A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Hejase, Hussein A, Mo, Ziyi, Campagna, Leonardo, Siepel, Adam (November 2021) A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph. Molecular Biology and Evolution. ISSN 0737-4038

[img] PDF
Available under License Creative Commons Attribution.

Download (1MB)
URL: https://www.ncbi.nlm.nih.gov/pubmed/34888675
DOI: 10.1093/molbev/msab332


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.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:
Communities: CSHL labs > Siepel lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 22 November 2021
Date Deposited: 15 Dec 2021 16:55
Last Modified: 28 Jan 2022 14:10
PMCID: PMC8789311
URI: https://repository.cshl.edu/id/eprint/40459

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving