The principal research theme in our laboratory is exploring the genetic basis of multifactorial diseases such as hypertension, diabetes, dyslipidemia, and vascular complications. Under the paradigm of personalized medicine, we aim to utilize genomic information for disease prevention, diagnosis, and prognosis estimation, and further, develop efficient therapeutic methods on the basis of the novel insights into the genetic architecture of cardiovascular disease. In keeping with such goals, we are using a combination of approaches, involving (1) disease-susceptibility gene mapping and functional genomics, (2) pharmacogenomics, and (3) quantitative trait genetics.
Disease-susceptibility gene mapping and functional genomics
Multifactorial diseases can be as complex as the name suggests. Lifestyle, environmental factors (e.g., diet), and genetic factors contribute to disease onset and development. In humans, many disorders show multifactorial inheritance patterns, such as hypertension and diabetes, and all of them are due to a complex interaction of genetic and environmental factors. Thus far, a number of methods have been developed to study such complex disorders in genetic epidemiology in a non-hypothesis-driven manner, one of the more promising methods being the use of genome-wide association studies (GWAS), which enables the identification of common genetic factors that influence quantitative traits (such as blood pressure and blood glucose) or the condition of major complex disorders (such as hypertension and diabetes). Given the modest effect sizes of individual susceptibility genes, complete genomic information on genetic variation, e.g., 500,000 or more single nucleotide polymorphisms (SNPs), combined with clinical and other phenotype data, offers great potential for increased understanding of the basic biological processes underlying multifactorial diseases. Genetic variations showing statistically significant evidence of association in GWAS are considered as “positional markers”; the disease-causing problem is likely to be located near these regions. We are conducting GWAS for diabetes, hypertension, and coronary artery disease in collaboration with several universities/medical institutions in Japan and other countries.
With the recent progress in GWAS, functional genomics has become an important tool for investigating the overall genetic architecture in the case of cardiovascular disease and its risk factors. Functional genomics is a field of molecular biology that provides knowledge of gene functions and interactions, with particular focus on dynamic aspects such as gene transcription and translation, thereby enabling us to understand the relationship between an individual’s genotype and phenotype. Functional genomics studies in our laboratory include those for natural variation in genes (epistasis) and RNA (transcriptome profiling) as well as functional disruptions affecting genes (e.g., gene-knockout or transgenic animals), and these generally involve high-throughput methods, such as microarrays, rather than more traditional gene-by-gene approaches, and bioinformatics.
Pharmacogenomics is sometimes analogously referred to as personalized medicine because recommendations on drug treatment can be made based on an individual’s genetic background. The field of pharmacogenomics is promising, and in future, it will have an expanding role in the practice of medicine. In pharmacogenomics, gene expression or variation (e.g., SNPs) is correlated with a drug’s efficacy or toxicity. It is assumed that interindividual differences in responding to a medication are determined by not just one gene but rather many genes that interact with each other. By definition, pharmacogenetics examines single gene interactions with drugs, whereas pharmacogenomics involves whole genome analysis to identify the genetic factors influencing drug responses. As in the case of disease-susceptibility gene mapping described above, i.e., GWAS, pharmacogenomic methods are increasingly becoming methods of choice for identifying causal genes or causal variants compared to the traditional candidate-gene approach.
Pharmacogenomics is still in the nascent stages. Millions of genetic variations probably exist, and identifying them all could take many years. Nevertheless, some types of pharmacogenomic tests are in use today; for example, the cytochrome P450 (CYP450) genotype test can be used to determine the dosage of anticoagulants (such as warfarin and clopidogrel) and proton pump inhibitors.
To facilitate pharmacogenomic studies in clinical medicine, 2 types of infrastructure are required: infrastructure for clinical genomic epidemiology to monitor individuals’ responses and adverse reactions to certain medications and infrastructure for annotated knowledge of genes that are involved in the pharmacokinetics and pharmacodynamics (PK/PD) of target drugs. To delineate molecules (genes) that play a key role in pharmacogenomics, we are currently constructing a dynamic gene-expression database after administration of a series of cardiovascular drugs (including antihypertensive, antidiabetic, lipid-lowering drugs) to rats.
Quantitative trait genetics
Natural human populations show a remarkable phenotypic variation in terms of disease susceptibility. Phenotypic variation is caused by the underlying genetic complexity resulting from multiple interacting loci, with individual allelic effects being substantially modified by environmental stimuli. Understanding the relationship between genetic variation (e.g., SNPs) and phenotypic variation in quantitative or complex traits can provide insights that are important for predicting disease risk and customizing treatment. A number of studies in model organisms and humans have resulted in the mapping of quantitative trait loci (QTLs) that affect disease phenotypes and complex traits and provided unexpected insights into the biology of multifactorial disease. However, these new loci account for only a small fraction of the total genetic variation in a population and they rarely pinpoint individual annotated genes. Thus, a systems genetics approach for integrating genotype–phenotype relationships at various levels of biological processes, i.e., gene regulation, epistasis, and network, is required to uncover genetic pathways leading to variation in complex traits; this approach, however, is not always feasible in humans.
Therefore, we are conducting quantitative trait genetic studies in the spontaneously hypertensive rat (SHR) and its substrain, stroke-prone SHR (SHRSP); both these models are widely investigated as animal models for lifestyle-related diseases such as essential hypertension. Our ongoing studies include QTL mapping of cardiovascular risk traits (blood pressure, lipids, etc.), transcriptome profiling, and development of novel models for cardiovascular disease.