Abstract
One major task of statistical genetics is the development of efficient methods to identify disease predisposing genes. While genetic mapping and positional cloning of Mendelian disorders can be considered routine nowadays, this systematic approach was successful only for one complex disorder, type II diabetes mellitus. It might hence be questioned whether this route should still be followed for unraveling the genetic background of a disease. One answer to this has been the development of new molecular biological techniques. In this paper, we sketch the recently developed techniques that might be alternatives for the identification of disease susceptibility genes and their function. We illustrate the absolute need for new statistical methods by means of two areas of current research, genomewideassociation analysis and gene expression arrays. Here, developments are required in study design, quality control and statistical data analysis. We conclude that these manifold challenges can only be accomplished in an interdisciplinary team.
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Ziegler, A., Hartmann, O., König, I.R., Schäfer, H. (2003). Statistical Genetics — Present and Future. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_41
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DOI: https://doi.org/10.1007/978-3-642-55721-7_41
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