Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population

Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population | bioRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population View ORCID Profile Yuehao Xu , Andreas Futschik , View ORCID Profile Ritabrata Dutta doi: https://doi.org/10.1101/2022.09.20.508756 Yuehao Xu a Department of Applied Statistics, Johannes Kepler University Linz , Altenbergerstraße 69, Linz, 4040, Upper Austria, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yuehao Xu Andreas Futschik a Department of Applied Statistics, Johannes Kepler University Linz , Altenbergerstraße 69, Linz, 4040, Upper Austria, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ritabrata Dutta b Department of Statistics, University of Warwick , University Road, Coventry, CV4 7AL, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ritabrata Dutta For correspondence: ritabrata.dutta{at}warwick.ac.uk Abstract Full Text Info/History Metrics Preview PDF Abstract Given the often intractable exact likelihood, likelihood-free inference plays an important role in population genetics. Indeed, several methodological developments in approximate Bayesian Computation (ABC) were inspired by applications in population genetics. Here, we explore a novel combination of recently proposed ABC tools that can handle high-dimensional summary statistics and apply them to infer selection strength and the number of selected loci from experimental evolution data. While several methods infer selection strength at the single-nucleotide polymorphism (SNP) level, our approach provides additional information about the selective architecture, including the number of selected positions in a candidate window of interest. This is nontrivial, since the spatial correlation induced by genomic linkage can produce selection signals at neighboring SNPs. A further advantage of our approach is that we can readily quantify uncertainty using the ABC posterior. On both simulated and real data, we demonstrate promising performance. This suggests that our ABC variant could also be interesting in other applications. Competing Interest Statement The authors have declared no competing interest. Footnotes table 2-10 being updated and figure 5 being added to answer reviewers' concerns. Some writing were also adapted. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted March 16, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population Yuehao Xu , Andreas Futschik , Ritabrata Dutta bioRxiv 2022.09.20.508756; doi: https://doi.org/10.1101/2022.09.20.508756 Share This Article: Copy Citation Tools Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population Yuehao Xu , Andreas Futschik , Ritabrata Dutta bioRxiv 2022.09.20.508756; doi: https://doi.org/10.1101/2022.09.20.508756 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Evolutionary Biology Subject Areas All Articles Animal Behavior and Cognition (7998) Biochemistry (18705) Bioengineering (14824) Bioinformatics (44323) Biophysics (22545) Cancer Biology (19664) Cell Biology (26832) Clinical Trials (138) Developmental Biology (13929) Ecology (20965) Epidemiology (2067) Evolutionary Biology (25405) Genetics (16143) Genomics (23459) Immunology (18664) Microbiology (42377) Molecular Biology (18015) Neuroscience (93278) Paleontology (699) Pathology (2976) Pharmacology and Toxicology (5082) Physiology (8097) Plant Biology (15963) Scientific Communication and Education (2094) Synthetic Biology (4546) Systems Biology (10214) Zoology (2382)

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