Similarity Measures for Clustering SNP and Epidemiological Data

The issue of suitable similarity measures for a joint consideration of so called SNP data and epidemiological variables arises from the GENICA (Interdisciplinary Study Group on Gene Environment Interaction and Breast Cancer in Germany) casecontrol study of sporadic breast cancer. The GENICA study aims to investigate the influence and interaction of single nucleotide polymorphic (SNP) loci and exogenous risk factors. A single nucleotide polymorphism is a point mutation that is present in at least 1 % of a population. SNPs are the most common form of human genetic variations. In particular, we consider 43 SNP loci in genes involved in the metabolism of hormones, xenobiotics and drugs as well as in the repair of DNA. Assuming that these single nucleotide changes may lead, for instance, to altered enzymes or to a reduced or enhanced amount of the original enzymes – with each alteration alone having minor effects – the aim is to detect combinations of SNPs that under certain environmental conditions increase the risk of sporadic breast cancer. The search for patterns in the present data set may be performed by a variety of clustering and classification approaches. I consider here the problem of suitable measures of proximity of two variables or subjects as an indispensable basis for a further cluster analysis. In the present data situation these measures have to be able to handle different numbers and meaning of categories of nominal scaled data as well as data of different scales. Generally, clustering approaches are a useful tool to detect structures and to generate hypothesis about potential relationships in complex data situations. Searching for patterns in the data there are two possible objectives: the identification of groups of similar objects or subjects or the identification of groups of similar variables within the whole or within subpopulations. The different objectives imply different requirements on the measures of similarity. Comparing the individual genetic profiles as well as comparing the genetic information across subpopulations I discuss possible choices of similarity measures suitable for genetic and epidemiological data, in particular, measures based on the ÷2-statistic, Flexible Matching Coefficients and combinations of similarity measures. --GENICA,single nucleotide polymorphism (SNP),sporadic breast cancer,similarity,cluster analysis,Flexible Matching Coefficient

Similarity Measures for Clustering SNP Data

The issue of suitable similarity measures for a particular kind of genetic data – so called SNP data – arises from the GENICA (Interdisciplinary Study Group on Gene Environment Interaction and Breast Cancer in Germany) case-control study of sporadic breast cancer. The GENICA study aims to investigate the influence and interaction of single nucleotide polymorphic (SNP) loci and exogenous risk factors. A single nucleotide polymorphism is a point mutation that is present in at least 1 % of a population. SNPs are the most common form of human genetic variations. In particular, we consider 65 SNP loci and 2 insertions of longer sequences in genes involved in the metabolism of hormones, xenobiotics and drugs as well as in the repair of DNA and signal transduction. Assuming that these single nucleotide changes may lead, for instance, to altered enzymes or to a reduced or enhanced amount of the original enzymes – with each alteration alone having minor effects – we aim to detect combinations of SNPs that under certain environmental conditions increase the risk of sporadic breast cancer. The search for patterns in the present data set may be performed by a variety of clustering and classification approaches. We consider here the problem of suitable measures of proximity of two variables or subjects as an indispensable basis for a further cluster analysis. Generally, clustering approaches are a useful tool to detect structures and to generate hypothesis about potential relationships in complex data situations. Searching for patterns in the data there are two possible objectives: the identification of groups of similar objects or subjects or the identification of groups of similar variables within the whole or within subpopulations. Comparing the individual genetic profiles as well as comparing the genetic information across subpopulations we discuss possible choices of similarity measures, in particular similarity measures based on the counts of matches and mismatches. New matching coefficients are introduced with a more flexible weighting scheme to account for the general problem of the comparison of SNP data: The large proportion of homozygous reference sequences relative to the homo- and heterozygous SNPs is masking the accordances and differences of interest. --GENICA,single nucleotide polymorphism (SNP),sporadic breast cancer,similarity,Matching Coefficient,Flexible Matching Coefficient

Comparison of the toxicokinetics of daidzein and bisphenol A in pregnant and non-pregnant DA/Han rats

Potentially adverse human and environmental effects due to hormone mimicry of environmental estrogens are a matter of current concern. Environmental estrogens belong to the socalled endocrine active compounds (EAC) and may alter signalling processes of the endocrine system leading to a broad range of effects during fetal and postnatal development, puberty, adulthood, and aging. A number of synthetic chemicals as well as several plant-derived compounds, socalled phytoestrogens, are known to have weak estrogenic activity. The present study is part of the risk assessment of the weak environmental estrogens daidzein and bisphenol A. The isoflavone daidzein is an important phytoestrogen with respect to dietary exposure (soy beans and soy products) whereas bisphenol A is an industrial chemical that occurs at much lower concentrations as a contaminant in food. The toxicokinetics of these compounds in female pregnant and non-pregnant DA/Han rats after single intravenous application were compared by the use of the Mann-Whitney- U-statistic. --Bisphenol A,daidzein,xenoestrogens,phytoestrogens,endocrine active compounds,Mann-Whitney-U-test

