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- W2948989750 abstract "Abstract Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here we describe ReLERNN, a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, while largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations." @default.
- W2948989750 created "2019-06-14" @default.
- W2948989750 creator A5005571919 @default.
- W2948989750 creator A5071445210 @default.
- W2948989750 creator A5091552306 @default.
- W2948989750 date "2019-06-06" @default.
- W2948989750 modified "2023-10-17" @default.
- W2948989750 title "Inferring the landscape of recombination using recurrent neural networks" @default.
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- W2948989750 doi "https://doi.org/10.1101/662247" @default.
- W2948989750 hasPublicationYear "2019" @default.
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