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SIA: Selection Inference Using the Ancestral Recombination Graph

Hussein A. Hejase, View ORCID ProfileZiyi Mo, View ORCID ProfileLeonardo Campagna, View ORCID ProfileAdam Siepel
doi: https://doi.org/10.1101/2021.06.22.449427
Hussein A. Hejase
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Ziyi Mo
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
2School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Leonardo Campagna
3Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Ithaca, NY, USA
4Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
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Adam Siepel
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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  • For correspondence: asiepel@cshl.edu
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Abstract

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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-ND 4.0 International license.
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Posted June 23, 2021.
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SIA: Selection Inference Using the Ancestral Recombination Graph
Hussein A. Hejase, Ziyi Mo, Leonardo Campagna, Adam Siepel
bioRxiv 2021.06.22.449427; doi: https://doi.org/10.1101/2021.06.22.449427
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SIA: Selection Inference Using the Ancestral Recombination Graph
Hussein A. Hejase, Ziyi Mo, Leonardo Campagna, Adam Siepel
bioRxiv 2021.06.22.449427; doi: https://doi.org/10.1101/2021.06.22.449427

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