Laboratory for Statistical Analysist

Establishing links between clinical data and genomic data for individual genetic variations in order to develop computer programs that can predict disease susceptibility and treatment outcomes

Scientists can now determine more than 50,000 pieces of genetic information from a single person. Eventually, it will be possible to determine the full genomic sequence of 3 billion base pairs for any individual, which will be useful for medical care and maintaining health. This genetic information can be stored in a database, but merely knowing the sequences has little practical relevance. Molecular biology, statistical and informatics methods must be used to decipher the meaning of the sequences.

The Laboratory for Statistical Analysis uses statistical and informatics approaches to create computer programs to investigate the association between disease, drug actions, side effects and genetic variations. We also develop computer programs that analyze genetic polymorphisms in individuals and predict drug actions and side effects. The programs are used to analyze actual clinical data.

Our research involves a high level of technical integration and expertise in genetics, statistics, computational science and computer technologies. Ultrafast computing capabilities are necessary to search for clear associations and generate predictions quickly and reliably, and we will soon be able to analyze our problems on a 10-petaflop supercomputer (now under development at RIKEN), which can perform 10¹⁶ floating-point operations in one second. With such expertise and tools, our research will be able to facilitate accurate predictions of a patient’s susceptibility to disease and response to drug treatment (including potential for side effects), resulting in more effective prevention of diseases and improved medical treatments with better results and fewer side effects.

Research Subjects

• (1)   Development of new methods of statistical analysis for the association between polymorphisms and phenotypes using large-scale SNP information.
• (2)   Development of algorithms and computer programs for predicting the outcomes of drug treatments using polymorphisms.
• (3)   Development of databases for predicting the outcomes of drug treatments using polymorphisms.
• (4)   Development of the methods for the study designs by the multi-stage association studies using genetic polymorphisms.
• (5)   The study of the meta-analysis for association studies using polymorphisms.

List of Selected Publications

• (1)   Yamaguchi-Kabata Y, Nakazono K, Takahashi A, Saito S, Hosono N, Kubo M, Nakamura Y, Kamatani N.
  Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups: effects on population-based association studies.
  Am J Hum Genet. 2008 Oct;83(4):445-56.
• (2)   Suzuki A, Yamada R, Kochi Y, Sawada T, Okada Y, Matsuda K, Kamatani Y, Mori M, Shimane K, Hirabayashi Y, Takahashi A, Tsunoda T, Miyatake A, Kubo M, Kamatani N, Nakamura Y, Yamamoto K.
  Functional SNPs in CD244 increase the risk of rheumatoid arthritis in a Japanese population.
  Nat Genet. 2008 Oct;40(10):1224-9.
• (3)   Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, Hirota Y, Mori H, Jonsson A, Sato Y, Yamagata K, Hinokio Y, Wang HY, Tanahashi T, Nakamura N, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y, Seino Y, Maegawa H, Kashiwagi A, Takeda J, Maeda E, Shin HD, Cho YM, Park KS, Lee HK, Ng MC, Ma RC, So WY, Chan JC, Lyssenko V, Tuomi T, Nilsson P, Groop L, Kamatani N, Sekine A, Nakamura Y, Yamamoto K, Yoshida T, Tokunaga K, Itakura M, Makino H, Nanjo K, Kadowaki T, Kasuga M.
  Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus.
  Nat Genet. 2008 Sep;40(9):1092-7.
• (4)   Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen G, Ng DP, Holmkvist J, Borch-Johnsen K, Jørgensen T, Sandbaek A, Lauritzen T, Hansen T, Nurbaya S, Tsunoda T, Kubo M, Babazono T, Hirose H, Hayashi M, Iwamoto Y, Kashiwagi A, Kaku K, Kawamori R, Tai ES, Pedersen O, Kamatani N, Kadowaki T, Kikkawa R, Nakamura Y, Maeda S.
  SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations.
  Nat Genet. 2008 Sep;40(9):1098-102.
• (5)   Misawa K, Fujii S, Yamazaki T, Takahashi A, Takasaki J, Yanagisawa M, Ohnishi Y, Nakamura Y, Kamatani N.
  New correction algorithms for multiple comparisons in case-control multilocus association studies based on haplotypes and diplotype configurations.
  J Hum Genet. 2008;53(9):789-801.
• (6)   Nakamura T, Shi D, Tzetis M, Rodriguez-Lopez J, Miyamoto Y, Tsezou A, Gonzalez A, Jiang Q, Kamatani N, Loughlin J, Ikegawa S.
  Meta-analysis of association between the ASPN D-repeat and osteoarthritis.
  Hum Mol Genet. 2007 Jul 15;16(14):1676-81.
• (7)   Takitoh S, Fujii S, Mase Y, Takasaki J, Yamazaki T, Ohnishi Y, Yanagisawa M, Nakamura Y, Kamatani N.
  Accurate automated clustering of two-dimensional data for single-nucleotide polymorphism genotyping by a combination of clustering methods: evaluation by large-scale real data.
  Bioinformatics. 2007 Feb 15;23(4):408-13.