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Daigle A, Johri P. Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579142. [PMID: 38370745 PMCID: PMC10871249 DOI: 10.1101/2024.02.06.579142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.
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Affiliation(s)
- Austin Daigle
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599
| | - Parul Johri
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599
- Integrative Program for Biological & Genome Sciences, University of North Carolina, Chapel Hill, NC 27599
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2
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Roberts M, Josephs EB. Previously unmeasured genetic diversity explains part of Lewontin's paradox in a k -mer-based meta-analysis of 112 plant species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594778. [PMID: 38798362 PMCID: PMC11118579 DOI: 10.1101/2024.05.17.594778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
At the molecular level, most evolution is expected to be neutral. A key prediction of this expectation is that the level of genetic diversity in a population should scale with population size. However, as was noted by Richard Lewontin in 1974 and reaffirmed by later studies, the slope of the population size-diversity relationship in nature is much weaker than expected under neutral theory. We hypothesize that one contributor to this paradox is that current methods relying on single nucleotide polymorphisms (SNPs) called from aligning short reads to a reference genome underestimate levels of genetic diversity in many species. To test this idea, we calculated nucleotide diversity ( π ) and k -mer-based metrics of genetic diversity across 112 plant species, amounting to over 205 terabases of DNA sequencing data from 27,488 individual plants. We then compared how these different metrics correlated with proxies of population size that account for both range size and population density variation across species. We found that our population size proxies scaled anywhere from about 3 to over 20 times faster with k -mer diversity than nucleotide diversity after adjusting for evolutionary history, mating system, life cycle habit, cultivation status, and invasiveness. The relationship between k -mer diversity and population size proxies also remains significant after correcting for genome size, whereas the analogous relationship for nucleotide diversity does not. These results suggest that variation not captured by common SNP-based analyses explains part of Lewontin's paradox in plants. Lay Summary Even after many revolutions in our ability to sequence and understand DNA, many important biological questions remain unsolved. One such problem is Lewontin's paradox, named after Richard Lewontin who first described it in 1974. The core of the paradox is a simple idea: species with more individuals should be more genetically diverse. The reasoning is that more individuals means more replication of DNA, and thus more opportunities for mutation to create new variation. However, species that differ massively in population size often have similar diversity levels. Lewontin's paradox has several potential, previously investigated mechanisms but what if one contributor is simply that our measurements of genetic diversity are off? Most studies estimate diversity by comparing sample genomes to a standard reference genome. While this approach is useful, it is impossible to measure variation in DNA that is not represented in the reference - a phenomenon known as reference bias. We estimate metrics of diversity that are free of reference-bias and re-investigate Lewontin's paradox in plants. Overall, we find that reference-free diversity metrics scale more with population size, compared to the reference-biased approach. While it is unlikely that reference-bias fully explains Lewontin's paradox, our analyses suggest that reference-bias plays an important role.
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3
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Marsh JI, Johri P. Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection. Mol Biol Evol 2024; 41:msae118. [PMID: 38874402 PMCID: PMC11245712 DOI: 10.1093/molbev/msae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.
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Affiliation(s)
- Jacob I Marsh
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Parul Johri
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Integrative Program for Biological and Genome Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
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4
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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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5
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Soni V, Terbot JW, Jensen JD. Population genetic considerations regarding the interpretation of within-patient SARS-CoV-2 polymorphism data. Nat Commun 2024; 15:3240. [PMID: 38627371 PMCID: PMC11021480 DOI: 10.1038/s41467-024-46261-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/29/2024] [Indexed: 04/19/2024] Open
Affiliation(s)
- Vivak Soni
- Center for Evolution & Medicine, Arizona State University, School of Life Sciences, Tempe, AZ, USA
| | - John W Terbot
- Center for Evolution & Medicine, Arizona State University, School of Life Sciences, Tempe, AZ, USA
- Division of Biological Sciences, University of Montana, Missoula, MT, USA
| | - Jeffrey D Jensen
- Center for Evolution & Medicine, Arizona State University, School of Life Sciences, Tempe, AZ, USA.
