Statistical genetics is an academic discipline that studies the causal relationship between genotype and phenotype information using statistics. There is a growing need for a discipline that can interpret omics data in a cross-disciplinary manner and return the data to society. Statistical genetics is a discipline suitable for cross-disciplinary integration of big data in diverse academic field. By integrating information on disease susceptibility genes with diverse biological and medical databases, we can contribute to the elucidation of new disease pathologies, the identification of disease biomarkers, the unraveling disease epidemiology, the discovery of novel genomic drugs. It has become clear that this can contribute to the promotion of personalized medicine. Genome-wide association analysis conducted on 700,000 individuals in an international biobank collaboration has identified susceptibility gene regions in a wide variety of human diseases. Genomic drug discovery methods that directly search for drug targets based on disease susceptibility genes are attracting attention as new directions. The importance of utilizing multilayered omics information has also been pointed out. Single-cell analysis technology observes gene dynamics at the single-cell level, and data analysis technologies such as trajectory estimation, cell-cell interaction, and single-cell eQTL analysis have shown rapid development. How to link the constructed multilayered omics information with human disease genome information derived from biobanks and to implement genome personalized medicine is the key to the future. I would like to introduce our efforts including "Genetic Statistics and Summer School" to foster young scientists.