Evidence map›Paper›PMID 23163532›Full record

ArticleAnnals of human genetics2013

Impact on modes of inheritance and relative risks of using extreme sampling when designing genetic association studies.

Gang Zheng, Xu Jinfeng, Ao Yuan, O Wu Colin

Abstract read
In one paragraph

Article in Annals of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Gang ZhengOffice of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA. zhengg@nhlbi.nih.gov
Xu Jinfeng
Ao Yuan
O Wu Colin

Funding

STRUCTURAL/FUNCTIONAL CHARACTERIZATION OF AMINOGLYCOSIDEG12RR003048 · NCRR · HOWARD UNIVERSITY · PI SOUTHERLAND, WILLIAM M. · 1985 to 2011
$19.0M
Intramural NIH HHS Z99 HL999999NCRR NIH HHS 2G12RR003048NCRR NIH HHS G12 RR003048
6 · The paper itself

Abstract

Using extreme phenotypes for association studies can improve statistical power . We study the impact of using samples with extremely high or low traits on the alternative model space, the genotype relative risks, and the genetic models in association studies. We prove the following results: when the risk allele causes high-trait values, the more extreme the high traits, the larger the genotype relative risks, which is not always true for using extreme low traits; we also prove that a genetic model theoretically changes with more extreme trait except for the recessive or dominant models. Practically, however, the impact of deviations from the true genetic model at a functional locus due to selective sampling is virtually negligible. The implications of our findings are discussed. Numerical values are reported for illustrations.

Indexed as

Genetic Association StudiesModels, GeneticQuantitative Trait, HeritableRiskHumansResearch Design

Identifiers

PMID23163532
PMCPMC3535545

What OpenQuestion holds

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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.