Population genetics allows us to answer questions across scales from deep evolutionary time to ongoing ecological dynamics, and dramatic reductions in sequencing costs enable the generation of unprecedented amounts of genomic data that can be used to address these questions (Ellegren, 2014). Ongoing efforts to systematically sequence life on Earth by initiatives such as the Earth Biogenome (Lewin et al., 2022) and its affiliated project networks, such as Vertebrate Genomes (Rhie et al., 2021), 10,000 Plants (Cheng et al., 2018) and others (Darwin Tree of Life Project Consortium, 2022), are providing the backbone for enormous increases in the amount of population-level genomic data available for model and non-model species. These data are being used, among other things, in the inference of population history and demographic parameters (Beichman et al., 2018), studying adaptive introgression (Gower et al., 2021), providing null expectations for selection scans (e.g. Hsieh et al., 2021), and understanding the implications of deleterious variation in populations of conservation concern (e.g. Robinson et al., 2023). While many of the methods that address these questions were initially developed for a few key model systems such as humans and Drosophila, more recent efforts are generalizing these methods to include important factors not initially accounted for, such as inbreeding or selfing (Blischak et al., 2020), skewed offspring distributions (Montano, 2016), and intense artificial selection even for non-model organisms (MacLeod et al., 2013; MacLeod et al., 2014).

Simulations can be useful at all stages of this work—for planning studies, analyzing data, testing inference methods, and validating findings from empirical and theoretical research. For instance, simulations provide training data for inference methods based on machine learning (Schrider and Kern, 2018) and Approximate Bayesian Computation (Csilléry et al., 2010). They can also serve as baselines for further analyses: for example, simulations incorporating demographic history serve as null models when detecting selection (Hsieh et al., 2016) or seed downstream breeding program simulations (Gaynor et al., 2021). More recently, population genomic simulations have been used to help guide conservation decisions for threatened species (Teixeira and Huber, 2021; Kyriazis et al., 2022).

Increasing amounts of data and the sophistication of inference methods have enabled researchers to ask ever more specific and precise questions. Consequently, simulations must incorporate more and more elements of biological realism. Important elements include genomic features such as mutation and recombination rates that strongly affect genetic variation and haplotype structure (Nachman, 2002). The inclusion of these genomic features is particularly important when linked selection is acting upon the patterns of genomic diversity being studied (Cutter and Payseur, 2013). Furthermore, the demographic history of a species—encompassing population sizes and distributions, divergences, and gene flow—can dramatically affect patterns of genomic variation (Teshima et al., 2006). Thus species-specific estimates of these and other ecological and evolutionary parameters (such as those governing the process of natural selection) are important when generating realistic simulations. This presents challenges, especially to new researchers, as it takes a great deal of specialized knowledge not only to code the simulations themselves but also to find and choose appropriate estimates of the parameters underlying the simulation model.

The recently developed community resource stdpopsim provides easy access to detailed population genomic simulations (Adrion et al., 2020). It lowers the technical barriers to performing these simulations and reduces the possibility of erroneous implementation of simulations for species with published demographic models. The initial release of stdpopsim was restricted to only six well-characterized model species, such as Drosophila melanogaster and Homo sapiens, but the feedback we received from the community identified a widespread desire to simulate a broader range of non-model species, and ideally to incorporate these into the stdpopsim catalog for future use. This feedback, and subsequent efforts to expand the catalog, also uncovered a vital need to better understand when it is practical to create a realistic simulation of a species of interest, and indeed what ‘realistic’ means in this context.

This paper reports on the updates made in the current release of stdpopsim (version 0.2), and is also intended as a resource for any researcher who wishes to develop chromosome-scale simulations for their own species of interest. We start by describing the central idea behind the standardized simulation framework of stdpopsim, and then outline the main updates made to the stdpopsim catalog and simulation framework in the past two years. We then provide guidelines for generating population genomic simulations, either for the purpose of using them in one specific study, or with the intent of making the simulations available for future work by adding the appropriate models to stdpopsim. Among other considerations, we discuss when a chromosome-scale simulation is more useful than simulations based on either individual loci or generic loci. We specify the required input data, mention common pitfalls in choosing appropriate parameters, and suggest courses of action for species that are missing estimates of some necessary inputs. We conclude with examples from two species recently added to stdpopsim, which demonstrate some of the main considerations involved in the process of designing realistic chromosome-scale simulations. While the guidelines provided in this paper are intended for any researcher interested in implementing a population genomic simulation using any software, we highlight the ways in which the stdpopsim framework eases the burden involved in this process and facilitates reproducible research.

Additions to stdpopsim

When first published, the stdpopsim catalog included six species: Homo sapiens, Pongo abelii, Canis familiaris, Drosophila melanogaster, Arabidopsis thaliana, and Escherichia coli (Figure 1). One way the catalog has expanded is through the introduction of additional demographic models for Homo sapiens, Pongo abelii, Drosophila melanogaster, and Arabidopsis thaliana, enabling a wider variety of simulations for these well-studied species. However, the initial collection of six species represents only a small slice of the tree of life. This is a concern not only because there is a large community of researchers studying other organisms, but also because methods developed for application to model species (such as humans) may not perform well when applied to other species with very different biology. Adding species to the stdpopsim catalog will allow developers to easily test their methods across a wider variety of organisms.

Figure 1

Download asset Open asset

Figure 1—source code 1

R code for generating the figure.

https://cdn.elifesciences.org/articles/84874/elife-84874-fig1-code1-v1.zip

Download elife-84874-fig1-code1-v1.zip

Figure 1—source code 2

Newick-format tree used as input in R code.

https://cdn.elifesciences.org/articles/84874/elife-84874-fig1-code2-v1.zip

Download elife-84874-fig1-code2-v1.zip

We thus made a concerted effort to recruit members of the population and evolutionary genetics community to add their species of interest to the stdpopsim catalog. This effort involved a series of workshops to introduce potential contributors to stdpopsim, followed by a ‘Growing the Zoo’ hackathon organized alongside the 2021 ProbGen conference. The seven initial workshops allowed us to reach a broad community of more than 150 researchers, many of whom expressed interest in adding non-model species to stdpopsim. The hackathon was then structured based on feedback from these participants. One month before the hackathon, we organized a final workshop to prepare interested participants, by introducing them to the process of developing a new species model and adding it to the stdpopsim code base. Roughly 20 scientists participated in the hackathon (most of whom are included as authors on this paper), which resulted in the addition of 15 species to the stdpopsim catalog (Figure 1). The catalog now includes a teleost fish (Gasterosteus aculeatus), a bird (Anas platyrhynchos), a reptile (Anolis carolinensis), a livestock species (Bos taurus), six insects including two vectors of human disease (Aedes aegypti and Anopheles gambiae), a nematode (Caenorhabditis elegans), two flowering plants including a crop (Helianthus annuus), an algae (Chlamydomonas reinhardtii), two bacteria, four primates, and a common mammalian associate of humans (Canis familiaris). Not all of these have recombination maps or demographic models (see Figure 1), but this lays a framework for future contributions.

Expanding the species catalog required adding several capabilities to the simulation framework of stdpopsim. Some features were added by upgrading the neutral simulation engine, msprime, from version 0.7.4 to version 1.0 (Baumdicker et al., 2021). Among other features, this upgrade includes a discrete-site model of mutation, which enables simulating sites with multiple mutations and possibly more than two alleles. Another key feature added to stdpopsim’s simulation framework was the ability to model non-crossover recombination. In bacteria and archaea, genetic material can be exchanged through horizontal gene transfer, which can add new genetic material (e.g. via the transfer of plasmids) or replace homologous sequences through homologous recombination (Thomas and Nielsen, 2005; Didelot and Maiden, 2010; Gophna and Altman-Price, 2022). However, the initial version of stdpopsim used crossover recombination to stand in for these processes. Although we cannot currently simulate varying gene content (as would be required to simulate the addition of new genetic material by horizontal gene transfer), the msprime and SLiM simulation engines now allow gene conversion, which has the same effect as non-crossover homologous recombination. Following Cury et al., 2022, we use this to include non-crossover homologous recombination in bacterial and archaeal species. This is done in stdpopsim by setting a flag in the species model to indicate that recombination should be modeled without crossovers and specifying an average tract length of exchanged genetic material. For example, the model for Escherichia coli has been updated in the stdpopsim catalog to use non-crossover recombination at an average rate of $8.9\times {10}^{-11}$ recombination events per base per generation, with an average tract length of 542 bases (Wielgoss et al., 2011; Didelot et al., 2012). Note that this rate ($8.9\times {10}^{-11}$) corresponds to the rate of initiation of a recombined tract.

Recombination without crossover is also prevalent in sexually reproducing species, where it is termed gene conversion. Gene conversion affects shorter segments than crossover recombination and creates distinct patterns of genetic diversity along the genome (Korunes and Noor, 2017). Indeed, gene conversion rates in some species are estimated to occur at similar or even higher rates than crossover recombination (Gay et al., 2007; Comeron et al., 2012; Wijnker et al., 2013). To accommodate this in stdpopsim simulations, one needs to specify the fraction of recombinations that occur due to gene conversion (i.e. without crossover), and the average tract length. For example, the model for Drosophila melanogaster has been updated in the stdpopsim catalog to have a fraction of gene conversions of 0.83 (in all chromosomes that undergo recombination) and an average tract length of 518 bases (Comeron et al., 2012). This update does not affect the rate of crossover recombination, but it adds gene conversion events at a ratio of 83:17 relative to crossover recombination events. We note that since non-crossover recombination incurs a high computation load in simulation, it is turned off by default in stdpopsim, and must be explicitly invoked by the simulation model. Note that ignoring gene conversion may result in a slightly skewed distribution of shared haplotypes between individuals (see Table 1).

Another important extension of stdpopsim allows augmenting a genome assembly with genome annotations, such as coding regions, promoters, and conserved elements. These annotations can be used to simulate selection at a subset of sites (such as the annotated coding regions) using parametric distributions of fitness effects. Standardized, easily accessible simulations that include the reality of pervasive linked selection in a species-specific manner has long been identified as a goal for evolutionary genetics (e.g. McVicker et al., 2009; Comeron, 2014). Thus, we expect this extension of stdpopsim to be transformative in the way simulations are carried out in population genetics. This significant new capability of the stdpopsim library will be detailed in a forthcoming publication, and is not the focus of this paper.

Anopheles gambiae (mosquito)

Anopheles gambiae, the African malaria mosquito, is a non-model organism whose population history has direct implications for human health. Several large-scale studies in recent years have provided information about the population history of this species on which population genomic simulations can be based (e.g. Miles, 2017; Clarkson et al., 2020). The genome assembly structure used in the species model is from the AgamP4 genome assembly (Sharakhova et al., 2007), downloaded directly from Ensembl (Howe et al., 2020) using the special utilities provided by stdpopsim.

Estimates of average recombination rates for each of the chromosomes (excluding the mitochondrial genome) were taken from a recombination map inferred by Pombi et al., 2006 which itself included information from Zheng et al., 1996; Figure 2A. As direct estimates of mutation rate (e.g. via mutation accumulation) do not currently exist for Anopheles gambiae, we used the genome-wide average mutation rate of $\mu =3.5\times {10}^{-9}$ mutations per generation per site estimated by Keightley et al., 2009 for the fellow Dipteran Drosophila melanogaster, a rate that was used for the analysis of A. gambiae data in Miles, 2017. To obtain an estimate for the default effective population size ($N_e$), we used the formula $\theta =4\mu N_e$, with the above mutation rate ($\mu =3.5\times {10}^{-9}$ mutations per base per generation) and a mean nucleotide diversity of $\theta \approx 0.015$, as reported by Miles, 2017 for the Gabon population. This resulted in an estimate of $N_e=1.07\times {10}^6$, which we rounded down to one million. These steps were documented in the code for the stdpopsim species model, to facilitate validation and future updates. We acknowledge that some of these steps involve somewhat arbitrary choices, such as the choice of the Gabon population and rounding down of the final value. However, this should not be seen as a considerable source of misspecification, since this value of $N_e$ is meant to provide only a rough approximation to historical population sizes and would be overwritten by a more detailed demographic model. Miles, 2017 inferred demographic models from Anopheles samples from nine different populations (locations) using the stairway plot method (Liu and Fu, 2015). We chose to include in stdpopsim the demographic model inferred from the Gabon sample, which consists of a single population whose size fluctuated from below 80,000 (an ancient bottleneck roughly 10,000 generations ago) to the present-day estimate of over 4 million individuals (Figure 2B). To convert the timescale from generations to years, we used an average generation time of 1/11 years, as in Miles, 2017.

Figure 2

Download asset Open asset

Figure 2—source code 1

R code for generating the figure.

https://cdn.elifesciences.org/articles/84874/elife-84874-fig2-code1-v1.zip

Download elife-84874-fig2-code1-v1.zip

All of these parameters were set in the species entry in the stdpopsim catalog, accompanied by the relevant citation information, and the model underwent the standard quality-control process. The species entry may be refined in the future by adding more demographic models, updating or refining the recombination map, or updating the mutation rate estimates based on ones directly estimated for this species. Note that even if the mutation rate is updated sometime in the future, the demographic model mentioned above should still be associated with the current mutation rate ($\mu =3.5\times {10}^{-9}$ mutations per base per generation), since this was the rate used in its inference.

The current manuscript is a computational study, which did not generate any new data. All source code files for stdpopsim and the species catalog are available at: https://github.com/popsim-consortium/stdpopsim, (copy archived at PopSim Consortium, 2023). The documentation page for the stdpopsim catalog is available at: https://popsim-consortium.github.io/stdpopsim-docs/stable/catalog.html. Figure 1 - Source code Files 1-2 and Figure 2 - Source Code File 1 contain the code and numerical data used to generate the figures.

1. 1. Adrion JR
   2. Cole CB
   3. Dukler N
   4. Galloway JG
   5. Gladstein AL
   6. Gower G
   7. Kyriazis CC
   8. Ragsdale AP
   9. Tsambos G
   10. Baumdicker F
   11. Carlson J
   12. Cartwright RA
   13. Durvasula A
   14. Gronau I
   15. Kim BY
   16. McKenzie P
   17. Messer PW
   18. Noskova E
   19. Ortega-Del Vecchyo D
   20. Racimo F
   21. Struck TJ
   22. Gravel S
   23. Gutenkunst RN
   24. Lohmueller KE
   25. Ralph PL
   26. Schrider DR
   27. Siepel A
   28. Kelleher J
   29. Kern AD

   (2020) A community-maintained standard library of population genetic models

   eLife 9:e54967.

   https://doi.org/10.7554/eLife.54967

   • PubMed
   • Google Scholar
2. 1. Amarasinghe SL
   2. Su S
   3. Dong X
   4. Zappia L
   5. Ritchie ME
   6. Gouil Q

   (2020) Opportunities and challenges in long-read sequencing data analysis

   Genome Biology 21:30.

   https://doi.org/10.1186/s13059-020-1935-5

   • PubMed
   • Google Scholar
3. 1. Amarasinghe SL
   2. Ritchie ME
   3. Gouil Q

   (2021) Long-Read-Tools.Org: an interactive catalogue of analysis methods for long-read sequencing data

   GigaScience 10:giab003.

   https://doi.org/10.1093/gigascience/giab003

   • PubMed
   • Google Scholar
4. 1. Baumdicker F
   2. Bisschop G
   3. Goldstein D
   4. Gower G
   5. Ragsdale AP
   6. Tsambos G
   7. Zhu S
   8. Eldon B
   9. Ellerman EC
   10. Galloway JG
   11. Gladstein AL
   12. Gorjanc G
   13. Guo B
   14. Jeffery B
   15. Kretzschumar WW
   16. Lohse K
   17. Matschiner M
   18. Nelson D
   19. Pope NS
   20. Quinto-Cortés CD
   21. Rodrigues MF
   22. Saunack K
   23. Sellinger T
   24. Thornton K
   25. van Kemenade H
   26. Wohns AW
   27. Wong Y
   28. Gravel S
   29. Kern AD
   30. Koskela J
   31. Ralph PL
   32. Kelleher J

   (2021) Efficient ancestry and Mutation simulation with Msprime 1.0

   Genetics 220:iyab229.

   https://doi.org/10.1093/genetics/iyab229

   • PubMed
   • Google Scholar
5. 1. Beichman AC
   2. Phung TN
   3. Lohmueller KE

   (2017) Comparison of single genome and allele frequency data reveals discordant demographic histories

   G3: Genes, Genomes, Genetics 7:3605–3620.

   https://doi.org/10.1534/g3.117.300259

   • PubMed
   • Google Scholar
6. 1. Beichman AC
   2. Huerta-Sanchez E
   3. Lohmueller KE

   (2018) Using Genomic data to infer historic population Dynamics of Nonmodel organisms

   Annual Review of Ecology, Evolution, and Systematics 49:433–456.

   https://doi.org/10.1146/annurev-ecolsys-110617-062431

   • Google Scholar
7. 1. Benzer S

   (1961) On the topography of the genetic fine structure

   PNAS 47:403–415.

   https://doi.org/10.1073/pnas.47.3.403

   • PubMed
   • Google Scholar
8. 1. Blischak PD
   2. Barker MS
   3. Gutenkunst RN
   4. Falush D

   (2020) Inferring the demographic history of inbred species from genome-wide SNP frequency data

   Molecular Biology and Evolution 37:2124–2136.

   https://doi.org/10.1093/molbev/msaa042

   • PubMed
   • Google Scholar
9. 1. Browning SR
   2. Browning BL

   (2015) Accurate non-parametric estimation of recent effective population size from segments of identity by descent

   American Journal of Human Genetics 97:404–418.

   https://doi.org/10.1016/j.ajhg.2015.07.012

   • PubMed
   • Google Scholar
10. 1. Cesarani A
    2. Lourenco D
    3. Tsuruta S
    4. Legarra A
    5. Nicolazzi EL
    6. VanRaden PM
    7. Misztal I

    (2022) Multibreed Genomic evaluation for production traits of dairy cattle in the United States using single-step Genomic best linear Unbiased Predictor

    Journal of Dairy Science 105:5141–5152.

    https://doi.org/10.3168/jds.2021-21505

    • PubMed
    • Google Scholar
11. 1. Chakraborty M
    2. Baldwin-Brown JG
    3. Long AD
    4. Emerson JJ

    (2016) Contiguous and accurate de novo assembly of Metazoan Genomes with modest long read coverage

    Nucleic Acids Research 44:e147.

    https://doi.org/10.1093/nar/gkw654

    • PubMed
    • Google Scholar
12. 1. Charlesworth B

    (2012) The effects of deleterious mutations on evolution at linked sites

    Genetics 190:5–22.

    https://doi.org/10.1534/genetics.111.134288

    • PubMed
    • Google Scholar
13. 1. Cheng S
    2. Melkonian M
    3. Smith SA
    4. Brockington S
    5. Archibald JM
    6. Delaux P-M
    7. Li F-W
    8. Melkonian B
    9. Mavrodiev EV
    10. Sun W
    11. Fu Y
    12. Yang H
    13. Soltis DE
    14. Graham SW
    15. Soltis PS
    16. Liu X
    17. Xu X
    18. Wong GK-S

    (2018) 10Kp: A Phylodiverse genome sequencing plan

    GigaScience 7:1–9.

    https://doi.org/10.1093/gigascience/giy013

    • PubMed
    • Google Scholar
14. 1. Clarkson CS
    2. Miles A
    3. Harding NJ
    4. Lucas ER
    5. Battey CJ
    6. Amaya-Romero JE
    7. Kern AD
    8. Fontaine MC
    9. Donnelly MJ
    10. Lawniczak MK

    (2020) Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles Gambiae and Anopheles Coluzzii

    Genome Research 30:1533–1546.

    https://doi.org/10.1101/gr.262790.120

    • Google Scholar
15. 1. Comeron JM
    2. Ratnappan R
    3. Bailin S

    (2012) The many landscapes of Recombination in Drosophila melanogaster

    PLOS Genetics 8:e1002905.

    https://doi.org/10.1371/journal.pgen.1002905

    • PubMed
    • Google Scholar
16. 1. Comeron JM

    (2014) Background selection as baseline for nucleotide variation across the Drosophila genome

    PLOS Genetics 10:e1004434.

    https://doi.org/10.1371/journal.pgen.1004434

    • PubMed
    • Google Scholar
17. 1. Csilléry K
    2. Blum MGB
    3. Gaggiotti OE
    4. François O

    (2010) Approximate Bayesian computation (ABC) in practice

    Trends in Ecology & Evolution 25:410–418.

    https://doi.org/10.1016/j.tree.2010.04.001

    • PubMed
    • Google Scholar
18. 1. Cury J
    2. Haller BC
    3. Achaz G
    4. Jay F

    (2022) Simulation of bacterial populations with slim

    Peer Community Journal 2:e7.

    https://doi.org/10.24072/pcjournal.72

    • Google Scholar
19. 1. Cutter AD
    2. Payseur BA

    (2013) Genomic signatures of selection at linked sites: unifying the disparity among species

    Nature Reviews. Genetics 14:262–274.

    https://doi.org/10.1038/nrg3425

    • PubMed
    • Google Scholar
20. 1. Darwin Tree of Life Project Consortium

    (2022) Sequence locally, think globally: the Darwin tree of life project

    PNAS 119:e2115642118.

    https://doi.org/10.1073/pnas.2115642118

    • PubMed
    • Google Scholar
21. 1. Dawson E
    2. Abecasis GR
    3. Bumpstead S
    4. Chen Y
    5. Hunt S
    6. Beare DM
    7. Pabial J
    8. Dibling T
    9. Tinsley E
    10. Kirby S
    11. Carter D
    12. Papaspyridonos M
    13. Livingstone S
    14. Ganske R
    15. Lõhmussaar E
    16. Zernant J
    17. Tõnisson N
    18. Remm M
    19. Mägi R
    20. Puurand T
    21. Vilo J
    22. Kurg A
    23. Rice K
    24. Deloukas P
    25. Mott R
    26. Metspalu A
    27. Bentley DR
    28. Cardon LR
    29. Dunham I

    (2002) A first-generation linkage disequilibrium map of human Chromosome 22

    Nature 418:544–548.

    https://doi.org/10.1038/nature00864

    • PubMed
    • Google Scholar
22. 1. Didelot X
    2. Maiden MCJ

    (2010) Impact of Recombination on bacterial evolution

    Trends in Microbiology 18:315–322.

    https://doi.org/10.1016/j.tim.2010.04.002

    • PubMed
    • Google Scholar
23. 1. Didelot X
    2. Méric G
    3. Falush D
    4. Darling AE

    (2012) Impact of Homologous and non-Homologous Recombination in the Genomic evolution of Escherichia coli

    BMC Genomics 13:256.

    https://doi.org/10.1186/1471-2164-13-256

    • PubMed
    • Google Scholar
24. 1. Eldon B
    2. Birkner M
    3. Blath J
    4. Freund F

    (2015) Can the site-frequency spectrum distinguish exponential population growth from multiple-merger Coalescents

    Genetics 199:841–856.

    https://doi.org/10.1534/genetics.114.173807

    • PubMed
    • Google Scholar
25. 1. Ellegren H
    2. Smith NGC
    3. Webster MT

    (2003) Mutation rate variation in the mammalian genome

    Current Opinion in Genetics & Development 13:562–568.

    https://doi.org/10.1016/j.gde.2003.10.008

    • PubMed
    • Google Scholar
26. 1. Ellegren H

    (2014) Genome sequencing and population Genomics in non-model organisms

    Trends in Ecology & Evolution 29:51–63.

    https://doi.org/10.1016/j.tree.2013.09.008

    • PubMed
    • Google Scholar
27. 1. Excoffier L
    2. Dupanloup I
    3. Huerta-Sánchez E
    4. Sousa VC
    5. Foll M

    (2013) Robust demographic inference from Genomic and SNP data

    PLOS Genetics 9:e1003905.

    https://doi.org/10.1371/journal.pgen.1003905

    • PubMed
    • Google Scholar
28. 1. Eyre-Walker A
    2. Keightley PD

    (2007) The distribution of fitness effects of new mutations

    Nature Reviews. Genetics 8:610–618.

    https://doi.org/10.1038/nrg2146

    • PubMed
    • Google Scholar
29. 1. Gaut BS
    2. Seymour DK
    3. Liu Q
    4. Zhou Y

    (2018) Demography and its effects on Genomic variation in crop Domestication

    Nature Plants 4:512–520.

    https://doi.org/10.1038/s41477-018-0210-1

    • PubMed
    • Google Scholar
30. 1. Gay J
    2. Myers S
    3. McVean G

    (2007) Estimating Meiotic Gene conversion rates from population genetic data

    Genetics 177:881–894.

    https://doi.org/10.1534/genetics.107.078907

    • PubMed
    • Google Scholar
31. 1. Gaynor RC
    2. Gorjanc G
    3. Hickey JM

    (2021) Alphasimr: an R package for breeding program Simulations

    G3: Genes, Genomes, Genetics 11:jkaa017.

    https://doi.org/10.1093/g3journal/jkaa017

    • PubMed
    • Google Scholar
32. 1. Gophna U
    2. Altman-Price N

    (2022) Horizontal Gene transfer in Archaea-from mechanisms to genome evolution

    Annual Review of Microbiology 76:481–502.

    https://doi.org/10.1146/annurev-micro-040820-124627

    • PubMed
    • Google Scholar
33. 1. Gower G
    2. Picazo PI
    3. Fumagalli M
    4. Racimo F

    (2021) Detecting adaptive Introgression in human evolution using Convolutional neural networks

    eLife 10:e64669.

    https://doi.org/10.7554/eLife.64669

    • PubMed
    • Google Scholar
34. 1. Gutenkunst RN
    2. Hernandez RD
    3. Williamson SH
    4. Bustamante CD

    (2009) Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data

    PLOS Genetics 5:e1000695.

    https://doi.org/10.1371/journal.pgen.1000695

    • PubMed
    • Google Scholar
35. 1. Haller BC
    2. Messer PW

    (2019) Slim 3: forward genetic Simulations beyond the Wright–Fisher model

    Molecular Biology and Evolution 36:632–637.

    https://doi.org/10.1093/molbev/msy228

    • PubMed
    • Google Scholar
36. Preprint

    1. Harland C
    2. Charlier C
    3. Karim L
    4. Cambisano N
    5. Deckers M
    6. Mni M
    7. Mullaart E
    8. Coppieters W
    9. Georges M

    (2017) Frequency of Mosaicism Points towards Mutation-Prone Early Cleavage Cell Divisions in Cattle

    bioRxiv.

    https://doi.org/10.1101/079863

    • Google Scholar
37. 1. Harris K
    2. Nielsen R

    (2013) Inferring demographic history from a spectrum of shared haplotype lengths

    PLOS Genetics 9:e1003521.

    https://doi.org/10.1371/journal.pgen.1003521

    • PubMed
    • Google Scholar
38. 1. Harris K
    2. Nielsen R

    (2016) The genetic cost of Neanderthal Introgression

    Genetics 203:881–891.

    https://doi.org/10.1534/genetics.116.186890

    • PubMed
    • Google Scholar
39. 1. Hartfield M
    2. Poulsen NA
    3. Guldbrandtsen B
    4. Bataillon T

    (2022) Using Singleton densities to detect recent selection in Bos Taurus

    Evolution Letters 5:595–606.

    https://doi.org/10.1002/evl3.263

    • PubMed
    • Google Scholar
40. 1. Heaton MP
    2. Smith TPL
    3. Bickhart DM
    4. Vander Ley BL
    5. Kuehn LA
    6. Oppenheimer J
    7. Shafer WR
    8. Schuetze FT
    9. Stroud B
    10. McClure JC
    11. Barfield JP
    12. Blackburn HD
    13. Kalbfleisch TS
    14. Davenport KM
    15. Kuhn KL
    16. Green RE
    17. Shapiro B
    18. Rosen BD
    19. Koepfli KP

    (2021) A reference genome assembly of Simmental cattle, Bos Taurus Taurus

    The Journal of Heredity 112:184–191.

