The deleterious mutation load is insensitive to recent population history

Journal name:
Nature Genetics
Year published:
Published online


Human populations have undergone major changes in population size in the past 100,000 years, including recent rapid growth. How these demographic events have affected the burden of deleterious mutations in individuals and the frequencies of disease mutations in populations remains unclear. We use population genetic models to show that recent human demography has probably had little impact on the average burden of deleterious mutations. This prediction is supported by two exome sequence data sets showing that individuals of west African and European ancestry carry very similar burdens of damaging mutations. We further show that for many diseases, rare alleles are unlikely to contribute a large fraction of the heritable variation, and therefore the impact of recent growth is likely to be modest. However, for those diseases that have a direct impact on fitness, strongly deleterious rare mutations probably do have an important role, and recent growth will have increased their impact.

At a glance


  1. Time course of load and other key aspects of variation through a bottleneck and exponential growth.
    Figure 1: Time course of load and other key aspects of variation through a bottleneck and exponential growth.

    (a,b) The bottleneck (a) and exponential growth (b). (cf) The expected number of variants and alleles per MB assuming semidominant mutations (c,d) or recessive mutations (e,f) with s = 1% and a mutation rate per site per generation of 10−8.

  2. Changes in load due to changes in population size during the histories of European and African Americans.
    Figure 2: Changes in load due to changes in population size during the histories of European and African Americans.

    (a,b) Semidominant (a) and recessive (b) sites. The blue lines denote the difference in load per base pair of DNA sequence in the present-day population compared to the ancestral (constant) population size as a function of the selection coefficient. The green and red lines show the difference in load due to segregating and fixed variants, respectively. The increase in load due to segregating variation in modern populations approximately cancels out the decrease in load due to fixed sites. The scale on the y axis is linear within the gray region and is logarithmic outside this region.

  3. Observed mean allele frequencies in AAs and EAs at various classes of SNVs.
    Figure 3: Observed mean allele frequencies in AAs and EAs at various classes of SNVs.

    The plot shows the mean frequencies in each population (± 2 s.d.) using exome sequence data from Fu et al.6. Here a site is considered a SNV if it is segregating in the combined AA-EA sample of 6,515 individuals. The functional classifications of sites are from PolyPhen-2 (ref. 22) with bias-correcting modifications. The AA and EA mean frequencies are essentially identical within all five functional categories (p > 0.05).

  4. Predicted effect of demography on the genetic architecture of disease risk.
    Figure 4: Predicted effect of demography on the genetic architecture of disease risk.

    All plots (af) assume an additive trait and, with the exception of b, are based on simulations with semidominant selection under the Tennessen et al.5 demographic model. Results for the constant population size model are also provided for comparison. The upper plots (ac) show the cumulative fractions of genetic variance due to alleles at frequency <x based on simulated data with weak selection (s = 0.0002; a); assuming the observed frequency spectrum at probably damaging sites6, 22, where a constant population size of 14,474 and selection coefficient of 0.02% are used for comparison (b); and simulated data with strong selection (s = 0.01; c). (df) The fraction of variance due to rare alleles (i.e., <0.1%) as a function of the selection coefficient (d); the per-site contribution to variance as a function of the selection coefficient under two extreme models, with effect sizes that are either independent of s (constant) or proportional to s (e); and the expected fraction of the variance due to rare variants (i.e., <0.1%) as a function of the correlation between the selection on and the effect sizes of variants (f). Further details on the model are provided in the Online Methods.


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Author information

  1. These authors contributed equally to this work.

    • Yuval B Simons &
    • Michael C Turchin


  1. Department of Ecology, Evolution and Behavior, Hebrew University of Jerusalem, Jerusalem, Israel.

    • Yuval B Simons
  2. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.

    • Michael C Turchin &
    • Jonathan K Pritchard
  3. Howard Hughes Medical Institute, Stanford University, Stanford, California, USA.

    • Jonathan K Pritchard
  4. Department of Biology, Stanford University, Stanford, California, USA.

    • Jonathan K Pritchard
  5. Department of Genetics, Stanford University, Stanford, California, USA.

    • Jonathan K Pritchard
  6. Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA.

    • Guy Sella
  7. Present address: Department of Biological Sciences, Columbia University, New York, New York, USA.

    • Guy Sella


J.K.P. and G.S. conceived and supervised the research. Y.B.S., G.S. and J.K.P. developed theory. Y.B.S. performed simulations. M.C.T. and J.K.P. performed data analysis. J.K.P. and G.S. wrote the manuscript with input from Y.B.S. and M.C.T.

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The authors declare no competing financial interests.

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