Evidence map›Paper›PMID 17676998›Full record

ArticlePLoS genetics2007

Imputation-based analysis of association studies: candidate regions and quantitative traits.

Bertrand Servin, Matthew Stephens

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 317 papers, 23 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
317citing papers in PubMed, 23 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

317 citing papers in PubMed, 23 syntheses or guidelines pooled it.

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257 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

2 authors.

Bertrand ServinDepartment of Statistics, University of Washington, Seattle, Washington, United States of America. bservin@toulouse.inra.fr
Matthew Stephens

Funding

Genome analysis: statistical methods and applicationsR01HG002585 · NHGRI · UNIVERSITY OF WASHINGTON · PI MATTHEW STEPHENS · 2002 to 2026
$8.4M
NHGRI NIH HHS R01 HG002585NHGRI NIH HHS R01 HG02585-01
6 · The paper itself

Abstract

We introduce a new framework for the analysis of association studies, designed to allow untyped variants to be more effectively and directly tested for association with a phenotype. The idea is to combine knowledge on patterns of correlation among SNPs (e.g., from the International HapMap project or resequencing data in a candidate region of interest) with genotype data at tag SNPs collected on a phenotyped study sample, to estimate ("impute") unmeasured genotypes, and then assess association between the phenotype and these estimated genotypes. Compared with standard single-SNP tests, this approach results in increased power to detect association, even in cases in which the causal variant is typed, with the greatest gain occurring when multiple causal variants are present. It also provides more interpretable explanations for observed associations, including assessing, for each SNP, the strength of the evidence that it (rather than another correlated SNP) is causal. Although we focus on association studies with quantitative phenotype and a relatively restricted region (e.g., a candidate gene), the framework is applicable and computationally practical for whole genome association studies. Methods described here are implemented in a software package, Bim-Bam, available from the Stephens Lab website http://stephenslab.uchicago.edu/software.html.

Indexed as

Polymorphism, Single NucleotideBayes TheoremChromosome MappingComputational BiologyData Interpretation, StatisticalGenomicsGenotypeHumansModels, BiologicalModels, StatisticalOdds RatioPhenotypePolymorphism, GeneticSoftware

Identifiers

PMID17676998
PMCPMC1934390

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.