Two topics in association analysis of DNA sequencing data: population structure and multivariate traits

University of Minnesota Ph.D. dissertation. August 2013. Major: Biostatistics. Advisor: Wei Pan. 1 computer file (PDF); vii, 151 pages, appendices A-C.As the next-generation sequencing technologies become mature and affordable, we now have access to massive data of single nucleotides variants (SNVs) with varying minor allele frequencies (MAFs). This poses new opportunities, as more information from the human genome is available. However, new challenges also show up, such as how to utilize those SNVs with low MAFs. With current intensive efforts in association testing to detect genetic loci associated with common diseases and complex traits, two issues are of primary interest: reducing spurious findings and increasing power for true discoveries. In association testing, a major cause to the elevated level of false positives is the confounding effect of population structure -- the so-called population stratification. As a remedy, one popular method is to add principal components (PCs) in a regression model, named principal component regression (PCR). Yet, it is not clear how PCR will work in testing rare variants (RVs, with MAF$<0.01$), or with population stratification in a fine scale. More questions arise, like what types and what sets of SNVs should be used to construct PCs, and whether there are other better methods than principal component analysis (PCA) for constructing PCs. Utilizing the DNA sequencing data from the 1000 Genomes project, we first investigate whether PCR is adequate in adjusting for population stratification while maintaining high power when testing low frequency variants (LFVs with 0.01&lq MAF