 [cytochrome P450, family 2, subfamily C, polypeptide 9 (CYP2C9)] and SNP rs9923231 [vitamin K epoxide reductase complex subunit 1 (VKORC1)], based on their genotypes of rs2108622 allowed identification of subjects who require higher dose of warfarin. Incorporating genotypes of rs2108622 into a warfarin dosing algorithm that considers age, body surface area, status of amiodarone co-administration and genotypes of SNPs in the CYP2C9 and VKORC1 genes improved the model's predictability to 43.4%. In this study, the association of CYP4F2 with warfarin dose of the Japanese has been established for the first time. Besides, a warfarin dosing algorithm that incorporates genotypes of rs2108622 and amiodarone co-administration status was suggested for the Japanese. Our study also implied that common SNPs other than those in the CYP2C9, VKORC1 and CYP4F2 genes that show strong effect on the therapeutic warfarin dose might not exist.

The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.