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6
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Murga-Moreno J, Casillas S, Barbadilla A, Uricchio L, Enard D. An efficient and robust ABC approach to infer the rate and strength of adaptation. G3 (BETHESDA, MD.) 2024; 14:jkae031. [PMID: 38365205 PMCID: PMC11090462 DOI: 10.1093/g3journal/jkae031] [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: 10/10/2023] [Revised: 10/10/2023] [Accepted: 01/29/2024] [Indexed: 02/18/2024]
Abstract
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.
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Affiliation(s)
- Jesús Murga-Moreno
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | | | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
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7
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Galtier N. Half a Century of Controversy: The Neutralist/Selectionist Debate in Molecular Evolution. Genome Biol Evol 2024; 16:evae003. [PMID: 38311843 PMCID: PMC10839204 DOI: 10.1093/gbe/evae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/06/2024] Open
Abstract
The neutral and nearly neutral theories, introduced more than 50 yr ago, have raised and still raise passionate discussion regarding the forces governing molecular evolution and their relative importance. The debate, initially focused on the amount of within-species polymorphism and constancy of the substitution rate, has spread, matured, and now underlies a wide range of topics and questions. The neutralist/selectionist controversy has structured the field and influences the way molecular evolutionary scientists conceive their research.
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Affiliation(s)
- Nicolas Galtier
- ISEM, CNRS, IRD, Université de Montpellier, Montpellier, France
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8
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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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9
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Russo CAM, Eyre-Walker A, Katz LA, Gaut BS. Forty Years of Inferential Methods in the Journals of the Society for Molecular Biology and Evolution. Mol Biol Evol 2024; 41:msad264. [PMID: 38197288 PMCID: PMC10763999 DOI: 10.1093/molbev/msad264] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 01/11/2024] Open
Abstract
We are launching a series to celebrate the 40th anniversary of the first issue of Molecular Biology and Evolution. In 2024, we will publish virtual issues containing selected papers published in the Society for Molecular Biology and Evolution journals, Molecular Biology and Evolution and Genome Biology and Evolution. Each virtual issue will be accompanied by a perspective that highlights the historic and contemporary contributions of our journals to a specific topic in molecular evolution. This perspective, the first in the series, presents an account of the broad array of methods that have been published in the Society for Molecular Biology and Evolution journals, including methods to infer phylogenies, to test hypotheses in a phylogenetic framework, and to infer population genetic processes. We also mention many of the software implementations that make methods tractable for empiricists. In short, the Society for Molecular Biology and Evolution community has much to celebrate after four decades of publishing high-quality science including numerous important inferential methods.
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Affiliation(s)
- Claudia A M Russo
- Departamento de Genética, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Laura A Katz
- Department of Biological Sciences, Smith College, Northampton, MA, USA
| | - Brandon S Gaut
- School of Biological Sciences, University of California, Irvine, CA, USA
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10
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Panigrahi M, Rajawat D, Nayak SS, Ghildiyal K, Sharma A, Jain K, Lei C, Bhushan B, Mishra BP, Dutt T. Landmarks in the history of selective sweeps. Anim Genet 2023; 54:667-688. [PMID: 37710403 DOI: 10.1111/age.13355] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.
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Affiliation(s)
- Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | | | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Karan Jain
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Bishnu Prasad Mishra
- Division of Animal Biotechnology, ICAR-National Bureau of Animal Genetic Resources, Karnal, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, India
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11
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Zheng L, Wang H, Lin J, Zhou Y, Xiao J, Li K. Population genomics provides insights into the genetic diversity and adaptation of the Pieris rapae in China. PLoS One 2023; 18:e0294521. [PMID: 37972203 PMCID: PMC10653512 DOI: 10.1371/journal.pone.0294521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
The cabbage white butterfly (Pieris rapae), a major agricultural pest, has become one of the most abundant and destructive butterflies in the world. It is widely distributed in a large variety of climates and terrains of China due to its strong adaptability. To gain insight into the population genetic characteristics of P. rapae in China, we resequenced the genome of 51 individuals from 19 areas throughout China. Using population genomics approaches, a dense variant map of P. rapae was observed, indicating a high level of polymorphism that could result in adaptation to a changing environment. The feature of the genetic structure suggested considerable genetic admixture in different geographical groups. Additionally, our analyses suggest that physical barriers may have played a more important role than geographic distance in driving genetic differentiation. Population history showed the effective population size of P. rapae was greatly affected by global temperature changes, with mild periods (i.e., temperatures warmer than those during glaciation but not excessively hot) leading to an increase in population size. Furthermore, by comparing populations from south and north China, we have identified selected genes related to sensing temperature, growth, neuromodulation and immune response, which may reveal the genetic basis of adaptation to different environments. Our study is the first to illustrate the genetic signatures of P. rapae in China at the population genomic level, providing fundamental knowledge of the genetic diversity and adaptation of P. rapae.
