We propose a new method for the evaluation of environmental water by using DNA microarray technology. Twenty-one types of environmental waters were sampled from incineration processes, some factories, and various soils. We performed a comprehensive analysis by DNA microarray, and attempted to classify the environmental water samples by hierarchical cluster analysis. These water samples were classified into clusters from A to F together with chemicals and physical stress conditions. The water sample that grouped into cluster A caused protein denaturation and oxidative stress, but not mutagenesis. The water samples that grouped into cluster B caused protein denaturation. The water samples that grouped into cluster C caused mutagenesis, protein denaturation, and cell wall damage. The water samples that grouped into cluster D caused mutagenesis. The water samples that grouped into cluster E caused protein denaturation and oxidative stress. The water samples that grouped into cluster F caused oxidative stress. Consequently, the potential influences of environmental water could be estimated by hierarchical cluster analysis.

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KiBank is a database of inhibition constant (Ki) values with 3D structures of target proteins and chemicals. It is accessible at http://kibank.iis.u-tokyo.ac.jp/. The contents of target proteins were nuclear and membrane receptors, enzymes, and, now, newly include transporters, ion channels and ion pumps. The numbers of data were updated to 16,084 Ki values, 101 target proteins (310 subtypes), and 9,279 chemicals in April 2006. Target proteins and the numbers of Ki values for corresponding chemicals were listed. Examples are shown for applications of KiBank for drug development.

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A subset of single nucleotide polymorphisms (SNPs) can be used to capture the majority of the information of genotype-phenotype association studies. The primary issue that we should discuss in SNPs analysis is how one can select a particular subset of SNPs while maximizing the power of detecting a significant association. From this point of view, we reviewed some statistical tools for SNPs analysis by focusing in this paper on the Hardy-Weinberg equilibrium, case-control association study, and haplotype estimation and association. We also paid special attention to the issue of how we can determine the required sample size by using the sequential test. With regard to the statistical tools for SNPs analysis, we reviewed the basic concepts of each method and its applications.

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