Download I-4 Statistical genetics, disease biology, and drug discovery

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Whole genome sequencing wikipedia, lookup

Genetic engineering wikipedia, lookup

Human Genome Project wikipedia, lookup

History of biology wikipedia, lookup

Neurogenomics wikipedia, lookup

Biology wikipedia, lookup

Gene expression profiling wikipedia, lookup

Quantitative trait locus wikipedia, lookup

Minimal genome wikipedia, lookup

Genetics wikipedia, lookup

Symbiogenesis wikipedia, lookup

Genome evolution wikipedia, lookup

Evolutionary developmental biology wikipedia, lookup

Race and health wikipedia, lookup

Nutriepigenomics wikipedia, lookup

Designer baby wikipedia, lookup

Introduction to genetics wikipedia, lookup

Personalized medicine wikipedia, lookup

Neurogenetics wikipedia, lookup

Statistical genetics, disease biology, and drug discovery
Yukinori Okada
Department of Statistical Genetics,
Osaka University Graduate School of Medicine
Statistical genetics is a research field that evaluates causality of human genetic variations on
diseases, using statistical and bioinformatics approaches. Recent development of high-throughput
genome sequencing and genotyping technologies, such as whole genome sequencing by next
generation sequencers, have provided human disease genome data of hundreds of thousands of the
subjects. Large scale disease risk association analyses successfully identified comprehensive
catalogues of genetic susceptible loci that are linked to human diseases. However, little is known
regarding how to develop methodology to integrate large-scale human genetic study results with
diverse biological resources, to which statistical genetics should contribute. We have developed
such methods and applied to a pioneering example of large-scale genetic association studies on a
variety of human complex traits. In the case of rheumatoid arthritis (RA), an autoimmune disease
characterized by inflammation and destruction of joints, our studies highlighted novel features of
RA biology. (i) enrichment of microRNA-target gene networks, (ii) negative correlation of genetic
risk with schizophrenia, (iii) significant risk of both classical HLA genes (HLA-DRB1, HLA-DPB1,
HLA-B) and non-classical HLA genes (HLA-DOA), (iv) specific localizations of RA risk variants in
the promoter regions of regulatory T cells, (v) overlap of risk genes with lymphoma and primary
immunodeficiency, and (vi) epigenetic contribution of regulatory T cells and Th17 cells on disease
biology. Further, we demonstrated that the RA risk genes were significantly enriched in overlap
with the target genes of the drugs currently used for clinical treatment of RA, and that in silico
analysis of the network between the disease risk genes and the drug target genes could identify
candidate of drug repositioning (e.g. CDK4/6 inhibitors). These results should empirically show the
value of statistical genetics to dissect disease biology and novel drug discovery.