Messer Lab – SLiM

About SLiM

SLiM is an evolutionary simulation framework that combines a powerful engine for population genetic simulations with the capability of modeling arbitrarily complex evolutionary scenarios. Simulations are configured via the integrated Eidos scripting language that allows interactive control over practically every aspect of the simulated evolutionary scenarios. The underlying individual-based simulation engine is highly optimized to enable modeling of entire chromosomes in large populations. We also provide a graphical user interface on macOS and Linux for easy simulation set-up, interactive runtime control, and dynamical visualization of simulation output.

A 4–5 day SLiM Workshop is now available online.  The SLiM Workshop is also offered in person from time to time; see the SLiM Workshops subsection below for more information.

Downloads (version 3.6)

icon_package_small icon_zip_small icon_pdf_small icon_pdf_small icon_zip_small

macOS Installer

Source Code

SLiM Manual

Eidos Manual

Ref Sheets

The macOS Installer will install the slim command-line tool, the SLiMgui graphical development environment, and both manuals. (If you are on macOS and you wish to run the old Cocoa-based SLiMguiLegacy app instead, for some reason, you can download it from or build it in Xcode; it is no longer included in the installer.)  On other Un*x platforms, you should follow the instructions in the SLiM manual (chapter 2); there may be an installer for your platform, or you may need to build from sources. SLiM can also run on Windows, under the WSL; see the manual for details.

The SLiM Manual includes a collection of recipes for common situations. You can download a zip archive of those recipes, if you wish; they are also directly available through SLiMgui’s File menu.  The Eidos Manual covers the details of the Eidos language, used to control SLiM via scripting. Reference sheets for both SLiM and Eidos make them easy to use with minimal use of the full documentation.

Note that the source code archive provided here contains neither macOS specific code, nor the Xcode project for SLiM; it is intended for users on Linux (macOS users are strongly encouraged to use the macOS Installer rather than building from sources). The complete sources including macOS files can be found on GitHub; you can get the sources for a tagged release, such as 3.6, or for the current development head.

We also provide a GitHub repository called SLiM-Extras with additional useful tidbits for users of SLiM, such as user-defined Eidos functions for performing some common tasks, and we welcome contributions to that repository from others.


With the SLiMgui graphical modeling environment (compatible with macOS, Linux, and Windows under the WSL), you can visualize your simulation as it runs and examine its parameters in real-time, allowing for much easier simulation development.


A screenshot of SLiMgui simulating the population dynamics of a CRISPR/Cas9 “gene drive” in a six-subpopulation island model with spatial variation in selection on the driver allele.  The Eidos scripting area is on the left, output is on the right.  A visual representation of the population structure is shown in the subwindow, and all of the individuals in the six subpopulations can be seen at top (colored according to their relative fitness).  The central black bar shows a summary of the genetic variation present in the population; rare neutral mutations are visible as short yellow bars, and the tall red bar represents the driver allele approaching fixation.

Mailing lists

There are two mailing lists. Please subscribe to either or both using the links below:

  1. slim-announce: A low-volume mailing list for announcements only.  Users may not post to this list.
  2. slim-discuss: A higher-volume mailing list for questions, comments, bug reports, and discussion among users of SLiM.

SLiM Workshops

We run 5-day SLiM workshops that are free and open to the public (with registration). At present, however, these workshops are on hold due to the coronavirus pandemic.

The workshop materials are now online, for people who want to do it themselves; visit the online workshop download page for more information.

Information on attending SLiM workshops
Downloadable solutions to workshop exercises

Past workshops:

The first SLiM workshop: Umeå, Sweden, August 2019.


2021 March 3: SLiM 3.6 is released! This is a major release, with many new features and several important bug fixes. This upgrade is strongly recommended for all users; version 3.5 should not be used due to two bugs that could cause incorrect model results. For more information, see slim-announce for the announcement and release notes.

2020 December 8: SLiM 3.5 is released! This is a major release, with many new features and several important bug fixes. This upgrade is recommended for all users, but it breaks backward compatibility/reproducibility in several ways; read the release notes carefully. For more information, see slim-announce for the announcement and release notes.

2020 May 12: SLiM 3.4 is released! This is a major release, including the first release of QtSLiM, as well as a few other new features and fixes for a couple of bugs. This upgrade is recommended for all users. For more information, see slim-announce as usual.

