Evidence map›Paper›PMID 25485788›Full record

ArticlePloS one2014

Design of DNA pooling to allow incorporation of covariates in rare variants analysis.

Weihua Guan, Chun Li

Abstract readComparative Study
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Weihua GuanDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America.
Chun LiDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, United States of America.

Funding

Developing Statistical Methods for Disease Gene DiscoveryR01HG004517 · NHGRI · VANDERBILT UNIVERSITY · PI LI, CHUN, LI, MINGYAO · 2008 to 2012
$1.9M
NHGRI NIH HHS R01 HG004517NHGRI NIH HHS R01HG004517
6 · The paper itself

Abstract

backgroundRapid advances in next-generation sequencing technologies facilitate genetic association studies of an increasingly wide array of rare variants. To capture the rare or less common variants, a large number of individuals will be needed. However, the cost of a large scale study using whole genome or exome sequencing is still high. DNA pooling can serve as a cost-effective approach, but with a potential limitation that the identity of individual genomes would be lost and therefore individual characteristics and environmental factors could not be adjusted in association analysis, which may result in power loss and a biased estimate of genetic effect.

methodsFor case-control studies, we propose a design strategy for pool creation and an analysis strategy that allows covariate adjustment, using multiple imputation technique.

resultsSimulations show that our approach can obtain reasonable estimate for genotypic effect with only slight loss of power compared to the much more expensive approach of sequencing individual genomes.

conclusionOur design and analysis strategies enable more powerful and cost-effective sequencing studies of complex diseases, while allowing incorporation of covariate adjustment.

Indexed as

Research DesignAlgorithmsCase-Control StudiesComputational BiologyDiseaseGenetic VariationGenotypeHigh-Throughput Nucleotide SequencingHumansSample SizeSequence Analysis, DNA

Identifiers

PMID25485788
PMCPMC4259344

What OpenQuestion holds

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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.