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Method Article
Identification of SNP Targeted Pathways From Genome-wide Association Study (GWAS) Data
Burcu Bakir-Gungor
Sezerman Lab, Sabanci University
Corresponding Author
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The identification of the variants that explain familial risk of a specific disease is important since it enables the development of genetic risk prediction tests, diagnosis tools and therapeutical applications. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways, where a series of mutations occur in distinct genes. While each of these variations extends slightly the likelihood of having the disease, they work together to give birth to the perturbations in normal biological processes. We provide a protocol (termed PANOGA, Pathway and Network Oriented GWAS (Genome-wide association study) Analysis) to devise functionally important pathways through the identification of genes within these pathways, where these genes are targeted by single nucleotide polymorphisms (SNPs) obtained from the GWAS analysis. Additionally, PANOGA helps to identify other disease related genes, not targeted by the SNPs, which are also located within these affected pathways. The program accepts tab delimited or excel file containing SNP rsIDs vs. genotypic p-values and is available at: http://akademik.bahcesehir.edu.tr/~bbgungor/panoga_protocol.zip
Keywords
Genome-wide association study, GWAS, human complex disease, single nucleotide polymorphism, SNP, pathway, protein protein interaction network, SNP functional information
Figure 1
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- supplement0.doc
Table 1 Pathway based representation of PANOGA results, focusing on SNP targeted genes. The top 5 SNP targeted KEGG pathways are shown along with their KEGG term IDs, ranks and p-values in the 1st, 3rd and 4th columns, respectively. SNP targeted genes that are identified in PANOGA protocol are shown in the 5th column; along with the number of typed SNPs, shown in paranthesis. For each identified SNP targeted pathway, number of SNP targeted genes, number of associated SNPs in GWAS, number of regulatory GWAS SNPs and how many times this pathway is identified are shown in columns 6 to 9, respectively.
- supplement0.doc
Table 2 Pathway based representation of PANOGA results, focusing on subnetwork genes. The top 5 SNP targeted KEGG pathways are shown along with their KEGG term IDs, ranks and p-values in the 1st, 3rd and 4th columns, respectively. Pathway associated genes that are found in the subnetworks are shown in the 5th column. While the genes without ‘*’ symbol are SNP targeted genes (e.g. JAK2 gene in the Jak-STAT signaling pathway), the genes with ‘*’ symbol are identified in the subnetwork due to the neighbour effect (e.g. JAK1 gene in the Jak-STAT signaling pathway). The genes with neighbour effect (not targeted by SNPs) are incorporated using PPI network in the subnetwork identification step of PANOGA and they help to identify SNP targeted pathways, which can not be picked up using SNP targeted genes only. Column 6 displays other members (genes) of the identified snp targeted pathway, that are not found in PANOGA subnetworks.
- supplement0.doc
Table 3 Pathway based representation of PANOGA results, focusing on associated SNPs from GWAS and their associated genes (SNP targeted genes). The top 5 SNP targeted KEGG pathways are shown along with their KEGG term IDs, ranks and p-values in the 1st, 3rd and 4th columns, respectively. For each identified SNP targeted pathway, column 5 displays pathway associated genes found in subnetworks, along with the rs IDs of the typed SNPs and their functional consequences on the gene, shown in brackets. For example, STAT1 gene is a SNP targeted gene in Jak-STAT signaling pathway. Column 5 indicates that while rs3024912 and rs16833177 are located near the 3’ end of STAT1; rs1914408 and rs6718902 are located near the 5’ end of STAT1; and rs11687659 is located in the intronic region of STAT1.
- supplement0.doc
Table 4 Gene list representation of PANOGA for the identified SNP targeted pathways. For each SNP targeted gene, number of associated SNPs in GWAS, number of regulatory GWAS SNPs, how many times this pathway is identified, number of associated snp targeted pathways, list of these pathways, associated SNPs from GWAS, functional information regarding associated SNPs from GWAS, SNP regulatory potential and number of Regulatory SNPs are shown in columns 2 to 9, respectively.
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Associated Publications
A New Methodology to Associate SNPs with Human Diseases According to Their Pathway Related Context
by Burcu Bakir-Gungor, Osman Ugur Sezerman, and Joaquín Dopazo
PLoS ONE (28 May, 2012)
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This is a preprint. It has not completed peer review.
Method Article
Identification of SNP Targeted Pathways From Genome-wide Association Study (GWAS) Data
Burcu Bakir-Gungor
Sezerman Lab, Sabanci University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
No community comments so far
Version 1
Posted 29 May, 2012
No comments provided
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
The most recent version of this article is available here.
The identification of the variants that explain familial risk of a specific disease is important since it enables the development of genetic risk prediction tests, diagnosis tools and therapeutical applications. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways, where a series of mutations occur in distinct genes. While each of these variations extends slightly the likelihood of having the disease, they work together to give birth to the perturbations in normal biological processes. We provide a protocol (termed PANOGA, Pathway and Network Oriented GWAS (Genome-wide association study) Analysis) to devise functionally important pathways through the identification of genes within these pathways, where these genes are targeted by single nucleotide polymorphisms (SNPs) obtained from the GWAS analysis. Additionally, PANOGA helps to identify other disease related genes, not targeted by the SNPs, which are also located within these affected pathways. The program accepts tab delimited or excel file containing SNP rsIDs vs. genotypic p-values and is available at: http://akademik.bahcesehir.edu.tr/~bbgungor/panoga_protocol.zip
Figure 1
Figure 2
Figure 3
Figure 4
Associated Publications
A New Methodology to Associate SNPs with Human Diseases According to Their Pathway Related Context
by Burcu Bakir-Gungor, Osman Ugur Sezerman, and Joaquín Dopazo
PLoS ONE (28 May, 2012)