Abstract
The advent of high-throughput genotyping technologies has revolutionized evolutionary biology and genetics. These fields are now inundated by massive quantities of data, as determining dense sets of genotypes from individual genomes is no longer an overly expensive and difficult task. Attempting to find genes that are targets of selection is currently a major focus in several biological research areas. Selected genes are important from a plant- and animal-breeding perspective since they are likely to be determinants of desirable traits. Until very recently, the only practical approach to identify genes under positive selection has been to examine candidate genes. While these studies have had some successes, with the massive amounts of data becoming available, we can now take a much more powerful genomic approach to search for positively selected genes. I propose to develop statistical methods for making inferences about the evolutionary history of populations from large-scale genotype information. I will focus on locating selected genes that deviate from the genome average and use these methods to search for selected genes in the human genome using a data set of ~550 individuals sampled across the globe and each typed for ~550,000 SNPs – the largest world-wide human population-genetic data set to date. The results will help researchers better understand complex demographic processes and how to incorporate the evolutionary history in searching for selected genes.
Sweden