Evidence map›Paper›PMID 31061475›Full record

ArticleNature ecology & evolution2019

Exploiting selection at linked sites to infer the rate and strength of adaptation.

Lawrence H Uricchio, Dmitri A Petrov, David Enard

Abstract read
In one paragraph

Article in Nature ecology & evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

  1. Review
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  8. Applying the Classic Test dN/dS to Detect Selection in Archaea.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  9. Article
  10. Local genetic adaptation to habitat in wild chimpanzees.bioRxiv : the preprint server for biology · 2024
    Article
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  15. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lawrence H UricchioDepartment of Biology, Stanford University, Stanford, CA, USA. uricchio@berkeley.edu.
Dmitri A PetrovDepartment of Biology, Stanford University, Stanford, CA, USA.
David EnardDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA. denard@email.arizona.edu.

Funding

Genomics of rapid adaptation in the lab and in the wildR35GM118165 · NIGMS · STANFORD UNIVERSITY · PI Dmitri Petrov · 2016 to 2026
$8.3M
Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
A Stanford - SJSU Postdoctoral Training Program to Enhance URM TeachingK12GM088033 · NIGMS · STANFORD UNIVERSITY · PI BOOTHROYD, JOHN C · 2010 to 2016
$4.1M
NHGRI NIH HHS R01 HG005855NIGMS NIH HHS K12 GM088033NIGMS NIH HHS R35 GM118165
6 · The paper itself

Abstract

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.

Indexed as

Adaptation, PhysiologicalSelection, GeneticAcclimatizationBiological EvolutionHumansPolymorphism, Genetic

Identifiers

PMID31061475
PMCPMC6693860

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.