2020 January 30: SLiM 3.3.2 is released! This is a minor release, with several small improvements and fixes for a couple of bugs (one significant). This upgrade is recommended for all users. For more information, see slim-announce as usual.

2020 January 13–17: We had a SLiM workshop on our home turf, at Cornell! We’ve done a couple so far (Sweden, the UK, New York City) but this was our first at Cornell. We had 28 attendees, and it went very well, including a catered lunch sponsored by 3CPG. We’ve got more workshops planned (see the previous subsection); please contact us if you’d like to host a workshop at your institution!

2019 September 28: SLiM 3.3.1 is released! This version is a minor release, mostly bug fixes (including a few rare but nasty bugs!). This upgrade is recommended for all users. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2019 May 15: SLiM 3.3 is released! This version is a major release, with big new features (nucleotide-based models, mutation() callbacks, etc.) and some big, important bug fixes. This upgrade is strongly recommended for all users. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2019 January 29: SLiM 3.2.1 is released! This version is a minor release, providing some smaller feature additions and a couple of new recipes, as well as bug fixes. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2019 January 18: In recent days we have had three (!) new papers published related to SLiM. (1) “SLiM 3: Forward genetic simulations beyond the Wright–Fisher model” describes the support for non-Wright–Fisher models and continuous space in SLiM 3 (DOI). (2) “Evolutionary modeling in SLiM 3 for beginners” walks new users through making a simple model in SLiM 3, with lots of explanations of basic concepts (DOI). (3) “Tree‐sequence recording in SLiM opens new horizons for forward‐time simulation of whole genomes” discusses the new tree-sequence recording feature in SLiM 3 in detail, with several examples of its practical application to speeding up model execution, burn-in simulation, and data analysis (DOI). We hope these papers are useful for getting up to speed on all the new stuff we’ve been working on!

2018 November 6: SLiM 3.2 is released! This version greatly improves the performance of large nonWF models, and provides numerous improvements and bug fixes. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2018 September 3: SLiM 3.1 is released! This version greatly improves the performance of spatial interactions, and provides improvements to tree-sequence recording, among other improvements and bug fixes. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2018 July 1: SLiM 3.0 is released! This is our first full version upgrade since SLiM 2.0 was released in early 2016. It adds support for non-Wright-Fisher (nonWF) models and tree-sequence recording, two features that greatly increase SLiM’s power and flexibility. Many smaller improvements have been made too. For more information, see the new manuals, and the release notes in the announcement on slim-announce.

2017 December 16: SLiM 2.6 is released! This is a major release, with lots of new stuff; see the new manuals, and the release notes in the announcement on slim-announce.

2017 October 27: SLiM 2.5 is released! This is a major release, with lots of new stuff and some important bug fixes; see the new manuals, and the release notes in the announcement on slim-announce.

2017 September 12: SLiM 2.4.2 is released (fix for a bug that could cause incorrect output from many/most models).

2017 July 26: SLiM 2.4.1 is released (fix for a bug that could cause crashes or incorrect results in multi-subpopulation simulations).

2017 July 14: SLiM 2.4 is released! This is a major release, with speed improvements for many types of models, new support for runtime profiling in SLiMgui, and many other features. See the new SLiM and Eidos manuals for current documentation. There are also release notes in the announcement on slim-announce.

2017 April 18: SLiM 2.3 is released! This is a major release, notably adding support for continuous space and spatial interactions. See the new SLiM and Eidos manuals for current documentation. There are also release notes in the announcement on slim-announce.

2017 February 22: SLiM 2.2.1 is released (new recipes, minor features and fixes).

2016 December 8: SLiM 2.2 is released! This is a major release with big performance improvements and several new features. See the new SLiM and Eidos manuals for current documentation. There are also release notes in the announcement on slim-announce.

2016 November 8: SLiM 2.1.1 is released (mostly bug fixes, some serious).

2016 October 4: Our publication on SLiM 2 is out in Molecular Biology and Evolution: DOI.

2016 September 19: SLiM 2.1 is released! This is a major release with lots of features added. See the new SLiM and Eidos manuals for current documentation. There are also release notes in the announcement on slim-announce.

2016 May 26: SLiM 2.0.4 is released (minor bug fixes).

2016 May 12: SLiM 2.0.3 is released (improvements to code completion).

2016 May 6: SLiM 2.0.2 is released (minor feature additions).

2016 April 27: SLiM 2.0.1 is released (minor bug fix and minor feature addition).

2016 April 1: SLiM 2.0 is released!  We are excited to announce SLiM 2.0, a new major release of the SLiM package.  SLiM 2.0 adds scriptability with Eidos, and interactive simulation development using the SLiMgui application.  We have put up a blog post with more details about the SLiM 2.0 release.

