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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)
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)
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)
Long term-low dose mutation assays offer a means to study the genetic effects of environmental mutagens at concentrations relevant to human exposure. These assays involve continuous induction of mutants, serial dilution of cultures and sampling to determine the mutant fraction as a function of time and mutagen concentration. An arithmetic model for the(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)
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)
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)
Multistage carcinogenesis models describe the evolution of the cells in an individual's organ from a normal stage to a pre-neoplastic stage to a neoplastic stage. The triggers for the passage from one stage to the next one are presumed to be genetic alterations, which are not only governed by purely random events but also by individual environmental and(More)
We argue that robust statistics has multiple goals, which are not always aligned. Robust thinking grew out of data analysis and the realisation that empirical evidence is at times supported merely by one or a few observations. The paper examines the outgrowth from this criticism of the statistical method over the last few decades. 1 A brief history(More)