    https://doi.org/10.1093/jhered/esab039

    • PubMed
    • Google Scholar
41. 1. Hinrichs AS
    2. Karolchik D
    3. Baertsch R
    4. Barber GP
    5. Bejerano G
    6. Clawson H
    7. Diekhans M
    8. Furey TS
    9. Harte RA
    10. Hsu F
    11. Hillman-Jackson J
    12. Kuhn RM
    13. Pedersen JS
    14. Pohl A
    15. Raney BJ
    16. Rosenbloom KR
    17. Siepel A
    18. Smith KE
    19. Sugnet CW
    20. Sultan-Qurraie A
    21. Thomas DJ
    22. Trumbower H
    23. Weber RJ
    24. Weirauch M
    25. Zweig AS
    26. Haussler D
    27. Kent WJ

    (2006) The UCSC genome browser database: update 2006

    Nucleic Acids Research 34:D590–D598.

    https://doi.org/10.1093/nar/gkj144

    • Google Scholar
42. 1. Howe KL
    2. Achuthan P
    3. Allen J
    4. Allen J
    5. Jorge Alvarez-Jarreta MRA
    6. Armean IM
    7. Azov AG
    8. Bennett R
    9. Bhai J
    10. Billis K
    11. Boddu S
    12. Charkhchi M
    13. Cummins C
    14. Fioretto LDR
    15. Davidson C
    16. Dodiya K
    17. Houdaigui BE
    18. Fatima R
    19. Gall A
    20. Giron CG
    21. Grego T
    22. Guijarro-Clarke C
    23. Haggerty L
    24. Hemrom A
    25. Hourlier T
    26. Izuogu OG
    27. Juettemann T
    28. Kaikala V
    29. Kay M
    30. Lavidas I
    31. Le T
    32. Lemos D
    33. Martinez JG
    34. Marugán JC
    35. Maurel T
    36. McMahon AC
    37. Mohanan S
    38. Moore B
    39. Muffato M
    40. Oheh DN
    41. Paraschas D
    42. Parker A
    43. Parton A
    44. Prosovetskaia I
    45. Sakthivel MP
    46. Salam AIA
    47. Schmitt BM
    48. Schuilenburg H
    49. Sheppard D
    50. Steed E
    51. Szpak M
    52. Szuba M
    53. Taylor K
    54. Jonathan MM
    55. Joanella M
    56. Emily P
    57. Magali R
    58. ohn T
    59. David T
    60. Stephen JT
    61. Fiona C
    62. Jane EL
    63. Fergal JM
    64. Nick L

    (2020) Anja Thormann

    Nucleic Acids Research 11:D1–D891.

    https://doi.org/10.1093/nar/gkaa942

    • Google Scholar
43. 1. Hsieh P
    2. Veeramah KR
    3. Lachance J
    4. Tishkoff SA
    5. Wall JD
    6. Hammer MF
    7. Gutenkunst RN

    (2016) Whole genome sequence analyses of Western central African Pygmy Hunter-Gatherers reveal a complex demographic history and identify candidate genes under positive natural selection

    Genome Research 26:279–290.

    https://doi.org/10.1101/gr.192971.115

    • PubMed
    • Google Scholar
44. 1. Hsieh P
    2. Dang V
    3. Vollger MR
    4. Mao Y
    5. Huang T-H
    6. Dishuck PC
    7. Baker C
    8. Cantsilieris S
    9. Lewis AP
    10. Munson KM
    11. Sorensen M
    12. Welch AE
    13. Underwood JG
    14. Eichler EE

    (2021) Evidence for opposing selective forces operating on human-specific duplicated Tcaf genes in Neanderthals and humans

    Nature Communications 12:5118.

    https://doi.org/10.1038/s41467-021-25435-4

    • PubMed
    • Google Scholar
45. 1. Huber CD
    2. Durvasula A
    3. Hancock AM
    4. Lohmueller KE

    (2018) Gene expression drives the evolution of dominance

    Nature Communications 9:2750.

    https://doi.org/10.1038/s41467-018-05281-7

    • PubMed
    • Google Scholar
46. Book

    1. Jukes TH
    2. Cantor CR

    (1969)

    Evolution of protein molecules

    In: Munro HN, editors. Mammalian Protein Metabolism. Academic Press. pp. 21–132.

    • Google Scholar
47. 1. Keightley PD
    2. Trivedi U
    3. Thomson M
    4. Oliver F
    5. Kumar S
    6. Blaxter ML

    (2009) Analysis of the genome sequences of three Drosophila melanogaster spontaneous Mutation accumulation lines

    Genome Research 19:1195–1201.

    https://doi.org/10.1101/gr.091231.109

    • PubMed
    • Google Scholar
48. 1. Kelleher J
    2. Etheridge AM
    3. McVean G

    (2016) Efficient Coalescent simulation and Genealogical analysis for large sample sizes

    PLOS Computational Biology 12:e1004842.

    https://doi.org/10.1371/journal.pcbi.1004842

    • PubMed
    • Google Scholar
49. 1. Korunes KL
    2. Noor MAF

    (2017) Gene conversion and linkage: effects on genome evolution and Speciation

    Molecular Ecology 26:351–364.

    https://doi.org/10.1111/mec.13736

    • PubMed
    • Google Scholar
50. 1. Kumar S
    2. Suleski M
    3. Craig JM
    4. Kasprowicz AE
    5. Sanderford M
    6. Li M
    7. Stecher G
    8. Hedges SB

    (2022) Timetree 5: an expanded resource for species divergence times

    Molecular Biology and Evolution 39:msac174.

    https://doi.org/10.1093/molbev/msac174

    • PubMed
    • Google Scholar
51. Preprint

    1. Kyriazis CC
    2. Robinson JA
    3. Lohmueller KE

    (2022) Using Computational Simulations to Quantify Genetic Load and Predict Extinction Risk

    bioRxiv.

    https://doi.org/10.1101/2022.08.12.503792

    • Google Scholar
52. 1. Larson G
    2. Burger J

    (2013) A population Genetics view of animal Domestication

    Trends in Genetics 29:197–205.

    https://doi.org/10.1016/j.tig.2013.01.003

    • PubMed
    • Google Scholar
53. 1. Lewin HA
    2. Richards S
    3. Lieberman Aiden E
    4. Allende ML
    5. Archibald JM
    6. Bálint M
    7. Barker KB
    8. Baumgartner B
    9. Belov K
    10. Bertorelle G
    11. Blaxter ML
    12. Cai J
    13. Caperello ND
    14. Carlson K
    15. Castilla-Rubio JC
    16. Chaw S-M
    17. Chen L
    18. Childers AK
    19. Coddington JA
    20. Conde DA
    21. Corominas M
    22. Crandall KA
    23. Crawford AJ
    24. DiPalma F
    25. Durbin R
    26. Ebenezer TE
    27. Edwards SV
    28. Fedrigo O
    29. Flicek P
    30. Formenti G
    31. Gibbs RA
    32. Gilbert MTP
    33. Goldstein MM
    34. Graves JM
    35. Greely HT
    36. Grigoriev IV
    37. Hackett KJ
    38. Hall N
    39. Haussler D
    40. Helgen KM
    41. Hogg CJ
    42. Isobe S
    43. Jakobsen KS
    44. Janke A
    45. Jarvis ED
    46. Johnson WE
    47. Jones SJM
    48. Karlsson EK
    49. Kersey PJ
    50. Kim J-H
    51. Kress WJ
    52. Kuraku S
    53. Lawniczak MKN
    54. Leebens-Mack JH
    55. Li X
    56. Lindblad-Toh K
    57. Liu X
    58. Lopez JV
    59. Marques-Bonet T
    60. Mazard S
    61. Mazet JAK
    62. Mazzoni CJ
    63. Myers EW
    64. O’Neill RJ
    65. Paez S
    66. Park H
    67. Robinson GE
    68. Roquet C
    69. Ryder OA
    70. Sabir JSM
    71. Shaffer HB
    72. Shank TM
    73. Sherkow JS
    74. Soltis PS
    75. Tang B
    76. Tedersoo L
    77. Uliano-Silva M
    78. Wang K
    79. Wei X
    80. Wetzer R
    81. Wilson JL
    82. Xu X
    83. Yang H
    84. Yoder AD
    85. Zhang G

    (2022) The earth Biogenome project 2020: starting the clock

    PNAS 119:e2115635118.

    https://doi.org/10.1073/pnas.2115635118

    • PubMed
    • Google Scholar
54. 1. Li H
    2. Durbin R

    (2011) Inference of human population history from individual whole-genome sequences

    Nature 475:493–496.

    https://doi.org/10.1038/nature10231

    • PubMed
    • Google Scholar
55. 1. Liu X
    2. Fu YX

    (2015) Corrigendum: exploring population size changes using SNP frequency spectra

    Nature Genetics 47:1099.

    https://doi.org/10.1038/ng0915-1099a

    • PubMed
    • Google Scholar
56. 1. Ma X
    2. Kelley JL
    3. Eilertson K
    4. Musharoff S
    5. Degenhardt JD
    6. Martins AL
    7. Vinar T
    8. Kosiol C
    9. Siepel A
    10. Gutenkunst RN
    11. Bustamante CD

    (2013) Population Genomic analysis reveals a rich Speciation and demographic history of Orang-Utans (Pongo Pygmaeus and Pongo Abelii)

    PLOS ONE 8:e77175.

    https://doi.org/10.1371/journal.pone.0077175

    • PubMed
    • Google Scholar
57. 1. Ma L
    2. O’Connell JR
    3. VanRaden PM
    4. Shen B
    5. Padhi A
    6. Sun C
    7. Bickhart DM
    8. Cole JB
    9. Null DJ
    10. Liu GE
    11. Da Y
    12. Wiggans GR

    (2015) Cattle sex-specific Recombination and genetic control from a large pedigree analysis

