GeneticsEvolutionary BiologyPeer-reviewed journal article
Comparison of the Distribution of Fitness Effects Across Primates
Janek SendrowskiiD, Bjarke Meyer PederseniD, Juraj BergmaniD, Vasili PankratoviD, Thomas M BatailloniD
In-depth summary
Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 3 (6 Sept 2026) · CC BY 4.0
Across 38 catarrhine subspecies, the population-scaled distribution of fitness effects (DFE) tracks effective population size while the unscaled deleterious DFE appears conserved, with no detectable epistasis.
Comparative DFE studies often conflate unscaled selection coefficients (s) with population-scaled ones (Nes), so drift differences can masquerade as fitness-landscape differences; earlier great-ape work invoked positive epistasis and recessive load. It extends the comparison to Old World monkeys, separates Ne rescaling from landscape change with a tail-robust quantile test, and adds dominance modeling and divergence-informed adaptive-rate estimation to fastDFE.
Key results
ShownLarger effective population size (Ne) moves population-scaled DFE mass from effectively neutral toward strongly deleterious classes, a pattern robust to parametrization, SFS folding, common-gene-set restriction and GC-conservative spectra.
Grafen PGLS on 30 species: fraction S ∈ (−∞, −10] r = 0.82, p = 3.6 × 10^−8 (discrete) and r = 0.83, p = 1.6 × 10^−8 (gamma); fraction S ∈ (−1, 0] r = −0.72, p = 8.1 × 10^−6 and r = −0.70, p = 1.4 × 10^−5; moderate class (−10, −1] not significant (discrete r = −0.28, p = 0.14). · 3.1 The DFE across catarrhines; 2.9 Regression analysis; 3.6 GC-biased gene conversion; 3.7 Gene-set differences
ShownA tail-robust quantile diagnostic finds the unscaled deleterious DFE consistent with simple Ne rescaling, giving no evidence of epistasis across the ∼20-fold catarrhine Ne range.
PGLS slope of log10 |Sq| on log10 Ne did not differ from the no-epistasis expectation of 1 at f = 0.4, 0.5 and 0.6 under all four model variants (all p > 0.05), e.g. slope 1.20, p = 0.20 for deleterious-only gamma at f = 0.6; a simulated fixed unscaled DFE rescaled by 4Ne gives slope exactly 1. · 3.3 Can variation in Ne explain the observed…; 2.9 Regression analysis; Tables
SuggestedUnder deleterious-only fits, predicted total dN/dS (ω) falls with Ne; polymorphism alone cannot resolve the adaptive rate ωa, but it rises with Ne once lineage-specific divergence is fit jointly, a trend that persists on GC-conservative sites under the discrete parametrization.
Deleterious-only ω vs Ne: discrete r = −0.75, p = 1.9 × 10^−6. Polymorphism-only ωa: r = 0.16, p = 0.41 (gamma), r = −0.12, p = 0.52 (discrete). With divergence: ωa gamma r = 0.57, discrete r = 0.64 (both p < 2 × 10^−3); GC-conservative discrete ωa r = 0.65, p = 1.1 × 10^−4; 14 independent reference lineages. · 3.4 Substitution rates and adaptive evolution; 3.6 GC-biased gene conversion; 2.7 Inference from SFS and divergence counts
ShownReanalyzing the nine great apes of Castellano et al. (2019) reproduces their Ne association for strongly deleterious mutations but not a significant unscaled sd-Ne trend or outlier bonobos and western chimpanzees, which undercuts their positive-epistasis interpretation.
Fraction S ∈ (−∞, −10] vs Ne: r = 0.89, p = 1.68 × 10^−2 (new data) vs r = 0.89, p = 1.82 × 10^−2 (original); unscaled sd vs Ne: r = 0.75, p = 8.27 × 10^−2 (old data), r = 0.44, p = 3.87 × 10^−1 (new data), turning weakly negative under independent DiscreteParametrization fits. · 3.5 Comparison with previous estimates; 4 Discussion
ShownModeling partial recessivity that intensifies with selection strength shifts inferred DFEs only modestly and uniformly, leaving Ne associations intact; simulations show h is weakly identifiable because it trades off against Sd.
Sensitivity model h(S) = 0.4 exp(−0.02|S|), with h(−100) ≈ 0.05, makes DFEs slightly more deleterious across species. In SLiM runs, simulating h = 0.2 but fitting h = 0.5 biases toward weaker selection; in fastDFE-simulated spectra (Sd = −300, b = 0.3, sample size 20), joint (h, Sd) bootstraps show strong negative covariance. · 3.8 Dominance; Demographic scenarios and model violations; 4 Discussion
Methods, data and reproducibility
Unfolded 0-fold and 4-fold site-frequency spectra (n = 8 haplotypes, probabilistically polarized with three or two outgroups) were fit in fastDFE under discrete and gamma-exponential parametrizations, optionally with divergence counts or dominance; DFE features were regressed on log10 Ne via Grafen PGLS.
