GSA-SNP protocols

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Associated diseases

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GSA-SNP specifications

Information


Unique identifier OMICS_04385
Name GSA-SNP
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Input data Some marker association and gene sets.
Output data A list of significantly associated gene sets with P-values, corrected P-values, and their members that are sorted according their association strength by descending order.
Output format CSV
Operating system Unix/Linux, Windows
Programming languages Java
License GNU General Public License version 2.0
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Sangsoo Kim <>

Publication for GSA-SNP

GSA-SNP in pipelines

 (2)
2014
PMCID: 3999194
PMID: 24763700
DOI: 10.1371/journal.pone.0095866

[…] (cd36) and inflammation (pcsk6) –. two pathways identified by icsnpathway, the adaptive immune response and leukocyte mediated immunity pathways, did not overlap with pathways identified by gsa-snp. although not overlapped in both software packages, genes enriched in the two pathways have a putative connection with inflammation and cvds , . in addition, some pathways identified […]

2014
PMCID: 4427556
PMID: 25385369
DOI: 10.1038/mp.2014.142

[…] the gwas findings. we used hyst to calculate a summary p-value for each gene accounting for its size, ld structure, and constituent gwas snp associations. we then utilized these summary p-values and gsa-snp to identify pathways enriched with significant association to memory. pathway definitions, consisting of canonical representations of biological processes compiled by expert reviewers, […]


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GSA-SNP in publications

 (13)
PMCID: 5537640
PMID: 28632202
DOI: 10.1038/tp.2017.115

[…] 674 gene sets were tested. the results were adjusted for multiple testing using false discovery rate (fdr). to validate the significant finding, the respective gene set was investigated with (i) gsa-snp, using the p-value of the second-best snp in each gene (https://gsa.muldas.org) and (ii) magma using summary data and a nominal p-value threshold of p<0.05., to investigate a possible […]

PMCID: 5471232
PMID: 28615668
DOI: 10.1038/s41598-017-03826-2

[…] potential. usually, these methods assess pathway associations through gene associations that are often estimated from p-values of snps assigned to the genes via gene-based tests. for example, gsa-snp represents each gene by the p-value of its k-th best snp, and evaluates a pathway by calculating a summed z-score on the p-values of its genes relative to the random chance. kgg selects […]

PMCID: 4726092
PMID: 26780889
DOI: 10.1038/srep19429

[…] only pathways that are significant using the hyst test and also a hypergeometric test enrolling genes with a p-value of 0.001. we also performed a pathway analysis using an alternative program, gsa-snp, using the page (parametric analysis of gene set enrichment) method. when using gsa-snp, we selected the snp with the 2nd lowest p-value to define the gene p-values, and used a padding window […]

PMCID: 4642532
PMID: 26299439
DOI: 10.1038/srep13422

[…] p = 0.001) for the rac1 pathway, although it failed to pass the multiple test correction (fdr = 0.11). we further replicated our pathway associations in the adipogen consortium data by the gsa-snp method. our results suggest that rac1 and related cell motility pathways might be associated with plasma adiponectin levels and biological functions of adiponectin., adiponectin […]

PMCID: 4391470
PMID: 25887572
DOI: 10.1186/s12863-015-0191-2

[…] the performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. the pathway-level programs gengen, gsa-snp and magenta had the best performance while accounting for potential confounders., novel genes and pathways can be identified using the gene and pathway-level methods. these methods may […]


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GSA-SNP institution(s)
School of Nano-Biotech and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea; School of Computer Science and Engineering, Seoul National University, Seoul, South Korea; Medical Genomics Research Center, Korea Research Institute for Bioscience and Biotechnology, Daejeon, South Korea; Department of Bioinformatics and Life Science, Soongsil University, Seoul, South Korea
GSA-SNP funding source(s)
Supported by the National Institute for Mathematical Sciences, Daejeon, Korea (NIMS); the Basic Science Research Program through the National Research Foundation of Korea (NRF) (grant no. 2010-0016668); the MEST, and NRF (grant R11-2008-0062293).

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