Method identifies recombination hotspots

摘要

Describing genetic recombination rate variation and the distribution of recombination "hotspots" across chromosomes are important goals in population genetics. Ying Wang and Bruce Rannala have developed a method for identifying recombination hotspots throughout the genome. Rather than use sperm-typing analyses, which are laborious and expensive, the authors used a population-based approach. Wang and Rannala used the Markov Chain Monte Carlo method to analyze single nucleotide polymorphism data from the human leukocyte antigen (HLA) region MS32 and chromosome 19 to identify areas prone to high levels of recombination. The authors found that the results closely matched those from sperm-typing studies and suggest that these recombination hotspots may pinpoint regions in the genome where diversity is particularly important and favored by natural selection. Conversely, the method identifies some intervening regions with low recombination rates, possibly because of selective constraints, according to the authors.