- Catarrhine population genomic panel from Pankratov et al. (2026): macaques (14), great apes (11), baboons (6), colobines (5), other cercopithecines (2) · non-human catarrhine primates (apes and Old World monkeys) · n = 38 subspecies (1,873 individuals)
- French individuals from the Human Genome Diversity Project, added because the panel lacks humans · human · n = 28 individuals
- Full curated primate dataset from which the analyzed subset was drawn · primates (multiple species) · n = 3,240 individuals from 269 species
- Species-specific reference genome assemblies used for mapping, degeneracy annotation and lineage divergence counts (GenBank accessions in Appendix Table B2) · catarrhine primates · n = 14 reference genomes
- Species-level mutation rates, generation times and species tree (supplementary material s4) from Kuderna et al. (2023) · primates
- NCBI one-to-one human ortholog groups shared by all references, used for the common gene-set robustness check · catarrhine primates (orthology to human) · n = 14,437 ortholog groups across 14 references
- Nine great ape populations analyzed by Castellano et al. (2019), drawn from Prado-Martinez et al. (2013), reanalyzed for comparison · great apes · n = nine populations
- SLiM forward simulations (Wright-Fisher; constant size, expansion, reduction, bottleneck, substructure, recessive h = 0.2) validating fastDFE · n = 1000 individuals; 10^9 sites; 10,000 generations
- Methods & tools
- fastDFE (dominance from version 1.2.0; joint divergence fitting from version 1.3.0), fastDFE DiscreteParametrization and GammaExpParametrization, fastDFE DegeneracyAnnotation, fastDFE MaximumLikelihoodAncestralAnnotation (EST-SFS model, K2SubstitutionModel), Minimap2, SLiM, msprime, ape (R package), nlme (R package), Phylogenetic generalized least squares (Brownian motion, Grafen transformation), Watterson's estimator, NCBI gene orthology database
- Data & accessions
- https://github.com/Sendrowski/PrimateDFE
- Software & environment
- fastDFE version 1.2.0 or later for dominance modeling and 1.3.0 or later for joint divergence fitting; regressions with R packages ape and nlme (versions not stated).
- Reproducibility gaps
- Repository provides the workflow, derived spectra and results; underlying genotypes come from Pankratov et al. (2026) and HGDP with no accessions given in the text; assembly accessions appear only in Appendix Table B2; some simulation checks are not shown.
Limitations (6)
- Bootstrap intervals capture only site-level Poisson sampling, ignoring between-gene heterogeneity and biological or pipeline differences, so residual cross-species variation beyond Ne may reflect underestimated uncertainty. · stated by the authors
- Divergence-informed ωa and α trends assume polymorphisms and fixed substitutions share one DFE, reflect historical rather than contemporary Ne, depend on outgroup choice, and rest on only 14 independent reference lineages. · stated by the authors
- fastDFE assumes independent sites and omits linked selection; nuisance parameters absorb only part of background-selection distortion, so residual linked-selection or recombination-heterogeneity effects cannot be excluded. · stated by the authors
- With n = 8 haplotypes, slightly beneficial and slightly deleterious mutations are hard to separate, leaving the beneficial fraction, ωa and α weakly identifiable; h covaries with selection strength and is also weakly identifiable. · stated by the authors
- Species-specific references sacrifice exact cross-species comparability of annotations; absolute Ne inherits uncertainty from Kuderna et al. (2023) mutation rates and may be affected by SNP-calling differences or non-neutral synonymous sites. · stated by the authors
- Conservation of the unscaled DFE is inferred from failing to reject slope 1 across 30 species; no equivalence test or power analysis bounds how large an epistatic deviation could go undetected. · noted in this summary
Read this if you infer DFEs from site-frequency spectra across species and need guidance on separating Ne rescaling from fitness-landscape change, testing for epistasis, or handling dominance assumptions in fastDFE.
- Citations
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- FWCI
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Abstract
The distribution of fitness effects (DFE) of mutations is a key determinant of both the efficacy of natural selection and the genetic load of populations. It also provides an indirect summary of the underlying fitness landscape, so the extent to which the DFE is conserved across species is informative about the invariance of those landscapes. Here, we infer the DFE of amino-acid-changing mutations in 38 catarrhine subspecies using site-frequency spectrum (SFS)-based methods. We find that effective population size (Ne) is the dominant axis of cross-species variation in the population-scaled DFE for deleterious mutations, with the apparent clade-level clustering of estimates explained mainly by shared Ne rather than by clade-specific effects. A quantile-based test further shows that the underlying unscaled DFE does not vary significantly across species, so differences are consistent with Ne-rescaling of a single conserved DFE. Consistent with this, the deleterious (non-adaptive) substitution rate {omega}na declines significantly with Ne, reflecting more efficient purging of slightly deleterious mutations in larger populations. Turning to adaptive substitution, we find weaker, suggestive evidence that the adaptive rate {omega}a increases with Ne. These conclusions are robust to the choice of DFE parametrization, phylogenetic regression framework, the effect of GC-biased gene conversion, and ancestral misidentification. We also extend the DFE estimation procedure to relax the assumption of additive fitness effects, finding that dominance is only weakly identifiable from the SFS but has minimal impact on comparative DFE inference.
- organism: non-human primate
- organism: human
- assay: whole-genome sequencing
- research focus: phylogenetics / evolution
- research focus: software / tool
- research focus: chromatin & epigenetic regulation
- approach: statistical model
- approach: simulation
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