License and citation

SLiM is free open-source software, licensed under the GNU General Public License version 3. It runs on any Un*x or Linux machine that can build C++11 code, and works well on computing clusters.
If you use SLiM in your research, please cite us. For SLiM 3, cite:

Haller, B.C., & Messer, P.W. (2019). SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Molecular Biology and Evolution 36(3), 632–637.  DOI

For models using tree-sequence recording, cite:

Haller, B.C., Galloway, J., Kelleher, J., Messer, P.W., & Ralph, P.L. (2019). Tree‐sequence recording in SLiM opens new horizons for forward‐time simulation of whole genomes. Molecular Ecology Resources 19(2), 552–566.  DOI

If appropriate, cite our “protocol” paper for beginning SLiM users:

Haller, B.C., & Messer, P.W. (2019). Evolutionary modeling in SLiM 3 for beginners. Molecular Biology and Evolution 36(5), 1101–1109.  DOI

For older models using SLiM 2, cite:

Haller, B.C., & Messer, P.W. (2017). SLiM 2: Flexible, interactive forward genetic simulations. Molecular Biology and Evolution 34(1), 230–240.  DOI

And for SLiM 1, cite:

Messer, P.W. (2013). SLiM: Simulating evolution with selection and linkage. Genetics 194(4), 1037–1039.  DOI

The SLiM icon

Graphics files for the SLiM icon are downloadable here. These images are: Copyright (c) 2016–2019 Philipp Messer, All Rights Reserved. Permission is hereby granted for re-use specifically for SLiM-related purposes that are respectful and supportive of the SLiM community. If you have any doubt as to this, please contact us for permission. Various sizes and formats are provided.

256×256, TIFF format: SLiM_256.tif
256×256, JPEG format: SLiM_256.jpg
1024×1024, TIFF format: SLiM_1024.tif
1024×1024, JPEG format: SLiM_1024.jpg
Vector, PDF format: SLiM_vector.pdf

Older versions

Old versions of SLiM are available for download here, although for new projects using the current version is strongly recommended. These old versions are no longer supported.

SLiM 1.8:   documentation   source


Please note that we have stopped updating this list, as it became too time-consuming.  Here are examples of publications that have used SLiM (in alphabetical order within year). Please let us know if you have any additions you would like to see included in this list.


