Evidence map›Paper›PMID 20926441›Full record

ArticleProceedings. Biological sciences2011

Candidate genes versus genome-wide associations: which are better for detecting genetic susceptibility to infectious disease?

W Amos, E Driscoll, J I Hoffman

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 4 pooled it
–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

48 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Pooled it
  5. Genetic susceptibility to respiratory health effects from outdoor air pollution: a structured narrative review.European respiratory review : an official journal of the European Respiratory Society · 2026
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  8. The application of clinical genetics · 2026
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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.

W AmosDepartment of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK. w.amos@zoo.cam.ac.uk
E Driscoll
J I Hoffman

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technological developments allow increasing numbers of markers to be deployed in case-control studies searching for genetic factors that influence disease susceptibility. However, with vast numbers of markers, true 'hits' may become lost in a sea of false positives. This problem may be particularly acute for infectious diseases, where the control group may contain unexposed individuals with susceptible genotypes. To explore this effect, we used a series of stochastic simulations to model a scenario based loosely on bovine tuberculosis. We find that a candidate gene approach tends to have greater statistical power than studies that use large numbers of single nucleotide polymorphisms (SNPs) in genome-wide association tests, almost regardless of the number of SNPs deployed. Both approaches struggle to detect genetic effects when these are either weak or if an appreciable proportion of individuals are unexposed to the disease when modest sample sizes (250 each of cases and controls) are used, but these issues are largely mitigated if sample sizes can be increased to 2000 or more of each class. We conclude that the power of any genotype-phenotype association test will be improved if the sampling strategy takes account of exposure heterogeneity, though this is not necessarily easy to do.

Indexed as

Computer SimulationGenetic Predisposition to DiseaseModels, GeneticAnimalsCattleGenetic MarkersGenotypeMicrosatellite RepeatsPolymorphism, Single NucleotideStochastic ProcessesTuberculosis, BovineGenetic Markers

Identifiers

PMID20926441
PMCPMC3049081

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.