2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2024

Hussein A. Hejase, Ziyi Mo, Leonardo Campagna, Adam Siepel. A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph. Molecular biology and evolution, 39(1). 2022.

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Download from publisher: https://doi.org/10.1093/molbev/msab332 PubMed

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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.