Cluster Analysis : A Comparison of Different Similarity Measures for SNP Data

The issue of suitable similarity measures for a particular kind of genetic data - so called SNP data - arises, e.g., from the GENICA (The Interdisciplinary Study Group on Gene Environmental Interactions and Breast Cancer in Germany) case-control study of sporadic breast cancer. The GENICA study aims to investigate the influence and interaction of single nucleotide polymorphic (SNP) loci and exogenous risk factors. It is very unlikely that there exists one main effect, say only one polymorphism, being responsible for such a complex disease as sporadic breast cancer as the role of a single gene within the carcinogenic process is limited (Garte, 2001). Nevertheless, it is assumed that a number of interacting SNPs in combination with certain environmental risk factors increase the individual susceptibility. The search for SNP patterns in the present data set may be performed by a variety of clustering and classification approaches. Here we consider the problem of adequate similarity measures for variables or subjects as an indispensable basis for a further cluster analysis. The term ?similarity? is still vague for SNP data. A main problem arises by the general structure of such data sets: the proportion of hetero- or homozygous SNPs is rather small compared with the homozygous reference sequence. Thus, the relevant information of combinations of genetic alterations is often masked by a huge amount of common occurrences of homozygous reference types. Therefore, we examine different similarity measures, conventional ones as well as new coefficients which we created especially for SNP data. Furthermore, we compare the resulting partitions with each other adapting the clustering of clustering methods of Rand (1971) for different similarity measures. --cluster analysis,clustering clustering methods,GENICA,similarity,single nucleotide polymorphism,sporadic breast cancer

Similarity measures for clustering SNP and epidemiological data

The issue of suitable similarity measures for a joint consideration of so called SNP data and epidemiological variables arises from the GENICA (Interdisciplinary Study Group on Gene Environment Interaction and Breast Cancer in Germany) casecontrol study of sporadic breast cancer. The GENICA study aims to investigate the influence and interaction of single nucleotide polymorphic (SNP) loci and exogenous risk factors. A single nucleotide polymorphism is a point mutation that is present in at least 1 % of a population. SNPs are the most common form of human genetic variations. In particular, we consider 43 SNP loci in genes involved in the metabolism of hormones, xenobiotics and drugs as well as in the repair of DNA. Assuming that these single nucleotide changes may lead, for instance, to altered enzymes or to a reduced or enhanced amount of the original enzymes – with each alteration alone having minor effects – the aim is to detect combinations of SNPs that under certain environmental conditions increase the risk of sporadic breast cancer. The search for patterns in the present data set may be performed by a variety of clustering and classification approaches. I consider here the problem of suitable 2 measures of proximity of two variables or subjects as an indispensable basis for a further cluster analysis. In the present data situation these measures have to be able to handle different numbers and meaning of categories of nominal scaled data as well as data of different scales. Generally, clustering approaches are a useful tool to detect structures and to generate hypothesis about potential relationships in complex data situations. Searching for patterns in the data there are two possible objectives: the identification of groups of similar objects or subjects or the identification of groups of similar variables within the whole or within subpopulations. The different objectives imply different requirements on the measures of similarity. Comparing the individual genetic profiles as well as comparing the genetic information across subpopulations I discuss possible choices of similarity measures suitable for genetic and epidemiological data, in particular, measures based on the χ2-statistic, Flexible Matching Coefficients and combinations of similarity measures

Estimation of toxicokinetic population parameters in a four-stage hierarchical model

A basic part in the risk assessment of potential carcinogens is the determination of toxicokinetic parameters. The partition of the xenobiotic in the body of experimental animals is a first step of the biochemical pathway of the formation of DNA adducts which might lead to the development of cancer. Fundamental in the extrapolation from one species to another is the characterisation of processes by means of population parameters. Nevertheless, the consideration of individual parameters varying between repeated experiments and different doses is of great importance to obtain a more precise insight into the variability structure of the process so that a valid basis for further research is the final result. Two nonlinear four-stage hierarchical models for a repeated measurement design and for repeated exposures to different doses are presented. The estimation of the individual and population parameters as well as of the covariance matrices is performed by an EM algorith