Evidence map›Paper›PMID 30131185›Full record

ReviewTrends in genetics : TIG2018

Giving the Genes a Shuffle: Using Natural Variation to Understand Host Genetic Contributions to Viral Infections.

Sarah R Leist, Ralph S Baric

Abstract readReview
In one paragraph

Review in Trends in genetics : TIG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Animal models of Long Covid: A hit-and-run disease.Science translational medicine · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource.International journal of molecular sciences · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Article
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

2 authors.

Sarah R LeistDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: leist@ad.unc.edu.
Ralph S BaricDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; https://sph.unc.edu/adv_profile/ralph-s-baric-phd/.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The laboratory mouse has proved an invaluable model to identify host factors that regulate the progression and outcome of virus-induced disease. The paradigm is to use single-gene knockouts in inbred mouse strains or genetic mapping studies using biparental mouse populations. However, genetic variation among these mouse strains is limited compared with the diversity seen in human populations. To address this disconnect, a multiparental mouse population has been developed to specifically dissect the multigenetic regulation of complex disease traits. The Collaborative Cross (CC) population of recombinant inbred mouse strains is a well-suited systems-genetics tool to identify susceptibility alleles that control viral and microbial infection outcomes and immune responses and to test the promise of personalized medicine.

Indexed as

Genetic Predisposition to DiseaseAnimalsChromosome MappingDisease Models, AnimalGenetics, PopulationHumansMiceMice, Inbred StrainsMice, KnockoutQuantitative Trait LociVirus DiseasesCollaborative Crossgenetic mappinghost factorsimmune responsesystems genetics

Identifiers

PMID30131185
PMCPMC7114642

What OpenQuestion holds

Textmetadata
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.