Browsing by Author "Wessman, Jaana"

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  • Wessman, Jaana (Helsingin yliopisto, 2012)
    The topic of this thesis is the analysis of complex diseases, and specifically the use of certain clustering methods to do it. We concern ourselves mostly with the modeling of complex phenotypes of diseases: the symptoms and signs of diseases, and the other multiple cophenotypes that go with them. The two related questions we seek answers for are: 1) how can we use these clustering methods to summarize the complex, multivariate phenotype data, for example to be used as a simple phenotype in genetic analyses and 2) how can we use these clustering methods to find subgroups of sufferers of a particular disease, such that might share the same causal factors of the disease. Current methods for studies on medical genetics ideally call for a single or at most handful of univariate phenotypes to be compared to genetic markers. Multidimensional phenotypes cannot be handled by the standard methods, and treating each variable as independent and testing one hundred phenotypes with unclear true dependency structure against thousands of markers results into problems with both running times and multiple testing correction. In this work, clustering is utilized to summarize a multi-dimensional phenotype into something that can then be used in association studies of both genetic and other type of potential causes. I describe a clustering process and some clustering methods used in this work, with comments on practical issues and references to the relevant literature. After some experiments on artificial data to gain insight to the properties of these methods, I present four case-studies on real data, highlighting both ways to succesfully use these methods and problems that can arise in the process.