Diss. (sammanfattning) Umeå : Umeå universitet, 2010.

As sequencing technology continues to produce better quality genomes at decreasing costs, there has been a recent surge in the variety of data that we are now able to analyze. This is particularly true with regards to our understanding of the human genome—where the last decade has seen data advances in primate epigenomics, ancient hominid genomics, and a proliferation of human polymorphism data from multiple populations. In order to utilize such data however, it has become critical to develop increasingly sophisticated tools spanning both bioinformatics and statistical inference. In population genetics particularly, new statistical approaches for analyzing population data are constantly being developed—unfortunately, often without proper model testing and evaluation of type-I and type-II error. Because the common Wright-Fisher assumptions underlying such models are generally violated in natural populations, this statistical testing is critical. Thus, my dissertation has two distinct but related themes: 1) evaluating methods of statistical inference in population genetics, and 2) utilizing these methods to analyze the evolutionary history of humans and our closest relatives. The resulting collection of work has not only provided important biological insights (including some of the first strong evidence of selection on human-specific epigenetic modifications (Shulha, Crisci, Reshetov, Tushir et al. 2012, PLoS Bio), and a characterization of human-specific genetic changes distinguishing modern humans from Neanderthals (Crisci et al. 2011, GBE)), but also important insights in to the performance of population genetic methodologies which will motivate the future development of improved approaches for statistical inference (Crisci et al, in review).

The three dimensional structure of the human genome is known to play a critical role in gene function and expression. I used chromosome conformation capture (3C) and 3C-carbon copy (5C) techniques to investigate the three-dimensional structure of the cystic fibrosis transmembrane conductance regulator (CFTR) locus. This is an important disease gene that, when mutated, causes cystic fibrosis. 3C experiments identified four distinct looping elements that contact the CFTR gene promoter only in CFTR-expressing cells. Using 5C, I expanded the region of study to a 2.8 Mb region surrounding the CFTR gene. The 5C study shows 7 clear topologically associating domains (TADs) present at the locus, identical in all five cell lines tested, regardless of gene expression status. CFTR and all its known regulatory elements are contained within one TAD, suggesting TADs play a role in constraining promoters to a local search space. The four looping elements identified in the 3C experiment and confirmed in the 5C experiment were then tested for enhancer activity using a luciferase assay, which showed that elements III and IV could act as enhancers. These elements were tested against a library of human transcription factors in a yeast one-hybrid assay to identify potential binding proteins. Element III gave two strong candidates, TCF4 and LEF1. A literature search supported these transcription factors as playing a role in CFTR gene expression. Overall, this work represents a model locus that can be used to test important questions regarding the role of three dimensional looping on gene expression.