Genomic Medicine’s Milestones and Future

Tatsuhiko Tsunoda
RIKEN Center for Genomic Medicine

Whole-genome approaches have opened new frontiers in medical research. Genome-wide association studies (GWAS) exhaustively explore disease-related genes in the human genome. They also provide information for deciding which types and/or what drugs dosages are adequate for individuals – personalized medicine. In addition, next-generation sequencing (NGS) enables us to apply whole-genome sequencing to our personal genomes with high quality within half a day. These technologies have revolutionized medical research as well as health care. In 2002, our center reported the world’s first GWAS results (Nature Genetics 32: 650-654, 2002). In 2004, using the gene-based data, I constructed the world’s first linkage disequilibrium (LD) map, and found exotic patterns of natural selection on genes (Human Molecular Genetics 13: 1623-1632, 2004). Thereafter, we participated in the International HapMap project to construct an LD map and select tagging SNPs, which have been used for chips/arrays (Nature 449:851-861, 2007). This has resulted in a large increase in the number of GWAS, further accelerated by the BioBank Japan project, revealing many genes related to common diseases, cancers, and drug responses. To extend this coverage, we are now enlarging sample sizes using disease cohorts and performing meta-analysis through collaborations around the world. To explore hidden SNPs with lower allele frequency, as well as to combine data from chips with different marker sets, we are using the imputation technique with reference haplotypes. One promising approach for developing higher quality reference haplotypes and for exploring unknown variation is analyzing lower frequency variations, e.g. SNVs and CNVs, through NGS. To develop our analytical pipeline for NGS data, we sequenced a single genome at high coverage, resulting in the first reported Japanese individual's whole-genome sequence (Nature Genetics 42:931-936, 2010). That work allowed us to establish methodologies for detecting multiple types of variations: single nucleotide variations (SNVs), structural variations including copy number variations (CNVs), and novel sequences. Based on our methodology, we have constructed a pipeline for analyzing cancer genomes (Nature 464:993-998, 2010) as well as whole-exome analysis for common/monogenic diseases. Now, one of the most challenging issues is how to extract and combine variations/mutations for high-accuracy prediction of medical outcomes toward personalized medicine.