    PLOS Genetics 11:e1005387.

    https://doi.org/10.1371/journal.pgen.1005387

    • PubMed
    • Google Scholar
58. 1. MacLeod IM
    2. Larkin DM
    3. Lewin HA
    4. Hayes BJ
    5. Goddard ME

    (2013) Inferring demography from runs of Homozygosity in whole-genome sequence, with correction for sequence errors

    Molecular Biology and Evolution 30:2209–2223.

    https://doi.org/10.1093/molbev/mst125

    • PubMed
    • Google Scholar
59. 1. MacLeod IM
    2. Hayes BJ
    3. Goddard ME

    (2014) The effects of demography and long-term selection on the accuracy of Genomic prediction with sequence data

    Genetics 198:1671–1684.

    https://doi.org/10.1534/genetics.114.168344

    • PubMed
    • Google Scholar
60. 1. Makanjuola BO
    2. Miglior F
    3. Abdalla EA
    4. Maltecca C
    5. Schenkel FS
    6. Baes CF

    (2020) Effect of Genomic selection on rate of inbreeding and Coancestry and effective population size of Holstein and Jersey cattle populations

    Journal of Dairy Science 103:5183–5199.

    https://doi.org/10.3168/jds.2019-18013

    • PubMed
    • Google Scholar
61. 1. McVicker G
    2. Gordon D
    3. Davis C
    4. Green P

    (2009) Widespread Genomic signatures of natural selection in Hominid evolution

    PLOS Genetics 5:e1000471.

    https://doi.org/10.1371/journal.pgen.1000471

    • PubMed
    • Google Scholar
62. 1. Meuwissen THE
    2. Hayes BJ
    3. Goddard ME

    (2001) Prediction of total genetic value using genome-wide dense marker maps

    Genetics 157:1819–1829.

    https://doi.org/10.1093/genetics/157.4.1819

    • PubMed
    • Google Scholar
63. 1. Miles A

    (2017) Genetic diversity of the African malaria vector Anopheles Gambiae

    Nature 552:96–100.

    https://doi.org/10.1038/nature24995

    • PubMed
    • Google Scholar
64. 1. Montano V

    (2016) Valeria montano.Coalescent inferences in conservation Genetics: should the exception become the rule

    Biology Letters 12:20160211.

    https://doi.org/10.1098/rsbl.2016.0211

    • PubMed
    • Google Scholar
65. 1. Nachman MW

    (2002) Variation in Recombination rate across the genome: evidence and implications

    Current Opinion in Genetics & Development 12:657–663.

    https://doi.org/10.1016/s0959-437x(02)00358-1

    • PubMed
    • Google Scholar
66. 1. Navascués M
    2. Emerson BC

    (2009) Elevated substitution rate estimates from ancient DNA: model violation and bias of Bayesian methods

    Molecular Ecology 18:4390–4397.

    https://doi.org/10.1111/j.1365-294X.2009.04333.x

    • PubMed
    • Google Scholar
67. 1. Nelson D
    2. Kelleher J
    3. Ragsdale AP
    4. Moreau C
    5. McVean G
    6. Gravel S

    (2020) Accounting for long-range correlations in genome-wide Simulations of large cohorts

    PLOS Genetics 16:e1008619.

    https://doi.org/10.1371/journal.pgen.1008619

    • PubMed
    • Google Scholar
68. 1. Obšteter J
    2. Jenko J
    3. Gorjanc G

    (2021) Genomic selection for any dairy breeding program via Optimized investment in Phenotyping and Genotyping

    Frontiers in Genetics 12:637017.

    https://doi.org/10.3389/fgene.2021.637017

    • PubMed
    • Google Scholar
69. 1. Pombi M
    2. Stump AD
    3. Della Torre A
    4. Besansky NJ

    (2006) Variation in recombination rate across the X chromosome of Anopheles Gambiae

    The American Journal of Tropical Medicine and Hygiene 75:901–903.

    https://doi.org/10.4269/ajtmh.2006.75.901

    • PubMed
    • Google Scholar
70. Software

    1. PopSim Consortium

    (2023) Stdpopsim, version swh:1:rev:a4ddf93ebca6d50d432a5df989c328a8ec9697ab

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:abeccf48a6f21774e94d1ac6a52735ffe4f8882d;origin=https://github.com/popsim-consortium/stdpopsim;visit=swh:1:snp:e302192d3073f614e88313b6d8a2ce58b49f3d37;anchor=swh:1:rev:a4ddf93ebca6d50d432a5df989c328a8ec9697ab
71. 1. Ragsdale AP
    2. Nelson D
    3. Gravel S
    4. Kelleher J

    (2020) Lessons learned from bugs in models of human history

    American Journal of Human Genetics 107:583–588.

    https://doi.org/10.1016/j.ajhg.2020.08.017

    • PubMed
    • Google Scholar
72. 1. Rhie A
    2. McCarthy SA
    3. Fedrigo O
    4. Damas J
    5. Formenti G
    6. Koren S
    7. Uliano-Silva M
    8. Chow W
    9. Fungtammasan A
    10. Kim J
    11. Lee C
    12. Ko BJ
    13. Chaisson M
    14. Gedman GL
    15. Cantin LJ
    16. Thibaud-Nissen F
    17. Haggerty L
    18. Bista I
    19. Smith M
    20. Haase B
    21. Mountcastle J
    22. Winkler S
    23. Paez S
    24. Howard J
    25. Vernes SC
    26. Lama TM
    27. Grutzner F
    28. Warren WC
    29. Balakrishnan CN
    30. Burt D
    31. George JM
    32. Biegler MT
    33. Iorns D
    34. Digby A
    35. Eason D
    36. Robertson B
    37. Edwards T
    38. Wilkinson M
    39. Turner G
    40. Meyer A
    41. Kautt AF
    42. Franchini P
    43. Detrich HW
    44. Svardal H
    45. Wagner M
    46. Naylor GJP
    47. Pippel M
    48. Malinsky M
    49. Mooney M
    50. Simbirsky M
    51. Hannigan BT
    52. Pesout T
    53. Houck M
    54. Misuraca A
    55. Kingan SB
    56. Hall R
    57. Kronenberg Z
    58. Sović I
    59. Dunn C
    60. Ning Z
    61. Hastie A
    62. Lee J
    63. Selvaraj S
    64. Green RE
    65. Putnam NH
    66. Gut I
    67. Ghurye J
    68. Garrison E
    69. Sims Y
    70. Collins J
    71. Pelan S
    72. Torrance J
    73. Tracey A
    74. Wood J
    75. Dagnew RE
    76. Guan D
    77. London SE
    78. Clayton DF
    79. Mello CV
    80. Friedrich SR
    81. Lovell PV
    82. Osipova E
    83. Al-Ajli FO
    84. Secomandi S
    85. Kim H
    86. Theofanopoulou C
    87. Hiller M
    88. Zhou Y
    89. Harris RS
    90. Makova KD
    91. Medvedev P
    92. Hoffman J
    93. Masterson P
    94. Clark K
    95. Martin F
    96. Howe K
    97. Flicek P
    98. Walenz BP
    99. Kwak W
    100. Clawson H
    101. Diekhans M
    102. Nassar L
    103. Paten B
    104. Kraus RHS
    105. Crawford AJ
    106. Gilbert MTP
    107. Zhang G
    108. Venkatesh B
    109. Murphy RW
    110. Koepfli KP
    111. Shapiro B
    112. Johnson WE
    113. Di Palma F
    114. Marques-Bonet T
    115. Teeling EC
    116. Warnow T
    117. Graves JM
    118. Ryder OA
    119. Haussler D
    120. O’Brien SJ
    121. Korlach J
    122. Lewin HA
    123. Howe K
    124. Myers EW
    125. Durbin R
    126. Phillippy AM
    127. Jarvis ED

    (2021) Towards complete and error-free genome Assemblies of all vertebrate species

    Nature 592:737–746.

    https://doi.org/10.1038/s41586-021-03451-0

    • PubMed
    • Google Scholar
73. 1. Robinson J
    2. Kyriazis CC
    3. Yuan SC
    4. Lohmueller KE

    (2023) Deleterious variation in natural populations and implications for conservation Genetics

    Annual Review of Animal Biosciences 11:93–114.

    https://doi.org/10.1146/annurev-animal-080522-093311

    • PubMed
    • Google Scholar
74. 1. Rosen BD
    2. Bickhart DM
    3. Schnabel RD
    4. Koren S
    5. Elsik CG
    6. Tseng E
    7. Rowan TN
    8. Low WY
    9. Zimin A
    10. Couldrey C
    11. Hall R
    12. Li W
    13. Rhie A
    14. Ghurye J
    15. McKay SD
    16. Thibaud-Nissen F
    17. Hoffman J
    18. Murdoch BM
    19. Snelling WM
    20. McDaneld TG
    21. Hammond JA
    22. Schwartz JC
    23. Nandolo W
    24. Hagen DE
    25. Dreischer C
    26. Schultheiss SJ
    27. Schroeder SG
    28. Phillippy AM
    29. Cole JB
    30. Van Tassell CP
    31. Liu G
    32. Smith TPL
    33. Medrano JF

    (2020) Medrano.De Novo assembly of the cattle reference genome with single-molecule sequencing

    GigaScience 9:giaa021.

    https://doi.org/10.1093/gigascience/giaa021

    • PubMed
    • Google Scholar
75. 1. Schiffels S
    2. Wang K

    (2020) MSMC and MSMC2: the multiple Sequentially Markovian Coalescent

    Methods in Molecular Biology 2090:147–166.

