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Soni V, Jensen JD. Temporal challenges in detecting balancing selection from population genomic data. G3 (BETHESDA, MD.) 2024; 14:jkae069. [PMID: 38551137 DOI: 10.1093/g3journal/jkae069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 12/21/2023] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
The role of balancing selection in maintaining genetic variation remains an open question in population genetics. Recent years have seen numerous studies identifying candidate loci potentially experiencing balancing selection, most predominantly in human populations. There are however numerous alternative evolutionary processes that may leave similar patterns of variation, thereby potentially confounding inference, and the expected signatures of balancing selection additionally change in a temporal fashion. Here we use forward-in-time simulations to quantify expected statistical power to detect balancing selection using both site frequency spectrum- and linkage disequilibrium-based methods under a variety of evolutionarily realistic null models. We find that whilst site frequency spectrum-based methods have little power immediately after a balanced mutation begins segregating, power increases with time since the introduction of the balanced allele. Conversely, linkage disequilibrium-based methods have considerable power whilst the allele is young, and power dissipates rapidly as the time since introduction increases. Taken together, this suggests that site frequency spectrum-based methods are most effective at detecting long-term balancing selection (>25N generations since the introduction of the balanced allele) whilst linkage disequilibrium-based methods are effective over much shorter timescales (<1N generations), thereby leaving a large time frame over which current methods have little power to detect the action of balancing selection. Finally, we investigate the extent to which alternative evolutionary processes may mimic these patterns, and demonstrate the need for caution in attempting to distinguish the signatures of balancing selection from those of both neutral processes (e.g. population structure and admixture) as well as of alternative selective processes (e.g. partial selective sweeps).
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
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Soni V, Pfeifer SP, Jensen JD. The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects. Genome Biol Evol 2024; 16:evae004. [PMID: 38207127 PMCID: PMC10834165 DOI: 10.1093/gbe/evae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/12/2023] [Accepted: 01/07/2024] [Indexed: 01/13/2024] Open
Abstract
Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavor; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modeled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination before utilizing population genomic data to quantify the effects of genetic drift (i.e. as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modeled in downstream inference.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
| | - Susanne P Pfeifer
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, USA
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Soni V, Johri P, Jensen JD. Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models. Evolution 2023; 77:2113-2127. [PMID: 37395482 PMCID: PMC10547124 DOI: 10.1093/evolut/qpad120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/15/2023] [Accepted: 06/30/2023] [Indexed: 07/04/2023]
Abstract
The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
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Soni V, Johri P, Jensen JD. Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.15.545166. [PMID: 37398347 PMCID: PMC10312679 DOI: 10.1101/2023.06.15.545166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser Text Outlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Present address: Department of Biology, Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
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Freund F, Kerdoncuff E, Matuszewski S, Lapierre M, Hildebrandt M, Jensen JD, Ferretti L, Lambert A, Sackton TB, Achaz G. Interpreting the pervasive observation of U-shaped Site Frequency Spectra. PLoS Genet 2023; 19:e1010677. [PMID: 36952570 PMCID: PMC10072462 DOI: 10.1371/journal.pgen.1010677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 04/04/2023] [Accepted: 02/22/2023] [Indexed: 03/25/2023] Open
Abstract
The standard neutral model of molecular evolution has traditionally been used as the null model for population genomics. We gathered a collection of 45 genome-wide site frequency spectra from a diverse set of species, most of which display an excess of low and high frequency variants compared to the expectation of the standard neutral model, resulting in U-shaped spectra. We show that multiple merger coalescent models often provide a better fit to these observations than the standard Kingman coalescent. Hence, in many circumstances these under-utilized models may serve as the more appropriate reference for genomic analyses. We further discuss the underlying evolutionary processes that may result in the widespread U-shape of frequency spectra.
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Affiliation(s)
- Fabian Freund
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Elise Kerdoncuff
- Department of Genetics, University of California, Berkeley, California, United States of America
- Informatics Group, Harvard University, Cambridge, Massachusetts, United States of America
| | | | - Marguerite Lapierre
- Informatics Group, Harvard University, Cambridge, Massachusetts, United States of America
| | | | - Jeffrey D Jensen
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Amaury Lambert
- Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, Paris, France
- Informatics Group, Harvard University, Cambridge, Massachusetts, United States of America
| | - Timothy B Sackton
- Éco-anthropologie, Muséum National d'Histoire Naturelle, Université Paris-Cité, Paris, France
| | - Guillaume Achaz
- Informatics Group, Harvard University, Cambridge, Massachusetts, United States of America
- SMILE group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, Paris, France
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Abstract
We discuss the genetic, demographic, and selective forces that are likely to be at play in restricting observed levels of DNA sequence variation in natural populations to a much smaller range of values than would be expected from the distribution of census population sizes alone-Lewontin's Paradox. While several processes that have previously been strongly emphasized must be involved, including the effects of direct selection and genetic hitchhiking, it seems unlikely that they are sufficient to explain this observation without contributions from other factors. We highlight a potentially important role for the less-appreciated contribution of population size change; specifically, the likelihood that many species and populations may be quite far from reaching the relatively high equilibrium diversity values that would be expected given their current census sizes.
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Affiliation(s)
- Brian Charlesworth
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
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Charlesworth B, Jensen JD. Effects of Selection at Linked Sites on Patterns of Genetic Variability. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2021; 52:177-197. [PMID: 37089401 PMCID: PMC10120885 DOI: 10.1146/annurev-ecolsys-010621-044528] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Patterns of variation and evolution at a given site in a genome can be strongly influenced by the effects of selection at genetically linked sites. In particular, the recombination rates of genomic regions correlate with their amount of within-population genetic variability, the degree to which the frequency distributions of DNA sequence variants differ from their neutral expectations, and the levels of adaptation of their functional components. We review the major population genetic processes that are thought to lead to these patterns, focusing on their effects on patterns of variability: selective sweeps, background selection, associative overdominance, and Hill–Robertson interference among deleterious mutations. We emphasize the difficulties in distinguishing among the footprints of these processes and disentangling them from the effects of purely demographic factors such as population size changes. We also discuss how interactions between selective and demographic processes can significantly affect patterns of variability within genomes.
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Affiliation(s)
- Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Jeffrey D. Jensen
- School of Life Sciences, Arizona State University, Tempe, Arizona 85281, USA
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Burbrink FT, Ruane S. Contemporary Philosophy and Methods for Studying Speciation and Delimiting Species. ICHTHYOLOGY & HERPETOLOGY 2021. [DOI: 10.1643/h2020073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Frank T. Burbrink
- Department of Herpetology, American Museum of Natural History, Central Park West at 79th Street, New York, New York 10024; . Send reprint requests to this address
| | - Sara Ruane
- Earth and Environmental Sciences: Ecology and Evolution, Rutgers University–Newark, 195 University Avenue, Newark, New Jersey 07102
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