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A likelihood approach for uncovering selective sweep signatures from haplotype data

Alexandre M. Harris, Michael DeGiorgio
doi: https://doi.org/10.1101/678722
Alexandre M. Harris
1Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
2Molecular, Cellular, and Integrative Biosciences at the Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
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Michael DeGiorgio
1Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
3Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
4Institute for CyberScience, Pennsylvania State University, University Park, PA 16802, USA
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  • For correspondence: mxd60@psu.edu
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Abstract

Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverage the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole genome polymorphism datasets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 21, 2019.
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A likelihood approach for uncovering selective sweep signatures from haplotype data
Alexandre M. Harris, Michael DeGiorgio
bioRxiv 678722; doi: https://doi.org/10.1101/678722
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A likelihood approach for uncovering selective sweep signatures from haplotype data
Alexandre M. Harris, Michael DeGiorgio
bioRxiv 678722; doi: https://doi.org/10.1101/678722

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