Fine human genetic map based on UK10K data set
Abstract
Abstract Recombination is a major force that shapes genetic diversity. Determination of recombination rate is important and can theoretically be improved by increasing the sample size. However, it is challenging to estimate recombination rates when the sample size is extraordinarily large because of computational burden. In this study, we used a refined artificial intelligence approach to estimate the recombination rate of the human genome using the UK10K human genomic dataset with 7,562 genomic sequences and its three subsets with 200, 400 and 2,000 genomic sequences under the Out-of-Africa demography model. We not only obtained an accurate human genetic map, but also found that the fluctuation of estimated recombination rate is reduced along the human genome when the sample size is increased. UK10K recombination activity is less concentrated than its subsets. Our results demonstrate how the sample size affects the estimated recombination rate, and analyses of a larger number of genomes result in a more precise estimation of recombination rate.
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