  • Booker, T.R., & Keightley, P.D. (2018). Understanding the factors that shape patterns of nucleotide diversity in the house mouse genome. Molecular Biology & Evolution (early online).  DOI
  • Champer, J., Liu, J., Oh, S.Y., Reeves, R., Luthra, A., Oakes, N., Clark, A.G., & Messer, P.W. (2018). Reducing resistance allele formation in CRISPR gene drive. Proceedings of the National Academy of Sciences 115(21), 5522-5527.  DOI
  • Champer, J., Zhao, J., Champer, S., Liu, J., Messer, P.W. (2018). Population dynamics of underdominance gene drive systems in continuous space. bioRxiv, 449355.  DOI
  • Cheng, X., & DeGiorgio, M. (2018). Detection of shared balancing selection in the absence of trans-species polymorphism. bioRxiv, 320390.  DOI
  • Fijarczyk, A., Dudek, K., Niedzicka, M., & Babik, W. (2018). Balancing selection and introgression of newt immune-response genes. Proc. R. Soc. B 285(1884), 1-9.  DOI
  • Gazal, S., Loh, P.R., Finucane, H., Ganna, A., Schoech, A., Sunyaev, S., & Price, A. (2018). Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations. bioRxiv, 297572.  DOI
  • Guo, J., Wu, Y., Zhu, Z., Zheng, Z., Trzaskowski, M., Zeng, J., Robinson, M.R., Visscher, P.M., & Yang, J. (2018). Global genetic differentiation of complex traits shaped by natural selection in humans. Nature Communications 9, 1865.  DOI
  • Haller, B.C., Galloway, J., Kelleher, J., Messer, P.W., & Ralph, P.L. (2018). Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. bioRxiv, 407783.  DOI
  • Haller, B.C., & Messer, P.W. (2018). SLiM 3: Forward genetic simulations beyond the Wright-Fisher model. bioRxiv, 418657.  DOI
  • Hämälä, T., & Savolainen, O. (2018). Local adaptation under gene flow: Recombination, conditional neutrality and genetic trade-offs shape genomic patterns in Arabidopsis lyrata. bioRxiv, 374900.  DOI
  • Harris, A.M., & DeGiorgio, M. (2018). Identifying and classifying shared selective sweeps from multilocus data. bioRxiv, 446005.  DOI
  • Harris, A.M., Garud, N.R., & DeGiorgio, M. (2018). Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity. Genetics (early online).  DOI
  • Harris, R., Sackman, A., & Jensen, J.D. (2018). On the unfounded enthusiasm for soft selective sweeps II: examining recent evidence from humans, flies, and viruses. bioRxiv, 443051.  DOI
  • Henden, L., Lee, S., Mueller, I., Barry, A., & Bahlo, M. (2018). Identity-by-descent analyses for measuring population dynamics and selection in recombining pathogens. PLoS Genetics 14(5): e1007279.  DOI
  • Johnson, K.E., & Voight, B.F. (2018). Patterns of shared signatures of recent positive selection across human populations. Nature Ecology & Evolution 2, 713-720.  DOI
  • Kim, B.Y., Huber, C.D., & Lohmueller, K.E. (2018). Deleterious variation shapes the genomic landscape of introgression. PLoS Genetics 14(10), 1-30.  DOI
  • Kiwoong, N.A.M., Nhim, S., Robin, S., Bretaudeau, A., & Negre, N. (2018). Genomic differentiation is initiated without physical linkage among targets of divergent selection in Fall armyworms. bioRxiv, 452870.  DOI
  • de Koning, A.J., & De Sanctis, B.D. (2018). The Rate of Observable Molecular Evolution When Mutation May Not Be Weak. bioRxiv, 259507.  DOI
  • Kosheleva, K., & Desai, M.M. (2018). Recombination Alters the Dynamics of Adaptation on Standing Variation in Laboratory Yeast Populations. Molecular Biology and Evolution 35(1), 180–201.  DOI
  • Li, H., & Ralph, P. (2018). Local PCA shows how the effect of population structure differs along the genome. bioRxiv, 070615.  DOI
  • Librado, P., & Orlando, L. (2018). Detecting Signatures of Positive Selection along Defined Branches of a Population Tree Using LSD. Molecular Biology and Evolution 35(6), 1520-1535.  DOI
  • Moon, J.M., Aronoff, D.M., Capra, J.A., Abbot, P., & Rokas, A. (2018). Examination of Signatures of Recent Positive Selection on Genes Involved in Human Sialic Acid Biology. G3: Genes, Genomes, Genetics 8(4), 1315-1325.  DOI
  • Mooney, J., Huber, C., Sul, J.H., Marsden, C., Zhang, Z., Sabatti, C., Ruiz-Linares, A., Bedoy, G., Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Freimer, N., & Lohmueller, K. E. (2018). Understanding the Hidden Complexity of Latin American Population Isolates. bioRxiv, 340158.  DOI
  • Parada, J.L.C., & Charlesworth, B. (2018). The effects on neutral variability of recurrent selective sweeps and background selection. bioRxiv, 358309.  DOI
  • Patel, R., Sanderford, M.D., Lanham, T.R., Tamura, K., Platt, A., Glicksberg, B.S., Xu, K., Dudley, J.T., & Kumar, S. (2018). Adaptive landscape of protein variation in human exomes. Molecular Biology and Evolution 35(8), 2015-2025.  DOI
  • Petr, M., Pääbo, S., Kelso, J., & Vernot, B. (2018). The limits of long-term selection against Neandertal introgression. bioRxiv, 362566.  DOI
  • Pouyet, F., Aeschbacher, S., Thiéry, A., & Excoffier, L. (2018). Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences. Elife 7, e36317.  DOI
  • Refoyo-Martínez, A., da Fonseca, R.R., Halldórsdóttir, K., Árnason, E., Mailund, T., & Racimo, F. (2018). Identifying loci under positive selection in complex population histories. bioRxiv, 453092.  DOI
  • Robinson, J.A., Brown, C., Kim, B.Y., Lohmueller, K.E., & Wayne, R.K. (2018). Purging of Strongly Deleterious Mutations Explains Long-Term Persistence and Absence of Inbreeding Depression in Island Foxes. Current Biology 28(21), 3487-3494.  DOI
  • Rougemont, Q., Carrier, A., Leluyer, J., Ferchaud, A.-L., Farrell, J., Hatin, D., Brodeur, P., & Bernatchez, L. (2018). Combining population genomics and forward simulations to investigate stocking impacts: A case study of Muskellunge (Esox masquinongy) from the St. Lawrence River basin. bioRxiv, 363283.  DOI
  • Rousselle, M., Mollion, M., Nabholz, B., Bataillon, T., & Galtier, N. (2018). Overestimation of the adaptive substitution rate in fluctuating populations. Biology Letters 14(5), 1-5.  DOI
  • Sato, D.X., & Kawata, M. (2018). Positive and balancing selection on SLC18A1 gene associated with psychiatric disorders and human‐unique personality traits. Evolution Letters 2(5), 499-510.  DOI
  • Sugden, L.A., Atkinson, E.G., Fischer, A.P., Rong, S., Henn, B.M., & Ramachandran, S. (2018). Localization of adaptive variants in human genomes using averaged one-dependence estimation. Nature Communications 9(1), 703.  DOI
  • Tennessen, J.A. (2018). Gene buddies: Linked balanced polymorphisms reinforce each other even in the absence of epistasis. PeerJ 6, e5110.  DOI
  • Zeng, J., de Vlaming, R., Wu, Y., Robinson, M.R., Lloyd-Jones, L.R., Yengo, L., Yap, C.X., Xue, A., Sidorenko, J., McRae, A.F., Powell, J.E., Montgomery, G.W., Metspalu, A., Esko, T., Gibson, G., Wray, N.R., Visscher, P.M., & Yang, J. (2018). Signatures of negative selection in the genetic architecture of human complex traits. Nature Genetics 50, 746-753.  DOI


