Evidence map›Paper›PMID 17847005›Full record

ArticleAmerican journal of human genetics2007

A Bayesian approach to copy-number-polymorphism analysis in nuclear pedigrees.

Konstantina Kosta, Ian Sabroe, Jonathan Goke, Robert J Nibbs, John Tsanakas, Moira K Whyte, M Dawn Teare

Open access · bronzeAbstract read
In one paragraph

Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 15% of its field
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

10 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Hidden copy number variation in the HapMap population.Proceedings of the National Academy of Sciences of the United States of America · 2008
    Article
  10. 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

7 authors at 3 institutions in 2 countries.

Konstantina KostaSchool of Medicine and Biomedical Sciences, University of Sheffield, Sheffield, UK.
Ian Sabroe
Jonathan Goke
Robert J Nibbs
John Tsanakas
Moira K Whyte
M Dawn Teare
University of Sheffield · GBHippocration General Hospital · GRUniversity of Glasgow · GB

Funding

Medical Research Council G116/170
6 · The paper itself

Abstract

Segmental copy-number polymorphisms (CNPs) represent a significant component of human genetic variation and are likely to contribute to disease susceptibility. These potentially multiallelic and highly polymorphic systems present new challenges to family-based genetic-analysis tools that commonly assume codominant markers and allow for no genotyping error. The copy-number quantitation (CNP phenotype) represents the total number of segmental copies present in an individual and provides a means to infer, rather than to observe, the underlying allele segregation. We present an integrated approach to meet these challenges, in the form of a graphical model in which we infer the underlying CNP phenotype from the (single or replicate) quantitative measure within the analysis while assuming an allele-based system segregating through the pedigree. This approach can be readily applied to the study of any form of genetic measure, and the construction permits extension to a wide variety of hypothesis tests. We have implemented the basic model for use with nuclear families, and we illustrate its application through an analysis of the CNP located in gene CCL3L1 in 201 families with asthma.

Indexed as

Bayes TheoremGene DosageModels, GeneticPolymorphism, GeneticAsthmaChemokines, CCFemaleGenetic VariationHumansMaleNuclear FamilyPedigreePhenotypePolymorphism, Single NucleotideCCL3L1 protein, humanChemokines, CC

Identifiers

PMID17847005
PMCPMC2227930
OpenAlexW2117206387

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.