Genome Variation in Human Populations : Exploring the Effects of Demographic History and the Potential for Mapping of Complex Traits

Sammanfattning: A major challenge in human genetics is to understand the genetic variation underlying common diseases. In this thesis, I focus on forces creating differences between individuals and genomic regions, methods for characterizing genomic variation, and the association between genomic and phenotypic variation. Genetic markers are widely used to locate genes associated with different phenotypes. In my first paper, I describe novel algorithms for automatic genotype determination of microsatellite markers, a procedure which is currently both time-consuming and error prone. The co-segregation of genetic markers in a population leads to non-random association of alleles at different loci - linkage disequilibrium (LD). LD varies throughout the genome and differs between populations due to factors such as their demographic history. In my second paper, I discuss the increased power, for mapping of human traits, that results from studying a population with appreciable levels of LD such as is found in the Swedish Sami population. Lately, large-scale analyses of single nucleotide polymorphisms (SNPs) have become available and efforts have been made to identify a set of SNPs, which captures most of the genome variation in a population (tagSNPs). In my third paper, I describe the limitations of this approach when applied to data from an independent population sample of randomly ascertained SNPs. The transferability of tagSNPs between populations is poor, presumably due to variation in allele frequencies and the bias towards common SNPs used in most studies. The level of genomic variation is influenced by population structure, recombination and mutation rate, as well as natural selection. During the exodus from Africa, humans have adapted to new environmental conditions. In my fourth paper, I describe a new method for identifying genomic regions carrying signatures of recent positive selection and apply this to an available dataset of millions of SNPs.