  • Antelope, C.X., Marnetto, D., Casey, F., & Huerta-Sanchez, E. (2017). Leveraging Multiple Populations across Time Helps Define Accurate Models of Human Evolution: A Reanalysis of the Lactase Persistence Adaptation. Human Biology 89(1), 81-97.  DOI
  • Bay, R.A., & Ruegg, K. (2017). Genomic islands of divergence or opportunities for introgression? Proc. R. Soc. B 284(1850), 1-9.  DOI
  • Booker, T.R., Ness, R.W., & Keightley, P.D. (2017). The recombination landscape in wild house mice inferred using population genomic data. Genetics 207(1), 297-309.  DOI
  • Cahill, J.A., Heintzman, P.D., Harris, K., Teasdale, M., Kapp, J., Soares, A.E.R., Stirling, I., Bradley, D., Edwards, C.J., Kisleika, A.A., Malev, A.V., Monaghan, N., Green, R.E., Shapiro, B. (2017). Genomic evidence of globally widespread admixture from polar bears into brown bears during the last ice age. bioRxiv, 154773.  DOI
  • Cheng, X., Xu, C., & DeGiorgio, M. (2017). Fast and robust detection of ancestral selective sweeps. Molecular Ecology 26(24), 6871-6891.  DOI
  • Comeron, J. M. (2017). Background selection as null hypothesis in population genomics: insights and challenges from Drosophila studies. Phil. Trans. R. Soc. B 372(1736), 1-13.  DOI
  • Eichstaedt, C.A., Pagani, L., Antao, T., Inchley, C.E., Cardona, A., Mörseburg, A., Clemente, F.J., Sluckin, T.J., Metspalu, E., Mitt, M., Mägi, R., Hudjashov, G., Metspalu, M., Mormina, M., Jacobs, G.S., & Kivisild, T. (2017). Evidence of Early-Stage Selection on EPAS1 and GPR126 Genes in Andean High Altitude Populations. Scientific Reports 7(1), 1-9.  DOI
  • Gazal, S., Finucane, H.K., Furlotte, N.A., et al. (2017). Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nature Genetics 49(10), 1421-1427.  DOI
  • Haller, B.C., & Messer, P.W. (2017). asymptoticMK: A web-based tool for the asymptotic McDonald–Kreitman test. G3: Genes, Genomes, Genetics 7(5), 1569-1575.  DOI
  • Huber, C.D., Kim, B.Y., Marsden, C.D., & Lohmuller, K.E. (2017). Determining the factors driving selective effects of new nonsynonymous mutations. PNAS 114(17), 4465–4470.  DOI
  • Kardos, M., Qvarnstrom, A., & Ellegren, H. (2017). Inferring individual inbreeding and demographic history from segments of identity by descent in Ficedula flycatcher genome sequences. Genetics 205(3), 1319-1334.  DOI
  • Kim, B.Y., Huber, C.D., & Lohmueller, K.E. (2017). Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples. Genetics 206(1), 345-361.  DOI
  • Laenen, B., Tedder, A., Nowak, M.D., et al. (2017). Demography and mating system shape the genome-wide impact of purifying selection in Arabis alpinabioRxiv, 127209.  DOI
  • Lange, J.D., & Pool, J.E. (2017). Impacts of recurrent hitchhiking on divergence and demographic inference in DrosophilabioRxiv, 187633.  DOI
  • Librado, P., Gamba, C., Gaunitz, C., et al. (2017). Ancient genomic changes associated with domestication of the horse. Science 356(6336), 442-445.  DOI
  • Liu, Q., Zhou, Y., Morrell, P.L., & Gaut, B.S. (2017). Deleterious variants in Asian rice and the potential cost of domestication. Molecular Biology and Evolution 34(4), 908-924. DOI
  • Matz, M.V., Treml, E.A., Aglyamova, G.V., et al. (2017). Potential for rapid genetic adaptation to warming in a Great Barrier Reef coral. bioRxiv, 114173.  DOI
  • Morgan, A.P., de Villena, F.P.-M. (2017). Sequence and Structural Diversity of Mouse Y Chromosomes. Molecular Biology and Evolution 34(12), 3186–3204.  DOI
  • Nam, K., Munch, K., Mailund, T., et al. (2017). Evidence that the rate of strong selective sweeps increases with population size in the great apes. PNAS 114(7), 1613-1618.  DOI
  • Pedersen, C.E.T., Lohmueller, K.E., Grarup, N., et al. (2017). The effect of an extreme and prolonged population bottleneck on patterns of deleterious variation: Insights from the Greenlandic Inuit. Genetics 205(2), 787-801.  DOI
  • Racimo, F., Gokhman, D., Fumagalli, M., et al. (2017). Archaic adaptive introgression in TBX15/WARS2Molecular Biology and Evolution 34(3), 509-524.  DOI
  • Racimo, F., Marnetto, D., & Huerta-Sanchez, E. (2017). Signatures of archaic adaptive introgression in present-day human populations. Molecular Biology and Evolution 34(2), 296-317.  DOI
  • Rogers, R.L., & Slatkin, M. (2017). Excess of genomic defects in a woolly mammoth on Wrangel island. PLoS Genetics 13(3), e1006601.  DOI
  • Schiffels, S., Mustonen, V., & Lässig, M. (2017). The asexual genome of Drosophila. bioRxiv, 226670.  DOI
  • Siewert, K.M., & Voight, B.F. (2017). Detecting long-term balancing selection using allele frequency correlation. Molecular Biology and Evolution 34(11), 2996-3005.  DOI
  • Sikora, M., Seguin-Orlando, A., Sousa, V.C., et al. (2017). Ancient genomes show social and reproductive behavior of early Upper Paleolithic foragers. Science, eaao1807.  DOI
  • Sohail, M., Vakhrusheva, O.A., Sul, J.H., et al. (2017). Negative selection in humans and fruit flies involves synergistic epistasis. Science 356(6337), 539-542.  DOI
  • Xu, D., Pavlidis, P., Taskent, R.O., et al. (2017). Archaic hominin introgression in Africa contributes to functional salivary MUC7 genetic variation.  Molecular Biology and Evolution 34(10), 2704-2715.  DOI


