Whole-genome and -exome resequencing using next-generation sequencers is a powerful approach for identifying genomic variations that are associated with diseases. However, systematic strategies for prioritizing causative variants from many candidates to explain the disease phenotype are still far from being established, because the population-specific frequency spectrum of genetic variation has not been characterized. Here, we have collected exomic genetic variation from 1208 Japanese individuals through a collaborative effort, and aggregated the data into a prevailing catalog. In total, we identified 156 622 previously unreported variants. The allele frequencies for the majority (88.8%) were lower than 0.5% in allele frequency and predicted to be functionally deleterious. In addition, we have constructed a Japanese-specific major allele reference genome by which the number of unique mapping of the short reads in our data has increased 0.045% on average. Our results illustrate the importance of constructing an ethnicity-specific reference genome for identifying rare variants. All the collected data were centralized to a newly developed database to serve as useful resources for exploring pathogenic variations. Public access to the database is available at http://www.genome.med.kyoto-u.ac.jp/SnpDB/.

Human immune systems are very complex, and the basis for individual differences in immune phenotypes is largely unclear. One reason is that the phenotype of the immune system is so complex that it is very difficult to describe its features and quantify differences between samples. To identify the genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination without having to rely on biological assumptions, we performed a genome-wide association study (GWAS), using cytometry data. Here, we applied computational analysis to the cytometry data of 301 people before receiving an influenza vaccine, and 1, 7, and 90 days after the vaccination to extract the feature statistics of the lymphocyte profiles in a nonparametric and data-driven manner. We analyzed two types of cytometry data: measurements of six markers for B cell classification and seven markers for T cell classification. The coordinate values calculated by this method can be treated as feature statistics of the lymphocyte profile. Next, we examined the genetic basis of individual differences in human immune phenotypes with a GWAS for the feature statistics, and we newly identified seven significant and 36 suggestive single-nucleotide polymorphisms associated with the individual differences in lymphocyte profiles and their change after vaccination. This study provides a new workflow for performing combined analyses of cytometry data and other types of genomics data.