Evidence map›Paper›PMID 26508762›Full record

ArticleBioinformatics (Oxford, England)2016

ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations.

Emile R Chimusa, Mamana Mbiyavanga, Gaston K Mazandu, Nicola J Mulder

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

17 citing papers in PubMed, 34 citations in OpenAlex.

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  7. Genetic modifiers of long-term survival in sickle cell anemia.Clinical and translational medicine · 2020
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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

4 authors at 1 institution in 1 country.

Emile R ChimusaComputational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.
Mamana MbiyavangaComputational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.
Gaston K MazanduComputational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.
Nicola J MulderComputational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.
University of Cape Town · ZA

Funding

H3ABioNet: a sustainable African Bioinformatics Network for H3AfricaU41HG006941 · NHGRI · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA · 2012 to 2017
$14.8M
NHGRI NIH HHS U41 HG006941NHGRI NIH HHS U41HG006941
6 · The paper itself

Abstract

motivationDespite numerous successful Genome-wide Association Studies (GWAS), detecting variants that have low disease risk still poses a challenge. GWAS may miss disease genes with weak genetic effects or strong epistatic effects due to the single-marker testing approach commonly used. GWAS may thus generate false negative or inconclusive results, suggesting the need for novel methods to combine effects of single nucleotide polymorphisms within a gene to increase the likelihood of fully characterizing the susceptibility gene.

resultsWe developed ancGWAS, an algebraic graph-based centrality measure that accounts for linkage disequilibrium in identifying significant disease sub-networks by integrating the association signal from GWAS data sets into the human protein-protein interaction (PPI) network. We validated ancGWAS using an association study result from a breast cancer data set and the simulation of interactive disease loci in the simulation of a complex admixed population, as well as pathway-based GWAS simulation. This new approach holds promise for deconvoluting the interactions between genes underlying the pathogenesis of complex diseases. Results obtained yield a novel central breast cancer sub-network of the human interactome implicated in the proteoglycan syndecan-mediated signaling events pathway which is known to play a major role in mesenchymal tumor cell proliferation, thus providing further insights into breast cancer pathogenesis. AVAILABILITY AND IMPLEMENTATION: The ancGWAS package and documents are available at http://www.cbio.uct.ac.za/~emile/software.html.

Indexed as

Genetics, PopulationSignal TransductionSoftwareBreast NeoplasmsFemaleGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLinkage DisequilibriumPolymorphism, Single NucleotideProtein Interaction Mapping

Identifiers

PMID26508762
PMCPMC5939890
OpenAlexW2177764460

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.