Evidence map›Paper›PMID 28687858›Full record

ReviewImmunogenetics2018

A genomic perspective on HLA evolution.

Diogo Meyer, Vitor R C Aguiar, Bárbara D Bitarello, Débora Y C Brandt, Kelly Nunes

Abstract readReview
In one paragraph

Review in Immunogenetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.

0numbers the graph read from it
0cells of the map it votes in
114citing 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

114 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  5. Global patterns of natural selection inferred using ancient DNA.bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study.bioRxiv : the preprint server for biology · 2025
    Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Article

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

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

5 authors.

Diogo MeyerDepartment of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil. diogo@ib.usp.br.
Vitor R C AguiarDepartment of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.
Bárbara D BitarelloDepartment of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.
Débora Y C BrandtDepartment of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.
Kelly NunesDepartment of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes-namely transcription levels-is now a feasible approach and can add another dimension to the study of genetic variation.

Indexed as

AllelesEvolution, MolecularGenetic VariationGenome-Wide Association StudyGenomicsHigh-Throughput Nucleotide SequencingHistocompatibility Antigens Class IHistocompatibility Antigens Class IIHistocompatibility TestingHLA AntigensHumansMajor Histocompatibility ComplexPolymorphism, GeneticSelection, GeneticHistocompatibility Antigens Class IHistocompatibility Antigens Class IIHLA AntigensBalancing selectionEvolutionGenomicsHLA (human leukocyte antigen)MHC (major histocompatibility complex)

Identifiers

PMID28687858
PMCPMC5748415

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.