Stephan Morgenthaler

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A method is described to discover if a gene carries one or more allelic mutations that confer risk for any specified common disease. The method does not depend upon genetic linkage of risk-conferring mutations to high frequency genetic markers such as single nucleotide polymorphisms. Instead, the sums of allelic mutation frequencies in case and control(More)
The relationship between the molecular mechanisms of mutagenesis and the actual processes by which most people get cancer is still poorly understood. One missing link is a physiologically based but quantitative model uniting the processes of mutation, cell growth and turnover. Any useful model must also account for human heterogeneity for inherited traits(More)
Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that(More)
We have extended the algebraic models for cancer initiation and progression developed by Nordling, Armitage-Doll and Knudson-Moolgavkar to include the effect of cell turnover rate in normal tissue, stochastic growth of preneoplastic adenomas, and the general case wherein a subfraction of the population is at risk. We have also gathered the mortality data(More)
Allele-specific mismatch amplification mutation assays (MAMA) of anatomically distinct sectors of the upper bronchial tracts of nine nonsmokers revealed many numerically dispersed clusters of the point mutations C742T, G746T, G747T of the TP53 gene, G35T of the KRAS gene and G508A of the HPRT1 gene. Assays of these five mutations in six smokers have yielded(More)
Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the(More)
We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity(More)