Evidence map›Paper›PMID 21254222›Full record

ArticleGenetic epidemiology2011

On optimal pooling designs to identify rare variants through massive resequencing.

Joon Sang Lee, Murim Choi, Xiting Yan, Richard P Lifton, Hongyu Zhao

Abstract read
In one paragraph

Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. eALPS: estimating abundance levels in pooled sequencing using available genotyping data.Journal of computational biology : a journal of computational molecular cell biology · 2013
    Article
  5. Article
  6. Article
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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

5 authors.

Joon Sang LeeDepartment of Epidemiology and Public Health, Yale University, New Haven, Connecticut, USA. joonsang.lee@yale.edu
Murim Choi
Xiting Yan
Richard P Lifton
Hongyu Zhao

Funding

MEDICAL INFORMATICS RESEARCH TRAINING AT YALET15LM007056 · NLM · YALE UNIVERSITY · PI Mark Bender Gerstein, LUCILA OHNO-MACHADO · 1987 to 2026
$22.2M
STATISTICAL METHODS TO MAP GENES FOR COMPLEX TRAITSR01GM059507 · NIGMS · YALE UNIVERSITY · PI ZHAO, HONGYU · 1999 to 2013
$3.6M
YALE BIOMEDICAL SUPERCOMPUTER: NEUROSCIENCES10RR019895 · NCRR · YALE UNIVERSITY · PI WILLIAMS, KENNETH ROBERT · 2004 to 2004
$1.6M
NCRR NIH HHS RR19895NCRR NIH HHS S10 RR019895NIGMS NIH HHS GM 59507NIGMS NIH HHS R01 GM059507NLM NIH HHS T15 LM007056NLM NIH HHS T15 LM07056
6 · The paper itself

Abstract

The advent of next-generation sequencing technologies has facilitated the detection of rare variants. Despite the significant cost reduction, sequencing cost is still high for large-scale studies. In this article, we examine DNA pooling as a cost-effective strategy for rare variant detection. We consider the optimal number of individuals in a DNA pool to detect an allele with a specific minor allele frequency (MAF) under a given coverage depth and detection threshold. We found that the optimal number of individuals in a pool is indifferent to the MAF at the same coverage depth and detection threshold. In addition, when the individual contributions to each pool are equal, the total number of individuals across different pools required in an optimal design to detect a variant with a desired power is similar at different coverage depths. When the contributions are more variable, more individuals tend to be needed for higher coverage depths. Our study provides general guidelines on using DNA pooling for more cost-effective identifications of rare variants.

Indexed as

Genetic VariationAllelesDiseaseGene FrequencyGenome-Wide Association StudyHumansLinkage DisequilibriumModels, GeneticModels, StatisticalMolecular EpidemiologyPolymorphism, Single NucleotideProbabilitySequence Analysis, DNA

Identifiers

PMID21254222
PMCPMC3176340

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.