  • Aeschbacher, S., Packard Selby, J., Willis, J.H., & Coop, G. (2016). Population-genomic inference of the strength and timing of selection against gene flow. bioRxiv, 072736.  DOI
  • Cassa, C.A., Weghorn, D., Balick, D.J., et al. (2016). Estimating the selective effect of heterozygous protein truncating variants from human exome data. bioRxiv, 075523.  DOI
  • Chen, C.-Y., Hung, L.-Y., Wu, C.-S., & Chuang, T.-J. (2016). Purifying selection shapes the coincident SNP distribution of primate coding sequences. Scientific Reports 6(27272), 1-15.  DOI
  • Enard, D., Cai L., Gwennap C., Petrov D.A. (2016). Viruses are a dominant driver of protein adaptation in mammals. eLife 17(5), e12469. DOI
  • Ewing, G.B., & Jensen, J.D. (2016). The consequences of not accounting for background selection in demographic inference. Molecular Ecology 25(1), 135-141.  DOI
  • de Filippo, C., Key, F.M., Ghirotto, S., et al. (2016). Recent selection changes in human genes under long-term balancing selection. Molecular Biology and Evolution 33(6), 1435-1447.  DOI
  • Galtier, N. (2016). Adaptive protein evolution in animals and the effective population size hypothesis. PLoS Genetics 12(1), 1-23.  DOI
  • Harris, K., & Nielsen, R. (2016). The genetic cost of Neanderthal introgression. Genetics 203(2), 881-891. DOI
  • Lindo, J., Huerta-Sanchez, E., Nakagome, S., et al. (2016). A time transect of exomes from a Native American population before and after European contact. Nature Communications 7(13175). DOI
  • Peyregne, S., Boyle, M.J., Dannemann, M., & Prufer, K. (2016). Detecting ancient positive selection in humans using extended lineage sorting. bioRxiv 092999. DOI
  • Racimo, F. (2016). Testing for ancient selection using cross-population allele frequency differentiation. Genetics 202(2), 733-750.  DOI