    https://doi.org/10.1007/978-1-0716-0199-0_7

    • PubMed
    • Google Scholar
76. 1. Schrider DR
    2. Kern AD

    (2018) Supervised machine learning for population Genetics: A new paradigm

    Trends in Genetics 34:301–312.

    https://doi.org/10.1016/j.tig.2017.12.005

    • PubMed
    • Google Scholar
77. 1. Schrider DR

    (2020) Background selection does not Mimic the patterns of genetic diversity produced by selective Sweeps

    Genetics 216:499–519.

    https://doi.org/10.1534/genetics.120.303469

    • PubMed
    • Google Scholar
78. 1. Sharakhova MV
    2. Hammond MP
    3. Lobo NF
    4. Krzywinski J
    5. Unger MF
    6. Hillenmeyer ME
    7. Bruggner RV
    8. Birney E
    9. Collins FH

    (2007) Update of the Anopheles Gambiae PEST genome assembly

    Genome Biology 8:R5.

    https://doi.org/10.1186/gb-2007-8-1-r5

    • PubMed
    • Google Scholar
79. 1. Supek F
    2. Lehner B

    (2019) Scales and mechanisms of somatic Mutation rate variation across the human genome

    DNA Repair 81:102647.

    https://doi.org/10.1016/j.dnarep.2019.102647

    • PubMed
    • Google Scholar
80. 1. Talenti A
    2. Powell J
    3. Hemmink JD
    4. Cook EAJ
    5. Wragg D
    6. Jayaraman S
    7. Paxton E
    8. Ezeasor C
    9. Obishakin ET
    10. Agusi ER
    11. Tijjani A
    12. Amanyire W
    13. Muhanguzi D
    14. Marshall K
    15. Fisch A
    16. Ferreira BR
    17. Qasim A
    18. Chaudhry U
    19. Wiener P
    20. Toye P
    21. Morrison LJ
    22. Connelley T
    23. Prendergast JGD

    (2022) A cattle graph genome incorporating global breed diversity

    Nature Communications 13:2983.

    https://doi.org/10.1038/s41467-022-30372-x

    • PubMed
    • Google Scholar
81. 1. Teixeira JC
    2. Huber CD

    (2021) The inflated significance of neutral genetic diversity in conservation Genetics

    PNAS 118:1–10.

    https://doi.org/10.1073/pnas.2015096118

    • PubMed
    • Google Scholar
82. 1. Tennessen JA
    2. Bigham AW
    3. O’Connor TD
    4. Fu W
    5. Kenny EE
    6. Gravel S
    7. McGee S
    8. Do R
    9. Liu X
    10. Jun G
    11. Kang HM
    12. Jordan D
    13. Leal SM
    14. Gabriel S
    15. Rieder MJ
    16. Abecasis G
    17. Altshuler D
    18. Nickerson DA
    19. Boerwinkle E
    20. Sunyaev S
    21. Bustamante CD
    22. Bamshad MJ
    23. Akey JM
    24. Broad GO
    25. Seattle GO
    26. NHLBI Exome Sequencing Project

    (2012) Evolution and functional impact of rare coding variation from deep sequencing of human Exomes

    Science 337:64–69.

    https://doi.org/10.1126/science.1219240

    • PubMed
    • Google Scholar
83. 1. Teshima KM
    2. Coop G
    3. Przeworski M

    (2006) How reliable are empirical Genomic scans for selective Sweeps

    Genome Research 16:702–712.

    https://doi.org/10.1101/gr.5105206

    • PubMed
    • Google Scholar
84. 1. Thomas CM
    2. Nielsen KM

    (2005) Mechanisms of, and barriers to, horizontal Gene transfer between bacteria

    Nature Reviews. Microbiology 3:711–721.

    https://doi.org/10.1038/nrmicro1234

    • PubMed
    • Google Scholar
85. 1. VanRaden PM

    (2020) Symposium review: how to implement Genomic selection

    Journal of Dairy Science 103:5291–5301.

    https://doi.org/10.3168/jds.2019-17684

    • PubMed
    • Google Scholar
86. 1. Wielgoss S
    2. Barrick JE
    3. Tenaillon O
    4. Cruveiller S
    5. Chane-Woon-Ming B
    6. Médigue C
    7. Lenski RE
    8. Schneider D

    (2011) Mutation rate inferred from synonymous substitutions in a long-term evolution experiment with Escherichia coli

    G3: Genes, Genomes, Genetics 1:183–186.

    https://doi.org/10.1534/g3.111.000406

    • PubMed
    • Google Scholar
87. 1. Wijnker E
    2. Velikkakam James G
    3. Ding J
    4. Becker F
    5. Klasen JR
    6. Rawat V
    7. Rowan BA
    8. de Jong DF
    9. de Snoo CB
    10. Zapata L
    11. Huettel B
    12. de Jong H
    13. Ossowski S
    14. Weigel D
    15. Koornneef M
    16. Keurentjes JJ
    17. Schneeberger K

    (2013) The Genomic landscape of Meiotic Crossovers and Gene conversions in Arabidopsis thaliana

    eLife 2:e01426.

    https://doi.org/10.7554/eLife.01426

    • PubMed
    • Google Scholar
88. 1. Zheng L
    2. Benedict MQ
    3. Cornel AJ
    4. Collins FH
    5. Kafatos FC

    (1996) An integrated genetic map of the African human malaria vector mosquito, Anopheles Gambiae

    Genetics 143:941–952.

    https://doi.org/10.1093/genetics/143.2.941

    • PubMed
    • Google Scholar
89. 1. Zhou Y
    2. Tian X
    3. Browning BL
    4. Browning SR

    (2018) Popdemog: Visualizing population demographic history from simulation scripts

    Bioinformatics 34:2854–2855.

    https://doi.org/10.1093/bioinformatics/bty184

    • PubMed
    • Google Scholar

The following is the authors' response to the original reviews.

We are very glad that the editor and reviewers found our paper of broad interest to the community of population, evolutionary, and ecological genetics. We thank them for their positive feedback and insightful comments and suggestions. We have revised our manuscript to address some of the issues raised by the review. The main change we made was providing a detailed discussion of limitations of simulated genomes, focusing on considerations one needs to make when selecting a demographic model. This can be found in a new section “Limitations of simulated genomes” (pages 9-10). We made a few additional adjustments in other parts of the text based on the reviewers’ suggestions. They are all listed in the detailed point-by-point response to reviewers comments and questions below.

Editor:

1. It was noted that demographic models (or genomic parameters) that are inferred based on certain aspects of the genomic data (eg., site frequency spectrum, haplotype structure) may not recapitulate other aspects of the data. In other words, any inferred demographic models are expected to reliably reproduce only some aspects of the genetic variation data but not necessarily all. It would be helpful to emphasize this limitation in the manuscript and to include a table summarizing the types of variation that the demographic models for the catalogued species were based on.

This is a very important point, which we addressed in the revision by adding a section entitled “Limitations of simulated genomes”. This section discusses the considerations that one should make when selecting an inferred demographic model to implement in simulation. This includes the samples used in analysis, the method used for inference, as well as various filters. In this section we also point to the documentation page of the stdpopsim catalog, which provides information about each demographic model that can help users decide whether it is appropriate for their needs. We decided not to summarize this information in a succinct table in the manuscript because it is not straightforward to summarize the strengths and potential limitations of each model in a table. Instead, we will expand the summary provided for each demographic model in the documentation page to provide additional information. See response to the second reviewer’s comment on this topic for more details.

2. It will make stdpopsim more user-friendly to include an automated module that can visualize a demographic model given the corresponding parameters (or simulation scripts).

As mentioned in the response to the first reviewer’s comment on this subject, the documentation page of the stdpopsim catalog provides a brief summary for each demographic model, including a graphical representation. See response below for more details.

Reviewer #1:

In the introduction, the authors cite numerous efforts to generate high-quality reference genomes. That's not an issue in itself, but leading with this might send the message to some readers that it is these reference genome efforts that are driving the need for population genomics analysis and simulation tools, which is not really the case - why not instead give some citation attention to actual population genomics projects aiming to address the types of evolutionary questions this paper is concerned with? The reference genome citations would fit better in the section dealing with reference genomes, where they already appear.

Indeed, the desire to answer complex evolutionary questions is the main motivation for sequencing these genomes and also for generating realistic genome simulations. The reason we chose to lead with the genome-sequencing efforts is that high quality genome data is an important prerequisite for obtaining parameters for chromosome-scale simulations. So, with that perspective, these efforts which we cite are the driving force behind expansion of stdpopsim in the near future. Thus, we decided to leave these citations in the introduction. To balance things out, we now start the introduction with a statement about board questions in population genetics. Moreover, after we list the genome sequencing efforts, we added a list of specific types of questions that can be addressed by these newly emerging genomes, with relevant citations. The beginning of the introduction now reads:

“Population genetics allows us to answer questions across scales from deep evolutionary time to ongoing ecological dynamics, and dramatic reductions in sequencing costs enable the generation of unprecedented amounts of genomic data that can be used to address these questions (Ellegren, 2014). Ongoing efforts to systematically sequence life on Earth by initiatives such as the Earth Biogenome (Lewin et al., 2022) and its affiliated project networks, such as Vertebrate Genomes (Rhie et al., 2021), 10,000 Plants (Cheng et al., 2018) and others (Darwin Tree of Life Project Consortium, 2022), are providing the backbone for enormous increases in the amount of population-level genomic data available for model and non-model species. These data are being used, among other things, in inference of population history and demographic parameters (Beichman et al., 2018), studying adaptive introgression (Gower et al., 2021), distinguishing adaptation from drift (e.g. Hsieh et al., 2021), and understanding the implications of deleterious variation in populations of conservation concern (e.g. Robinson et al., 2023).”