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Affiliation(s)
- Linlin Zheng
- College of Biological Science and Medical Engineering, Donghua University, Songjiang District, Shanghai, China
| | - Huan Wang
- Department of Plant Science and Technology, Shanghai Vocational College of Agriculture and Forestry, Shanghai, China
| | - Junjie Lin
- College of Biological Science and Medical Engineering, Donghua University, Songjiang District, Shanghai, China
| | - Yuxun Zhou
- College of Biological Science and Medical Engineering, Donghua University, Songjiang District, Shanghai, China
| | - Junhua Xiao
- College of Biological Science and Medical Engineering, Donghua University, Songjiang District, Shanghai, China
| | - Kai Li
- College of Biological Science and Medical Engineering, Donghua University, Songjiang District, Shanghai, China
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12
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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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.11.566703. [PMID: 38014252 PMCID: PMC10680612 DOI: 10.1101/2023.11.11.566703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
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 endeavour; 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 modelled, 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 prior to 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 modelled in downstream inference.
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Affiliation(s)
- Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
| | - Susanne P. Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
| | - Jeffrey D. Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine
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13
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James J, Kastally C, Budde KB, González-Martínez SC, Milesi P, Pyhäjärvi T, Lascoux M. Between but Not Within-Species Variation in the Distribution of Fitness Effects. Mol Biol Evol 2023; 40:msad228. [PMID: 37832225 PMCID: PMC10630145 DOI: 10.1093/molbev/msad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/04/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
New mutations provide the raw material for evolution and adaptation. The distribution of fitness effects (DFE) describes the spectrum of effects of new mutations that can occur along a genome, and is, therefore, of vital interest in evolutionary biology. Recent work has uncovered striking similarities in the DFE between closely related species, prompting us to ask whether there is variation in the DFE among populations of the same species, or among species with different degrees of divergence, that is whether there is variation in the DFE at different levels of evolution. Using exome capture data from six tree species sampled across Europe we characterized the DFE for multiple species, and for each species, multiple populations, and investigated the factors potentially influencing the DFE, such as demography, population divergence, and genetic background. We find statistical support for the presence of variation in the DFE at the species level, even among relatively closely related species. However, we find very little difference at the population level, suggesting that differences in the DFE are primarily driven by deep features of species biology, and those evolutionarily recent events, such as demographic changes and local adaptation, have little impact.
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Affiliation(s)
- Jennifer James
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
- Swedish Collegium of Advanced Study, Uppsala University, Uppsala, Sweden
| | - Chedly Kastally
- Department of Forest Sciences, University of Helsinki, Helsinki, Finland
- Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
| | - Katharina B Budde
- Department of Forest Genetics and Forest Tree Breeding, Georg-August-University Goettingen, Goettingen, Germany
- Center of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany
| | - Santiago C González-Martínez
- National Research Institute for Agriculture, Food and the Environment (INRAE), University of Bordeaux, BIOGECO, Cestas, France
| | - Pascal Milesi
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory (SciLifeLab), Uppsala University, Uppsala, Sweden
| | - Tanja Pyhäjärvi
- Department of Forest Sciences, University of Helsinki, Helsinki, Finland
- Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
| | - Martin Lascoux
- Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
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14
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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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15
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Murga-Moreno J, Casillas S, Barbadilla A, Uricchio L, Enard D. An efficient and robust ABC approach to infer the rate and strength of adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555322. [PMID: 37693550 PMCID: PMC10491248 DOI: 10.1101/2023.08.29.555322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in non-model species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to non-model genomes. We apply ABC-MK to the human proteome and a set of known Virus Interacting Proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.