  • Arunkumar, R., Ness, R.W., Wright, S.I., & Barrett, S.C. (2015). The evolution of selfing is accompanied by reduced efficacy of selection and purging of deleterious mutations. Genetics 199(3), 817-829.  DOI
  • Assaf, Z.J., Petrov, D.A., & Blundell, J.R. (2015). Obstruction of adaptation in diploids by recessive, strongly deleterious alleles. PNAS 112(20), E2658-E2666.  DOI
  • Bianco, E., Soto, H.W., Vargas, L., & Pérez‐Enciso, M. (2015). The chimerical genome of Isla del Coco feral pigs (Costa Rica), an isolated population since 1793 but with remarkable levels of diversity. Molecular Ecology 24(10), 2364-2378.  DOI
  • Caballero, A., Tenesa, A., & Keightley, P.D. (2015). The nature of genetic variation for complex traits revealed by GWAS and regional heritability mapping analyses. Genetics 201(4), 1601-1613.  DOI
  • Cocca, M., Pybus, M., Palamara, P.F., et al. (2015). Purging of deleterious variants due to drift and founder effect in Italian populations with extended autozygosity. bioRxiv, 022947.  DOI
  • Douglas, G.M., Gos, G., Steige, K.A., et al. (2015). Hybrid origins and the earliest stages of diploidization in the highly successful recent polyploid Capsella bursa-pastorisPNAS 112(9), 2806-2811.  DOI
  • Hussin, J.G., Hodgkinson, A., Idaghdour, Y., et al. (2015). Recombination affects accumulation of damaging and disease-associated mutations in human populations. Nature Genetics 47(4), 400-404.  DOI
  • Mafessoni, F., & Lachmann, M. (2015). Selective strolls: fixation and extinction in diploids are slower for weakly selected mutations than for neutral ones. Genetics 201(4), 1581-1589.  DOI
  • Schrider, D.R., & Kern, A.D. (2015). Inferring selective constraint from population genomic data suggests recent regulatory turnover in the human brain. Genome Biology and Evolution 7(12), 3511-3528.  DOI


  • Bergland, A.O., Behrman, E.L., O’Brien, K.R., Schmidt, P.S., & Petrov, D.A. (2014). Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in DrosophilaPLoS Genetics 10(11), e1004775.  DOI
  • Comeron, J.M. (2014). Background selection as baseline for nucleotide variation across the Drosophila genome. PLoS Genetics 10(6), e1004434.  DOI
  • Enard, D., Messer, P.W., & Petrov, D.A. (2014). Genome-wide signals of positive selection in human evolution. Genome Research 24(6), 885-895.  DOI
  • Veeramah, K.R., Gutenkunst, R.N., Woerner, A.E., et al. (2014). Evidence for increased levels of positive and negative selection on the X chromosome versus autosomes in humans. Molecular Biology and Evolution 31(9), 2267-2282.  DOI


  • Kousathanas, A., & Keightley, P.D. (2013). A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193(4), 1197-1208.  DOI
  • Messer, P.W., & Petrov, D.A. (2013). Frequent adaptation and the McDonald–Kreitman test. PNAS 110(21), 8615-8620.  DOI