Evidence map›Paper›PMID 21076150›Full record

ArticleBioinformatics (Oxford, England)2011

Automated construction and testing of multi-locus gene-gene associations.

Ryan Abo, Stacey Knight, Alun Thomas, Nicola J Camp

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2011. 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.

Ryan AboDepartment of Biomedical Informatics, University of Utah School of Medicine, UT, USA. ryan.abo@hsc.utah.edu
Stacey Knight
Alun Thomas
Nicola J Camp

Funding

GENETIC ANALYSIS TECHNIQUES FOR COMMON CANCERSK07CA098364 · NCI · UNIVERSITY OF UTAH · PI CAMP, NICOLA J. · 2004 to 2008
$701k
NCI NIH HHS CA 098364NLM NIH HHS T15 LM0724
6 · The paper itself

Abstract

unlabelledIt has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable. AVAILABILITY: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.

Indexed as

AlgorithmsGenesGenetic Association StudiesGenetic LociHaplotypesMonte Carlo MethodPolymorphism, Single NucleotideSoftware

Identifiers

PMID21076150
PMCPMC3008644

What OpenQuestion holds

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LicenceCC BY-NC
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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.