Detection of domestication signals through the analysis of the full distribution of fitness effects

  1. David Castellano
  2. Ioanna-Theoni Vourlaki
  3. Ryan N. Gutenkunst
  4. Sebastian E. Ramos-Onsins

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Abstract

Domestication is a process marked by complex interactions between demographic changes and selective pressures, which together shape genetic diversity. While the phenotypic outcomes of domestication are well documented, its genetic basis—particularly the dynamics of selection—remain less well understood. To investigate these dynamics, we performed simulations designed to approximate the demographic history of large domestic mammals. These simulations used selection coefficients as a modeling tool to represent changes in selection pressures, recognizing that such coefficients are abstractions rather than direct representations of biological reality. Specifically, we analyzed site frequency spectra (SFS) under varying distributions of fitness effects (DFE) and proportions of mutations with divergent selective pressures. Our results show that the discretized deleterious DFE can be reliably inferred from the SFS of a single population, but reconstructing the beneficial DFE and demographic history remains challenging, even when using the joint SFS of both populations. We further developed a novel joint DFE inference model to estimate the proportion of mutations with divergent selection coefficients ( p c ), although we found that signals of classic hard sweeps can mimic increases in p c , complicating interpretation. These findings underscore both the utility and limitations of DFE inference and highlight the need for caution when interpreting demographic histories in domesticated populations based on such modeling assumptions.

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  1. Version published to 10.24072/pcjournal.540
  2. Peer Community in Evolutionary Biology

    The joint full distribution of fitness effects (DEF) is an important indicator in population genetic studies, and its inference has been the subject of intense research [1]. However, we still lack a solid framework to estimate DFE under certain demographic conditions.

    In this study, Castellano and colleagues propose to estimate the DFE by analysing the site frequency spectrum (SFS), and specifically develop a new approach for the joint DFE model inference [2]. The latter is based on the proportion of variants with divergent selection coefficients. Authors performed extensive simulations under models of domestication which is arguably one of the most crucial series of events in human evolution [3]. Domestication is associated with significant genetic costs in animals [4].

    While DFE is typically estimated by contrasting SFS of silent and functional mutations [5], it has been recently suggested to use the joint SFS between domesticated and wild populations to estimate the DFE [6]. Authors build on this model and expand its parameterisation. Authors were able to dissect the impact of linked selection on inferred demographic history of wild and domesticated populations, with a robust estimation of the deleterious DFE.

    There are still several limitations in the interpretation of DFE as, for instance, some selective sweeps can bias their estimates and some demographic scenarios are challenging to infer. Also, classic quantitative trait models should be evaluated as a complementary approach. Finally, the in silico predictions presented in this study could be validated by empirical scans on existing genomic data sets. Nevertheless, this study is an important contribution to our understanding on how demography, and domestication in particular, can affect variants under selection in recent evolutionary histories.

    References

    [1] Eyre-Walker A, Keightley PD. The distribution of fitness effects of new mutations. Nat Rev Genet. 2007;8(8):610-618. https://doi.org/10.1038/nrg2146

    [2] Castellano D, Vourlaki IT, Gutenkunst RN, Ramos-Onsins SE. Detection of Domestication Signals through the Analysis of the Full Distribution of Fitness Effects. BioRxiv 2025, ver.4 peer-reviewed and recommended by PCI Evol Biol https://doi.org/10.1101/2022.08.24.505198

    [3] Frantz LAF, Bradley DG, Larson G, Orlando L. Animal domestication in the era of ancient genomics. Nat Rev Genet. 2020;21(8):449-460. https://doi.org/10.1038/s41576-020-0225-0

    [4] Schubert M, Jónsson H, Chang D, et al. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc Natl Acad Sci U S A. 2014;111(52):E5661-E5669. https://doi.org/10.1073/pnas.1416991111

    [5] Kousathanas A, Keightley PD. A comparison of models to infer the distribution of fitness effects of new mutations. Genetics. 2013;193(4):1197-1208. https://doi.org/10.1534/genetics.112.148023

    [6] Huang X, Fortier AL, Coffman AJ, et al. Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations. Mol Biol Evol. 2021;38(10):4588-4602. https://doi.org/10.1093/molbev/msab162

  3. Version published to 10.1101/2022.08.24.505198 on bioRxiv