Evidence map›Paper›PMID 23626757›Full record

ArticlePloS one2013

Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes.

Zhixiang Zhu, Xiaoran Tong, Zhihong Zhu, Meimei Liang, Wenyan Cui, Kunkai Su, Ming D Li, Jun Zhu

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 5% 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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.

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  17. Evolutionary footprint of epistasis.PLoS computational biology · 2018
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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

8 authors at 2 institutions in 2 countries.

Zhixiang ZhuInstitute of Bioinformatics, Zhejiang University, Hangzhou, China.
Xiaoran Tong
Zhihong Zhu
Meimei Liang
Wenyan Cui
Kunkai Su
Ming D Li
Jun Zhu
First Affiliated Hospital Zhejiang University · CNZhejiang University · CN

Funding

MAPPING OF SUSCEPTIBILITY LOCI FOR NICOTINE DEPENDENCER01DA012844 · NIDA · UNIVERSITY OF VIRGINIA · PI LI, MING D · 1999 to 2013
$10.0M
NIDA NIH HHS DA-012844NIDA NIH HHS R01 DA012844Wellcome TrustWellcome Trust 076113
6 · The paper itself

Abstract

Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene-gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.

Indexed as

AlgorithmsEpistasis, GeneticPolymorphism, Single NucleotideSoftwareCELF ProteinsCore Binding Factor Alpha 2 SubunitDiabetes Mellitus, Type 2DNA-Binding ProteinsGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansModels, GeneticMultifactor Dimensionality ReductionPhenotypeRNA-Binding ProteinsCELF4 protein, humanCELF ProteinsCore Binding Factor Alpha 2 SubunitDNA-Binding ProteinsL3MBTL3 protein, humanRNA-Binding ProteinsRUNX1 protein, human

Identifiers

PMID23626757
PMCPMC3633958
OpenAlexW2092940392

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.