Something that would be useful for the stdpopsim resource in general, though not necessarily something for the paper, would be some kind of more human-friendly representation of the demographic models implemented in the curated library. Perhaps I'm not looking in the right place, but as far as I can tell, if I want to study the curated demographic models, I need to go into the Python scripts on the stdpopsim GitHub page (e.g. https://github.com/popsim-consortium/stdpopsim/tree/main/stdpopsim/catalog/BosTau). Here the various parameters and demographic events are hard-coded into the scripts. To understand the model being implemented, one thus needs to go dig into these scripts - something which is not necessarily very accessible to all researchers. Visual representations, such as the one for Anopheles gambiae in Fig 2. in the paper, are more widely accessible. I wonder if such figures could be produced for all the curated models and included in the GitHub folders alongside the scripts, perhaps aided by an existing model visualization software such as POPdemog. Again, I would not suggest that this is necessary for the paper, but if practically feasible I think it would be a useful addition to the resource in the longer term.

This is a very good point. The stdpopsim catalog actually has a documentation page that provides a brief summary for each demographic model, including a graphical representation. This graphical representation is generated using demesdraw applied to the demographic model object implemented in the code. Thus, potential users do not have to dig through the Python code to figure out the details of the demographic model. We used a similar approach to generate the image of the demographic history of A. gambiae for Fig. 2 of the paper. The documentation page is an important part of the stdpopsim catalog, and we now added a link to it in section “Data availability”, and we mention it in key places in the manuscript, such as the caption of Fig 2.

Reviewer #2:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

It would be helpful to provide additional information regarding the coding annotation (and associated distribution of fitness effects and linked selection) that is implemented in the current version of stdpopsim, but will be detailed in a forthcoming paper. This is not to take away from the forthcoming paper, but I believe this is the most important update to the software, and the current manuscript only brushes over it.

We agree that implementation of selection in simulations is a significant addition to stdpopsim. However, our intention in this manuscript is to focus on the separate effort we made in the last two years to expand the utility of stdpopsim to a more diverse set of species. We think the manuscript stands firmly even without discussing in detail the new features that allow modeling selection. The main reason we briefly mention these features in sections “Additions to stdpopsim” and “Basic setup for chromosome-level simulations” is because the released version of stdpopsim contains implemented DFEs for a few species, and we did not want to completely ignore this. We thus added a brief comment at the end of the “Basic setup” section (page 8) mentioning the three model species for which the stdpopsim catalog currently has annotations and implemented DFE models. We think that a more detailed description of how these features and how they should be used is best left to the manuscript that the PopSim community is currently writing (preprint expected later this year).

When it comes to simulating realistic genomic data, the authors clearly lay out that parameters obtained from the literature must be compatible, such as the same recombination and mutation rates used to infer a demographic history should also be used within stdpopsim if employing that demographic history for simulation. This is a highly important point, which is often overlooked. However, it is also important that readers understand that depending on the method used to estimate the demographic history, different demographic models within stdpopsim may not reproduce certain patterns of genetic variation well. The authors do touch on this a bit, providing the example that a constant size demographic history will be unable to capture variation expected from recent size changes (e.g., excess of low-frequency alleles). However, depending on the data used to estimate a demographic history, certain types of variation may be unreliably modeled (Biechman et al. 2017; G3, 7:3605-3620). For example, if a site frequency spectrum method was used to estimate a demographic history, then the simulations under this model from y stdpopsim may not recapitulate the haplotype structure well in the observed species. Similarly, if a method such as PSMC applied to a single diploid genome was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the site frequency spectrum well in the observed species. Though the authors indicate that citations are given to each demographic model and model parameter for each species, this may not be sufficient for a novice researcher in this field to understand what forms of genomic variation the models may be capable of reliably producing. A potential worry is that the inclusion of a species within stdpopsim may serve as an endorsement to users regarding the available simulation models (though I understand this is not the case by the authors), and it would be helpful if users and readers were guided on the type of variation the models should be able to reliably reproduce for each species and demographic history available for each species. It would be helpful to include a table with types of observed variation that the current set of 21 species (and associated demographic histories) are likely and unlikely to recapitulate well.

This is a very important point, which we now address in the section “Limitations of simulated genomes”, which we added to the manuscript. In this section, we expand on this topic and discuss various things that will affect the way simulated genomes reflect true sequence variation. This includes the choice of demographic inference method, but also the analyzed samples, and various filters. The main message of this section is that one should consider various things when deciding to implement a demographic model in simulation (or selecting a model among those implemented in stdpopsim). We also cite studies (including Beichman, et al. 2017), which compared different approaches to demography inference. However, we note that the conclusions of these comparisons are not as straightforward as the reviewer suggests. In particular, methods that make use of the site frequency spectrum (such as dadi) should be able to capture some aspects of haplotype structure, because this information is encoded in the demographic history. Furthermore, a demographic history inferred from a single genome (e.g., using PSMC) should do a reasonable job approximating some aspects of the site frequency spectrum. In other words, the aspects of genetic variation not modeled well by a given demographic inference method are not always predicted in a straightforward way. This is why we avoid summarizing this information in a table in the manuscript. The 2nd paragraph of the “Limitations of simulated genomes” section addresses some of these subtle considerations. In particular, we suggest that considering a demographic model for simulation requires some familiarity with the inference method and the way it was applied to data. Regarding the demographic models currently implemented in stdpopsim, we provide some information about each model in the documentation page of the catalog. When selecting a demographic model from the catalog, users should make use of this documentation to guide their decision. This is mentioned in the 3rd paragraph of the “Limitations of simulated genomes” section. Following-up on this issue, we intend to review the documentation and make sure it provides sufficient information for each demographic model. See this GitHub issue.

Reviewer #3:

- p5, 2nd paragraph: I think many Biologists, myself included, will think of horizontal gene transfer mostly as plasmids being transferred among bacteria and adding extra genetic material, not as homologous bacterial recombination. This made me confused about modelling horizontal gene transfer in the same way as gene conversion. It may be helpful for some readers if you specify that you are modelling this particular type of horizontal gene transfer. Some explanation along the lines of what is in Cury et al (2022) would be enough.

This is a good point. We modified the text in that sentence in the 2nd paragraph on page 5 to clarify that we are modeling non-crossover homologous recombination, and not incorporation of exogenous DNA (e.g., via plasmid transfer). The relevant part of the text now says:

“In bacteria and archaea, genetic material can be exchanged through horizontal gene transfer, which can add new genetic material (e.g., via the transfer of plasmids) or replace homologous sequences through homologous recombination (Thomas and Nielsen, 2005; Didelot and Maiden, 2010; Gophna and Altman-Price, 2022). However, the initial version of stdpopsim used crossover recombination to stand in for these processes. Although we cannot currently simulate varying gene content (as would be required to simulate the addition of new genetic material by horizontal gene transfer), the msprime and SLiM simulation engines now allow gene conversion, which has the same effect as non-crossover homologous recombination.

Following (Cury et al., 2022), we use this to include non-crossover homologous recombination in bacterial and archaeal species.”

- p5, 3rd paragraph: When you say gene conversion is turned off by default, you could refer to table 1 and briefly mention the consequence of ignoring gene conversion.

We agree that it is important to note that avoiding to model gene conversion may lead to faulty lengths of shared haplotypes across individuals. This is implied by the statement we make in the beginning of the 3rd paragraph on page 5, where we lay out the motivation for modeling gene conversion in simulation. Following the reviewer’s suggestion, we now added a statement about this in the end of that paragraph:

“Note that ignoring gene conversion may result in a slightly skewed distribution of shared haplotypes between individuals (see Table 1)”

- p7, item 1 and p9, 1st paragraph: I am not sure what you mean by genetic map here, can you define this term? I am not sure if it is synonymous with gene annotations, a recombination map, or something else. The linkage map doesn't seem to make sense to me here.

The term ‘genetic map’ referred to the recombination map whenever it was used in the manuscript. To avoid any confusion, we now removed all mentions of ‘genetic map’, and use ‘recombination map’ instead. The recombination map is relevant in item 1 of page 7 because in species with poor assemblies you will not be able to reliably estimate recombination maps, making chromosome-scale simulations less effective. In the 1st paragraph of page 9, we discuss the issue of lifting over coordinates from one assembly to another, and if you have a recombination map estimated in one assembly, you might need to lift it over to another assembly to apply it in your simulation.

- Table 1, last row, middle column: when you say "simulated population", I think it is a bit ambiguous. You mean "the true population that we are trying to simulate", but could be read as "the population data that was generated by simulation". I would delete the word simulated here.

What we mean here is that the selected effective population size should reflect the observed genetic diversity in real genomic data. We realize that the previous wording was confusing, and changed this to the following:

“Set the effective population size (Ne) to a value that reflects the observed genetic diversity”

- Figure 2, and other places when you refer to mutation and recombination rate (eg p11, last paragraph), can you include the units (e.g. per base pair, per generation)?

Throughout the manuscript, rates are always specified per base per generation. In Figure 2, this is specified in the caption (3rd line). We added units in other places in section “Examples of added species” on pages 12-13, where they were indeed missing.

- p11, "default effective population size": can you use a more descriptive word instead of the default? Maybe the historical average? Also, what is this value used for in the simulations when there is a demographic model specified (as in the case of Anopheles)?

