Statistical Methods for Gene Mapping Based on a Confidence Set Approach

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Multiple testing is an important but difficult statistical issue in many areas of genetic research. One particular multiple testing problem arises when many markers are screened throughout the genome for their linkage or association with a disease locus, which is the focus of this project, with a broad long-term objective of developing methods applicable in various areas of genetic and genomic research. The main thrust of the proposed approach lies in its formulation of the hypotheses for linkage. Traditionally, hypotheses for linkage are usually set up with the null hypotheses being no linkage and the alternative hypothesis being linkage. In the new formulation, the null and alternative hypotheses are being ``reversed'', with the null hypothesis being tight linkage and the alternative hypothesis being loose linkage or no linkage. Two of the fundamental advantages with this new paradigm are: first, multiplicity adjustment is unnecessary for the number of tests performed in a genome-scan study, and second, the location of a disease gene can be narrowed down to a small genomic region, even at the stage of a preliminary genome-scan study. The first specific aim is to develop methods for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on parametric tests of the hypotheses. Single-marker and multiple-marker approaches will be developed for data from general pedigrees. The second specific aim can be viewed as a non-parametric counterpart of the first aim. Methods will be developed for constructing confidence sets/regions based on non-parametric tests using allele-sharing statistics. A wide variety of allele-sharing statistics and data types, ranging from simple structures (sibships, relative pairs) to general pedigrees, will be considered.With the completion of the Human Genome Project, and the development of high throughput technology for genotyping, it is now a routine matter to search up to thousands of genetic markers distributed throughout the genome to look for disease susceptibility genes. This project aims at developing statistical methods suitable for probing each of these markers without compromising the power of finding nearby susceptibility locus. As the number of participating families increases, the rate of falsely implicating a marker not located within a short distance from a disease gene will eventually go down to zero. This would not only increase the chance of successful identification of disease genes, but would also save tremendous resources by reducing the chance of going after "ghost" genes. Thus, the methods developed can be a valuable tool to the gene mapping community. In particular, it is expected that the methods developed in this project will be applied to data from projects, on which the investigator is collaborating with medical doctors and other researchers, on a range of autoimmune diseases, including systemic lupus erythematosus and multiple sclerosis.