Basic Medicine, Bioinformatics

Statistical Genomics and Genetics

To Open New Avenues for Sustainable Medicine and Future of Humanity: Theoretical and Statistical Genetics for Genomic and Personalized Medicine

Medical Sciences Course

  • Master / Doctoral Degree

Faculty

TAMIYA, GenTAMIYA, Gen
TAMIYA, Gen

Professor, Ph.D.

*Concurrent Position

Research Theme

  • Revealing disease etiologies by analyzing genomic big data using theoretical and statistical genetics
  • Achieving disease risk prediction using risk factors
  • Addressing problems in future of humanity by exploiting genetic properties of human populations
Research Keywords:

statistical genetics, population genetics, quantitative genetics, genomic medicine, genomic cohort

Technical Keywords:

neutral theory, big data in genomics, gene-by-environment interaction, big data with high dimension and low sample size, large p small n problem

Laboratory Introduction

Human common complex diseases, such as diabetes, hypertension and mental illness, can be contributed by multiple etiologies involving environmental and/or genetic factors. As far, traditional epidemiological researches revealed a lot of environmental risk factors, whereas genomic researches identified risk genes for such diseases. However, these risk factors could explain only small fraction of any complex disease prevalence. This is probably due to underpowered research designs, limited genomic data and/or poor genetic theories so on. Recently, to overcome this situation, both research communities together make efforts to decipher comprehensively complex disease etiologies including genes, environments and their interactions by so-called national-wide genomic cohort study. Although this design seems excellent and promising, there are some radical problems; one is the lack of appropriate models for the genetic architectures of common complex diseases, and the other is that we have no effective statistical methods for such big data with high dimension/small sample size. The former is often called “missing heritability problem” in the field of genetics and the later “p>>n problem” in that of statistics. Our department aims to resolve such radical problems using the most advanced statistical genetics based on traditional population/quantitative genetic theories. Our studies will open the way for sustainable genomic/personalized medicine as well as the future of humanity.

Figure 1. Genomic/Personalized medicine

Figure 1. Genomic/Personalized medicine

Recent Publications

  • Tamiya G, et al. A mutation of COX6A1 causes a recessive axonal or mixed form of Charcot-Marie-Tooth disease. Am J Hum Genet. 95(3):294-300, 2014
  • Yamagata University Genomic Cohort Consortium. Pleiotropic Effect of Common Variants at ABO Glycosyltranferase Locus in 9q32 on Plasma Levels of Pancreatic Lipase and Angiotensin Converting Enzyme. PLoS ONE 9(2):e55903, 2014
  • Ueki M and Tamiya G. Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis. BMC Bioinformatics 13(1):72, 2012
  • Tamiya G. Transcriptional dysregulation: a cause of dystonia? Lancet Neurol. 8(5):416-8, 2009
  • Makino S, et al. Reduced neuron-specific expression of the TAF1 gene is associated with X-linked dystonia-parkinsonism. Am J Hum Genet. 80(3):393-406, 2007