We think that “default effective population size” is the most appropriate term to use here, since we are referring to the parameter in the species model in stdpopsim. It is correct that the value of this parameter should reflect the historical average size in some sense, but it is really unclear what this should be in the case of a species like Bos taurus, which experienced a very dramatic bottleneck in the recent past. We address this subtle, yet important, issue in the sentence preceding this one. If a demographic model is specified in simulation, it overrides the default effective population size, and its value is ignored (which is why we refer to it as ‘default’). We added a short sentence clarifying this in the 2nd paragraph of the “Bos Taurus” section (now page 12).

“Note that the default Ne is only used in simulation if a demographic model is not specified.”

- p8, when you say "Such simulations are useful for a number of purposes, but they cannot be used to model the influence of natural selection on patterns of genetic variation.": You may want to bring up the discussion that many of these neutral parameters taken from the literature could have been estimated assuming genome-wide neutrality, and thus ignoring the effect of background selection. Therefore the parameter values might reflect some effect of background selection that was unaccounted for during their estimation.

This is an important subtle point, which we now address in the section “Limitations of simulated genomes”, which we added to the revised manuscript. In that section, we discuss various limitations of simulations, focusing on inferred demographic models. We address the potential influence of the segments selected for analysis toward the end of 2nd paragraph in that section (page 9):

“... all methods assume that the input sequences are neutrally evolving. This implies that technical choices, such as the specific genomic segments analyzed and various filters, may also influence the inferred model and its ability to model observed genetic variation.”

Interestingly, background selection in itself typically does not have a strong effect on the inferred model. This is something that is examined in the forthcoming publication that presents simulations with natural selection in stdpopsim.

- Why are some concepts written in bold (eg effective population size, demographic model)? Were you planning to make a vocabulary box? I think this is a good idea given that you are aiming for a public that can include people who are not very familiar with some population genetics concepts.

In the “Examples of added species” section, we use boldface fonts to highlight the model parameters that were determined for each species. We added a statement clarifying this in the beginning of this section (page 11), and made sure that all the relevant parameters were consistently highlighted throughout this section. In other sections, we use boldface fonts only for titles. A few cases that did not conform to this rule were removed in the current version. We did not intend on adding a vocabulary box, but considered this when revising the manuscript, due to the reviewer’s suggestion. However, we found it difficult to converge on a small (yet comprehensive) set of terms with accurate and succinct definitions. We think that the important terms are adequately defined within the text of the manuscript, providing sufficient information also for readers who are not expert population geneticists.

- p4, 2nd paragraph: Are these automated scripts that are used to compare models publicly available? If you are suggesting that people use this approach generally when coming up with a simulation model (p8, penultimate paragraph), it would be helpful to have access to these automated scripts.

The scripts are part of the public stdpopsim repository on GitHub, and may be used by anyone. Some components of these scripts are more easy to apply in general, such as comparing a demographic model with one implemented separately by the reviewer. This step, for example, is achieved by application of the Demography.is_equivalent method in msprime. Other parts of the comparison depend on the specific structure of python objects used by stdpopsim, so they are not likely to be useful when implementing simulations outside the framework of stdpopsim.

- p9, 1st paragraph, and p.12 2nd paragraph: instead of adjusting the mutation rate to fit the demographic model (and using an old estimate of the mutation rate), would it be ok to adjust the demographic model to fit the new mutation rate? E.g. with a new mutation rate that is the double of a previous estimate, would it be ok to just divide Ne by 2 such that Ne*mu is constant (in a constant population size model)? I imagine this could get complicated with population size changes.

In principle, this could be done if you were simulating neutrally evolving sequences (without modeling natural selection). Since the coalescence is scale-free, then you can scale down all population sizes and divergence times by a multiplicative factor, and scale up migration rates and the mutation rate by the same factor, and you get the exact same distribution over the output sequences. However, making sure you get the scaling right is tricky and is quite error-prone. Especially considering the fact that you have to do this every time the mutation rate of a species is updated. Moreover, once you start modeling natural selection, this scale-free property no longer holds. Thus, the simple solution we came up with in stdpopsim is to attach to each demographic model the mutation rate used in its inference.

Author details

1. M Elise Lauterbur

   Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States

   Contribution

   Conceptualization, Resources, Software, Investigation, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   lauterbur@email.arizona.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7362-3618

2. Maria Izabel A Cavassim

   Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Conceptualization, Resources, Software, Investigation, Writing – original draft

   Contributed equally with

   Ariella L Gladstein, Graham Gower, Nathaniel S Pope and Georgia Tsambos

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9726-1431

3. Ariella L Gladstein

   Embark Veterinary, Inc, Boston, United States

   Contribution

   Resources, Software, Methodology

   Contributed equally with

   Maria Izabel A Cavassim, Graham Gower, Nathaniel S Pope and Georgia Tsambos

   Competing interests

   is an employee of Embark Veterinary, Inc. The author declares that no other competing interests exist

   "This ORCID iD identifies the author of this article:" 0000-0001-7735-2336

4. Graham Gower

   Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Resources, Software, Methodology

   Contributed equally with

   Maria Izabel A Cavassim, Ariella L Gladstein, Nathaniel S Pope and Georgia Tsambos

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6197-3872

5. Nathaniel S Pope

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Resources, Software, Methodology

   Contributed equally with

   Maria Izabel A Cavassim, Ariella L Gladstein, Graham Gower and Georgia Tsambos

   Competing interests

   No competing interests declared

6. Georgia Tsambos

   School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia

   Contribution

   Resources, Software, Methodology

   Contributed equally with

   Maria Izabel A Cavassim, Ariella L Gladstein, Graham Gower and Nathaniel S Pope

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7001-2275

7. Jeffrey Adrion

   1. Institute of Ecology and Evolution, University of Oregon, Eugene, United States
   2. Ancestry DNA, San Francisco, United States

   Contribution

   Resources, Software, Methodology

   Competing interests

   is an employee of Ancestry DNA. The author declares that no other competing interests exist

   "This ORCID iD identifies the author of this article:" 0000-0003-1021-6000

8. Saurabh Belsare

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Resources, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8148-1867

9. Arjun Biddanda

   54Gene, Inc, Washington, United States

   Contribution

   Resources, Software

   Competing interests

   is an employee of 54Gene, Inc. The author declares that no other competing interests exist

   "This ORCID iD identifies the author of this article:" 0000-0003-1861-1523

10. Victoria Caudill

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0577-5513

11. Jean Cury

    Universite Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numerique, Orsay, France

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

12. Ignacio Echevarria

    School of Life Sciences, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

13. Benjamin C Haller

    Department of Computational Biology, Cornell University, Ithaca, United States

    Contribution

    Resources, Software, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1874-8327

14. Ahmed R Hasan

    1. Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
    2. Department of Biology, University of Toronto Mississauga, Mississauga, Canada

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0002-8399

15. Xin Huang

    1. Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
    2. Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

16. Leonardo Nicola Martin Iasi

    Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

17. Ekaterina Noskova

    Computer Technologies Laboratory, ITMO University, St Petersburg, Russian Federation

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1168-0497

18. Jana Obsteter

    Agricultural Institute of Slovenia, Department of Animal Science, Ljubljana, Slovenia

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

19. Vitor Antonio Correa Pavinato

    Entomology Department, The Ohio State University, Wooster, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2483-1207

20. Alice Pearson

    1. Department of Genetics, University of Cambridge, Cambridge, United Kingdom
    2. Department of Zoology, University of Cambridge, Cambridge, United Kingdom

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

21. David Peede

    1. Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, United States
    2. Center for Computational Molecular Biology, Brown University, Providence, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

22. Manolo F Perez

    Department of Genetics and Evolution, Federal University of Sao Carlos, Sao Carlos, Brazil

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

23. Murillo F Rodrigues

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

24. Chris CR Smith

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

25. Jeffrey P Spence

    Department of Genetics, Stanford University School of Medicine, Stanford, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3199-1447

26. Anastasia Teterina

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

27. Silas Tittes

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4697-7434

28. Per Unneberg

    Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

29. Juan Manuel Vazquez

    Department of Integrative Biology, University of California, Berkeley, Berkeley, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8341-2390

30. Ryan K Waples

    Department of Biostatistics, University of Washington, Seattle, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

31. Anthony Wilder Wohns

    Broad Institute of MIT and Harvard, Cambridge, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

32. Yan Wong

    Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3536-6411

33. Franz Baumdicker

    Cluster of Excellence - Controlling Microbes to Fight Infections, Eberhard Karls Universit¨at Tubingen, Tubingen, Germany

    Contribution

    Resources, Software, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

34. Reed A Cartwright

    School of Life Sciences and The Biodesign Institute, Arizona State University, Tempe, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0837-9380

35. Gregor Gorjanc

    The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Resources, Software, Writing – review and editing

    Competing interests

    No competing interests declared

36. Ryan N Gutenkunst

    Department of Molecular and Cellular Biology, University of Arizona, Tucson, United States

    Contribution

    Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8659-0579

37. Jerome Kelleher

    Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom

    Contribution

    Resources, Software, Supervision, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7894-5253

38. Andrew D Kern

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States

    Contribution

    Software, Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4381-4680

39. Aaron P Ragsdale

    Department of Integrative Biology, University of Wisconsin–Madison, Madison, United States

    Contribution

    Resources, Software

    Competing interests

    No competing interests declared

40. Peter L Ralph

    1. Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    2. Department of Mathematics, University of Oregon, Eugene, United States

    Contribution

    Software, Supervision, Methodology, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9459-6866

41. Daniel R Schrider

    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5249-4151

42. Ilan Gronau

    Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    ilan.gronau@runi.ac.il

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8536-4062

• 250

  Page views

• 33

  Downloads

• 1

  Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.