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Affiliation(s)
- Jesús Murga-Moreno
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, USA
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | | | - David Enard
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, USA
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16
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Whitehouse LS, Schrider DR. Timesweeper: accurately identifying selective sweeps using population genomic time series. Genetics 2023; 224:iyad084. [PMID: 37157914 PMCID: PMC10324941 DOI: 10.1093/genetics/iyad084] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/25/2022] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.
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Affiliation(s)
- Logan S Whitehouse
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
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17
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Baumgarten L, Pieper B, Song B, Mane S, Lempe J, Lamb J, Cooke EL, Srivastava R, Strütt S, Žanko D, Casimiro PGP, Hallab A, Cartolano M, Tattersall AD, Huettel B, Filatov DA, Pavlidis P, Neuffer B, Bazakos C, Schaefer H, Mott R, Gan X, Alonso-Blanco C, Laurent S, Tsiantis M. Pan-European study of genotypes and phenotypes in the Arabidopsis relative Cardamine hirsuta reveals how adaptation, demography, and development shape diversity patterns. PLoS Biol 2023; 21:e3002191. [PMID: 37463141 PMCID: PMC10353826 DOI: 10.1371/journal.pbio.3002191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 06/10/2023] [Indexed: 07/20/2023] Open
Abstract
We study natural DNA polymorphisms and associated phenotypes in the Arabidopsis relative Cardamine hirsuta. We observed strong genetic differentiation among several ancestry groups and broader distribution of Iberian relict strains in European C. hirsuta compared to Arabidopsis. We found synchronization between vegetative and reproductive development and a pervasive role for heterochronic pathways in shaping C. hirsuta natural variation. A single, fast-cycling ChFRIGIDA allele evolved adaptively allowing range expansion from glacial refugia, unlike Arabidopsis where multiple FRIGIDA haplotypes were involved. The Azores islands, where Arabidopsis is scarce, are a hotspot for C. hirsuta diversity. We identified a quantitative trait locus (QTL) in the heterochronic SPL9 transcription factor as a determinant of an Azorean morphotype. This QTL shows evidence for positive selection, and its distribution mirrors a climate gradient that broadly shaped the Azorean flora. Overall, we establish a framework to explore how the interplay of adaptation, demography, and development shaped diversity patterns of 2 related plant species.
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Affiliation(s)
- Lukas Baumgarten
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Bjorn Pieper
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Baoxing Song
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Sébastien Mane
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Janne Lempe
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Jonathan Lamb
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Elizabeth L. Cooke
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Rachita Srivastava
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Stefan Strütt
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Danijela Žanko
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Asis Hallab
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Maria Cartolano
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Bruno Huettel
- Max Planck Genome Centre Cologne, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Pavlos Pavlidis
- Institute of Computer Science, Foundation for Research and Technology, Crete, Greece
| | - Barbara Neuffer
- Department of Botany, University of Osnabrück, Osnabrück, Germany
| | - Christos Bazakos
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Hanno Schaefer
- Department Life Science Systems, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Richard Mott
- Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Xiangchao Gan
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Carlos Alonso-Blanco
- Department of Plant Molecular Genetics, Centro Nacional de Biotecnología (CNB), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Stefan Laurent
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Miltos Tsiantis
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
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18
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Johri P, Pfeifer SP, Jensen JD. Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.11.536488. [PMID: 37090533 PMCID: PMC10120674 DOI: 10.1101/2023.04.11.536488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection ( e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome ( i.e. , purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.
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19
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Terbot JW, Johri P, Liphardt SW, Soni V, Pfeifer SP, Cooper BS, Good JM, Jensen JD. Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples. PLoS Pathog 2023; 19:e1011265. [PMID: 37018331 PMCID: PMC10075409 DOI: 10.1371/journal.ppat.1011265] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.
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Affiliation(s)
- John W Terbot
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Parul Johri
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Schuyler W Liphardt
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Vivak Soni
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Susanne P Pfeifer
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
| | - Brandon S Cooper
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey M Good
- University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America
| | - Jeffrey D Jensen
- Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America
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20
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Jensen JD. Population genetic concerns related to the interpretation of empirical outliers and the neglect of common evolutionary processes. Heredity (Edinb) 2023; 130:109-110. [PMID: 36829044 PMCID: PMC9981695 DOI: 10.1038/s41437-022-00575-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 02/26/2023] Open
Affiliation(s)
- Jeffrey D Jensen
- School of Life Science, Arizona State University, Tempe, AZ, USA.
