Multi-locus association tests for detecting complex disease genes

This dissertation has three separate parts: the first part dealing with linkage or association test based on nuclear families by using multi-marker haplotypes; the second part dealing with random genotyping errors; the third part dealing with candidate gene association test based on population data sets.; In the first part, I developed a new statistical method, Haplotype Sharing Transmission/Disequilibrium Test (HS-TDT), to test for linkage or association between a trait and a chromosomal region in which several tightly linked markers have been typed. This method is applicable to both quantitative traits and qualitative traits. It is applicable to any size of nuclear family with or without ambiguous phase information, and it is applicable to any number of alleles at each of the markers.; Part II is concerned with random genotyping errors. In this part, new HS-TDTs that allow for random genotyping errors are introduced. We evaluate the type I error rate and power of the new proposed tests under a variety of scenarios and perform a power comparison among the proposed tests, the HS-TDT and the single-marker TDT. The results indicate that the HS-TDT shows a significant increase in type I error when applied to data in which either Mendelian inconsistent trios are removed or Mendelian inconsistent markers are treated as missing genotypes, and the magnitude of the type I error rate increases both with an increase in sample size and with an increase in genotyping error rate. In part III, we propose to use the principal component method to reduce the dimension and then construct association tests on the lower-dimensional space to test the association between haplotypes and a quantitative trait using population-based samples. We use simulation studies to evaluate the type I error rate of the tests and compare the power of the proposed tests with that of the tests without dimension reduction and the tests with dimension reduction by merging rare haplotypes. (Abstract shortened by UMI.)...