Empirical bayes methods in statistical genetics

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The goal of this program is the development of statistical tools in the field of statistical genetics. With the recent development of molecular biotechnologies and instruments, genetic and genomic data can now be generated from any organism almost with no limits. Statistical genetics, aimed to provide effective and efficient analyses and utilization of these data, has thus become a very active and promising area of statistics. The overall objective of my research is to develop a collection of theoretical models and statistical methods for genetic and genomic research in the context of microarray data and pedigree data. Microarray technology is one of the most recent and important experimental breakthroughs in molecular biology, allowing the measurement and comparison of expression level of thousands of genes simultaneously. I am interested in the development of methods leading to the detection of differentially expressed genes between one or more cell conditions. The applications of such methods are very wide, and can be used in the field of cancer research or drug development, for example. The second type of genetic data I consider comes from gene mapping studies involving large families. Co-inheritance patterns between the affection status of a disease and genetic markers are studied through linkage analysis. I am interested in modeling the dependence framework between individuals of such studies.