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21
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Korfmann K, Gaggiotti OE, Fumagalli M. Deep Learning in Population Genetics. Genome Biol Evol 2023; 15:evad008. [PMID: 36683406 PMCID: PMC9897193 DOI: 10.1093/gbe/evad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/19/2022] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.
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Affiliation(s)
- Kevin Korfmann
- Professorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, Germany
| | - Oscar E Gaggiotti
- Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews, Fife KY16 9TF, UK
| | - Matteo Fumagalli
- Department of Biological and Behavioural Sciences, Queen Mary University of London, UK
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22
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Nocchi G, Wang J, Yang L, Ding J, Gao Y, Buggs RJA, Wang N. Genomic signals of local adaptation and hybridization in Asian white birch. Mol Ecol 2023; 32:595-612. [PMID: 36394364 DOI: 10.1111/mec.16788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022]
Abstract
Disentangling the numerous processes that affect patterns of genome-wide diversity in widespread tree species has important implications for taxonomy, conservation, and forestry. Here, we investigate the population genomic structure of Asian white birch (Betula platyphylla) in China and seek to explain it in terms of hybridization, demography and adaptation. We generate whole genome sequence data from 83 individuals across the species range in China. Combining this with an existing data set for 79 European and Russian white birches, we show a clear distinction between B. pendula and B. platyphylla, which have sometimes been lumped taxonomically. Genomic diversity of B. platyphylla in north-western China and Central Russia is affected greatly by hybridization with B. pendula. Excluding these hybridized populations, B. platyphylla in China has a linear distribution from north-eastern to south-western China, along the edge of the inland mountainous region. Within this distribution, three genetic clusters are found, which we model as long diverged with subsequent episodes of gene flow. Patterns of covariation between allele frequencies and environmental variables in B. platyphylla suggest the role of natural selection in the distribution of diversity at 7609 SNPs of which 3767 were significantly differentiated among the genetic clusters. The putative adaptive SNPs are distributed throughout the genome and span 1633 genic regions. Of these genic regions, 87 were previously identified as candidates for selective sweeps in Eurasian B. pendula. We use the 7609 environmentally associated SNPs to estimate the risk of nonadaptedness for each sequenced B. platyphylla individual under a scenario of future climate change, highlighting areas where populations may be under future threat from rising temperatures.
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Affiliation(s)
- Gabriele Nocchi
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.,Royal Botanic Gardens Kew, Richmond, Surrey, UK
| | - Jing Wang
- Key Laboratory for Bio-resources and Eco-environment, College of Life Science, Sichuan University, Chengdu, China
| | - Long Yang
- Agricultural Big-Data Research Centre and College of Plant Protection, Shandong Agricultural University, Tai'an, China
| | - Junyi Ding
- State Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, College of Forestry, Shandong Agricultural University, Tai'an, China.,Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, College of Forestry, Shandong Agricultural University, Tai'an, China
| | - Ying Gao
- Agricultural Big-Data Research Centre and College of Plant Protection, Shandong Agricultural University, Tai'an, China
| | - Richard J A Buggs
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.,Royal Botanic Gardens Kew, Richmond, Surrey, UK
| | - Nian Wang
- State Forestry and Grassland Administration Key Laboratory of Silviculture in Downstream Areas of the Yellow River, College of Forestry, Shandong Agricultural University, Tai'an, China.,Mountain Tai Forest Ecosystem Research Station of State Forestry and Grassland Administration, College of Forestry, Shandong Agricultural University, Tai'an, China.,State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an, China
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23
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Johri P, Aquadro CF, Beaumont M, Charlesworth B, Excoffier L, Eyre-Walker A, Keightley PD, Lynch M, McVean G, Payseur BA, Pfeifer SP, Stephan W, Jensen JD. Recommendations for improving statistical inference in population genomics. PLoS Biol 2022; 20:e3001669. [PMID: 35639797 PMCID: PMC9154105 DOI: 10.1371/journal.pbio.3001669] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Charles F. Aquadro
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
| | - Mark Beaumont
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne, Switzerland
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Peter D. Keightley
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael Lynch
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Bret A. Payseur
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Susanne P. Pfeifer
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Jeffrey D. Jensen
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
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