dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks

Jia, Peilin; Zheng, Siyuan; Long, Jirong; Wang, Zheng; Zhao, Zhongming

doi:10.1093/bioinformatics/btq615

Cited by 249 publications

(284 citation statements)

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“…We assessed the complete GWAS dataset under a systems biology aspect incorporating protein interaction information to gain further insight into the biologic networks underlying pediatric stroke. 6 The latter analysis highlights the complex interplay of genes at the interface of vascular biology and coagulation in the pathogenesis of PS and supports the need of an extensive analysis of combined genetic and physiologic properties of complex diseases. …”

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confidence: 73%

“…We used dmGWAS, 6 a method that searches for densely connected modules within proteininteraction graphs, where members of such modules are constrained toward an accumulation of strong association signals. In a primary step, a representative P value was assigned to each gene, which is the smallest of (1) all intragenic SNPs and (2) SNPs with an LD r 2 Ͼ 0.3.…”

Section: Systems Biology Analysismentioning

confidence: 99%

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A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke

Arning

Hiersche

Witten

et al. 2012

Blood

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Pediatric stroke is a rare but highly penetrant disease with a strong genetic background. Although there are an increasing number of genome-wide association studies (GWASs) for stroke in adults, such studies for stroke of pediatric onset are lacking. Here we report the results of the first GWAS on pediatric stroke using a large cohort of 270 family-based trios. GWAS was performed using the Illumina 370 CNV single nucleotide polymorphisms array and analyzed using the transmission disequilibrium test as implemented in PLINK. An enrichment analysis was performed to identify additional true association signals among lower P value signals and searched for cumulatively associated genes within protein interaction data using dmGWAS. We observed clustering of association signals in 4 genes belonging to one family of metalloproteinases at high (ADAMTS12, P ‫؍‬ 2.9 ؋ 10 ؊6 ; ADAMTS2, P ‫؍‬ 8.0 ؋ 10 ؊6 ) and moderate (ADAMTS13, P ‫؍‬ 9.3 ؋ 10 ؊4 ; ADAMTS17, P ‫؍‬ 8.5 ؋ 10 ؊4 ) significance levels. Over-representation and gene-network IntroductionPediatric stroke (PS) is a heterogeneous disorder associated with significant morbidity and mortality. It is recognized as an important childhood disease, with an incidence of 2.6-6.4 per 100 000 children per year. 1 Although PS is relatively rare, it is devastating to those affected as half of the survivors develop cognitive or motor disabilities. 2 Risk factors for stroke are different in children and adults, and classic risk factors, such as smoking, arteriosclerosis, or diabetes, are unlikely to contribute to pediatric stroke. The most prominent risk factors for stroke in children include underlying medical conditions (ie, cardiac disorders, metabolic diseases, cerebrovascular pathologies, and infectious diseases) 1 as well as many prothrombotic abnormalities. 3,4 Numerous association studies have established several genetic polymorphisms contributing to pediatric stroke risk. These candidate gene approaches identified several susceptibility genes for PS (ie, prothrombin, and GPX3). 3,4 However, genetic predisposition, environmental effects, and other risk factors can often not be disentangled because of the complex nature of PS etiology. Furthermore, a large proportion of missing heritability remains to be accounted for. 5 Genome-wide association studies (GWASs) offer a powerful approach to gene function discovery and are the current method of choice to dissect the genetic basis of complex diseases. Up-to-date, studies in families with a known first onset of pediatric stroke are lacking mainly because of limited sample size.Therefore, we performed a family-based GWAS for pediatric stroke in 270 families from Muenster, Germany composed of affected children and their parents. We identified several single nucleotide polymorphisms (SNPs), which are associated with PS at significance levels P Ͻ 10 Ϫ5 . In addition, we determined potential combined effects of SNPs contributing to stroke risk that may be missed through conventional single marker or haplotype association. We assessed ...

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mentioning

confidence: 73%

Section: Systems Biology Analysismentioning

confidence: 99%

A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke

Arning

Hiersche

Witten

et al. 2012

Blood

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“…The second approach uses the dense module searching algorithm (a heuristic 'greedy' method) described in dmGWAS 85 , where a module is defined as a sub-network within the whole network if it contains a locally increased proportion of low p-value genes. This method differs from the earlier method in using the association p-values, in combination with the PPI data, to construct the networks.…”

Section: Protein-protein Interaction Analyses In the Exome Datamentioning

confidence: 99%

Genetic architecture of type 2 diabetes

Hara

Shojima

Hosoe

et al. 2014

Biochemical and Biophysical Research Communications

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The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lowerfrequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

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“…Dense module searching for GWAS (dmGWAS) (Jia et al 2011) provides a dense module searching (DMS) algorithm to identify candidate subnetworks or genes for complex diseases by integrating the association signal from GWAS datasets into the human protein-protein interaction (PPI) network. The dmGWAS algorithm considers gene-centric disease association probability as the node weight and prioritizes candidate genes by DMS.…”

Section: Disease Gene Prioritization By Network Propagationmentioning

confidence: 99%

Network-assisted approaches for human disease research

Shim

Lee

2015

Animal Cells and Systems

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Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and disease pathways is crucial. The functional interdependence between genes for disease progression has been identified by their connections in gene networks, which enables prediction of novel disease genes based on their connections to known disease genes. Disease modules can be identified by subnetworks that are enriched for patientspecific activated or mutated genes. Network biology also facilitates the subtyping of complex diseases such as cancer, which is a prerequisite for developing personalized medicinal therapies. In this review, we discuss network-assisted approaches in human disease research, with particular focus on the three major tasks. Network biology will provide powerful research platforms to dissect and interpret disease genomics data in the future.

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dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks

Abstract: dmGWAS package and documents are available at http://bioinfo.mc.vanderbilt.edu/dmGWAS.html.

Cited by 249 publications

References 31 publications

A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke

A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke

Genetic architecture of type 2 diabetes

Network-assisted approaches for human disease research

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