Large and complex genetic data sets present a great deal of challenges to standard data analysis techniques. In many cases, standard methods are infeasible or even impossible for analyzing such data. This research is to develop statistical and computational methods relevant to the analysis of complex human pedigree data. The first main focus is to solve problems that involve large complex pedigrees, multiple polymorphic markers, incomplete genotypes, and complex disease models. The second main focus is to study further the Chi-square (CHS) recombination models, to develop new techniques to incorporate CHS into methods of gene mapping to achieve greater efficiency of data, and to apply this methodology to study genetic interference in the human genome. Most of the proposed research in this project is to be carried out using the Markov chain Monte Carlo (MCMC) methodology. Exploration of MCMC methods in human pedigree analysis thus far shows that this methodology is highly suitable for estimating probabilities and likelihoods. However, because of special features of models and methods appropriate for modern human genetics, special modifications to the standard MCMC approach are required for this technique to be effective in a variety of genetic mapping problems. This project continues the work of making these modifications and exploring new applications.
The last decade has seen rapid advancements in the field of human genetics. Large and complex data sets are accumulating at an incredible rate. Some disease genes (most of them are for single-gene simple genetic disorders) have been identified and mapped. This includes the genes responsible for cystic fibrosis, Huntington's disease and some breast cancers. This has tremendous implication in genetic counseling, genetic testing and screening, drug discovery, and genetic therapy. Identification of genetic factors for complex diseases is a far more difficult task. Complex diseases may be genetically heterogeneous caused by different susceptibility genes, or may be caused by a combination of genes with possible environmental effects. Many common diseases have complex etiology, and are believed to be at least partially due to genetic predisposition. Common diseases such as diabetes, alcohol dependence, and some forms of cancer are examples of complex disorders. Methods developed in this research can handle complex genetic models and make use of available genetic data fully, thereby increasing the power to map genes for complex diseases.
