Computational Methods for Meiotic Recombination Inference

Meiotic recombination is one of major evolutionary mechanisms responsible for promoting genetic variation in a population, and is important for many problems in evolutionary biology and population genetics. In this thesis, we investigate two computational problems that arise in studying meiotic recombination. The first problem is concerned with two different type of meiotic recombination: crossovers and gene conversions. Although crossovers and gene conversions have different effects on the evolutionary history of chromosomes and therefore leave behind different footprints in the genome, it is a challenging task to tease apart their relative contributions to the observed genetic variation. In fact, the methods employed in recent studies of recombination rate variation in the human genome actually capture combined effects of crossovers and gene conversions. By explicitly incorporating overlapping gene conversion events, we propose a new statistical model that can jointly estimate the crossover rate, the gene conversion rate and the mean tract length, which is widely regarded as a very difficult problem. Our simulated results show that modeling overlapping gene conversions is crucial for improving the accuracy of the joint estimation of the aforementioned three fundamental parameters. Our analysis of real data from the telomere of the X chromosome of Drosophila melanogaster suggests that the ratio of the gene conversion rate to the crossover rate for the region may not be nearly as high as previously claimed.In the second problem, we investigate the molecular basis of meiotic recombination. In mammalian organisms, recombination events tend to cluster into short 1-2 kb genomic regions known as recombination hotspots. Recent studies have mainly focused on identifying cis and trans-acting elements that can modulate the activity of recombination hotspots in mammals, but most of the work neglects the role of nucleosomes, the basic unit of DNA packaging in eukaryotes. Our analysis on the correlation of H2A.Z nucleosome positions and recombination rates in Drosophila melanogaster suggests that nucleosome occupancy could also influence, at least partly, the activity of recombination