Genomic data encode past evolutionary events and have the potential to reveal the strength, rate and biological drivers of adaptation. However, joint estimation of adaptation rate (α) and adaptation strength remains challenging because evolutionary processes such as demography, linkage and non-neutral polymorphism can confound inference. Here, we exploit the influence of background selection to reduce the fixation rate of weakly beneficial alleles to jointly infer the strength and rate of adaptation. We develop a McDonald–Kreitman-based method to infer adaptation rate and strength, and estimate α = 0.135 in human protein-coding sequences, 72% of which is contributed by weakly adaptive variants. We show that, in this adaptation regime, α is reduced ~25% by linkage genome-wide. Moreover, we show that virus-interacting proteins undergo adaptation that is both stronger and nearly twice as frequent as the genome average (α = 0.224, 56% due to strongly beneficial alleles). Our results suggest that, while most adaptation in human proteins is weakly beneficial, adaptation to viruses is often strongly beneficial. Our method provides a robust framework for estimation of adaptation rate and strength across species.
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Variation of the adaptive substitution rate between species and within genomes
Evolutionary Ecology Open Access 14 December 2019
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Supplemental Data Table 1 is provided on the publisher’s website. The data that we used to parameterize our model are also available online at https://github.com/uricchio/mktest. Columns in Supplementary Data Table 1 are as follows: 1, Ensembl coding gene identification; 2, number of non-synonymous polymorphic sites; 3, respective derived allele frequencies of these sites separated by commas; 4, number of synonymous polymorphic sites; 5, respective frequency-derived allele frequencies of these sites; 6, number of fixed non-synonymous substitutions on the human branch; and 7, number of fixed synonymous substitutions on the human branch.
The code that we used to parameterize our model is freely available online at https://github.com/uricchio/mktest.
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We thank A. Aw, N. Rosenberg and members of the Rosenberg and Petrov laboratories for helpful discussions. L.H.U. was partially supported by an IRACDA fellowship through NIGMS grant No. K12GM088033. L.H.U was supported by National Institutes of Health grant R01 HG005855 and National Science Foundation grant DBI-1458059 (to N. Rosenberg). We also thank the Stanford/SJSU IRACDA Program for support.
The authors declare no competing interests.
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Supplementary Methods, Supplementary Figs. 1–20
Supplementary Data 1
Data we used to parameterize the model.
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Uricchio, L.H., Petrov, D.A. & Enard, D. Exploiting selection at linked sites to infer the rate and strength of adaptation. Nat Ecol Evol 3, 977–984 (2019). https://doi.org/10.1038/s41559-019-0890-6
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