Evidence map›Paper›PMID 23749186›Full record

ArticleNature genetics2013

Genome-wide inference of natural selection on human transcription factor binding sites.

Leonardo Arbiza, Ilan Gronau, Bulent A Aksoy, Melissa J Hubisz, Brad Gulko, Alon Keinan, Adam Siepel

Abstract read
In one paragraph

Article in Nature genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed
7.8field-weighted citation impact, top 2% of its field
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

75 citing papers in PubMed, 151 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Epistatic contributions to human traits via transcription factor mechanisms.medRxiv : the preprint server for health sciences · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Exploration of Tools for the Interpretation of Human Non-Coding Variants.International journal of molecular sciences · 2022
    Review
  13. Article
  14. Article
  15. Systems biology analysis of human genomes points to key pathways conferring spina bifida risk.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  16. Review
  17. Review
  18. Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021
    Review
  19. Article
  20. Article

15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Leonardo ArbizaDepartment of Biological Statistics & Computational Biology, Cornell University, Ithaca, NY, USA.
Ilan Gronau
Bulent A Aksoy
Melissa J Hubisz
Brad Gulko
Alon Keinan
Adam Siepel
Cornell University · USTri-Institutional PhD Program in Chemical Biology · US

Funding

Tri-Institutional Training Program in Computational Biology & MedicineT32GM083937 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHRISTINI, DAVID J. · 2009 to 2018
$3.1M
Computational methods for human genomic data integration: demography, selection,R01GM102192 · NIGMS · CORNELL UNIVERSITY · PI SIEPEL, ADAM CHARLES · 2013 to 2017
$1.7M
NIGMS NIH HHS GM102192NIGMS NIH HHS R01 GM102192NIGMS NIH HHS T32 GM083937NIGMS NIH HHS T32-GM083937
6 · The paper itself

Abstract

For decades, it has been hypothesized that gene regulation has had a central role in human evolution, yet much remains unknown about the genome-wide impact of regulatory mutations. Here we use whole-genome sequences and genome-wide chromatin immunoprecipitation and sequencing data to demonstrate that natural selection has profoundly influenced human transcription factor binding sites since the divergence of humans from chimpanzees 4-6 million years ago. Our analysis uses a new probabilistic method, called INSIGHT, for measuring the influence of selection on collections of short, interspersed noncoding elements. We find that, on average, transcription factor binding sites have experienced somewhat weaker selection than protein-coding genes. However, the binding sites of several transcription factors show clear evidence of adaptation. Several measures of selection are strongly correlated with predicted binding affinity. Overall, regulatory elements seem to contribute substantially to both adaptive substitutions and deleterious polymorphisms with key implications for human evolution and disease.

Indexed as

Genome, HumanAnimalsBase SequenceBinding SitesChromosome MappingComputer SimulationGenome-Wide Association StudyHumansModels, GeneticModels, StatisticalMutationRegulatory Sequences, Nucleic AcidSelection, GeneticSubstrate SpecificityTranscription FactorsTranscription Factors

Identifiers

PMID23749186
PMCPMC3932982
OpenAlexW2006838271

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.