Tag: GWAS

PTest 20130115 – Testing set of observed small P-values in GWAS

PTest 20130115

:: DESCRIPTION

PTest (Partition test) is a software for case-control genetic association studies in human gene mapping. The PTest is based on single-SNP p-values resulting from an association test, for example, the chi-square test comparing genotype or allele frequencies in cases and controls.

::DEVELOPER

Jurg Ott, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux

:: DOWNLOAD

 PTest

:: MORE INFORMATION

Citation

Challenging false discovery rate: a partition test based on p values in human case-control association studies.
Ott J, Liu Z, Shen Y.
Hum Hered. 2012;74(1):45-50. doi: 10.1159/000343752. Epub 2012 Nov 13.

GWAS Pathway Identifier 1.0.0 – Pathway- and Protein-Interaction-Based Identification of Disease Specific SNP Sets in GWAS

GWAS Pathway Identifier 1.0.0

:: DESCRIPTION

GWAS Pathway Identifier combines GWAS(Genome-Wide Accociation Studies) and pathway data as well as known and predicted protein-interaction data to identify disease specific SNP sets.

::DEVELOPER

the Interfaculty Institute for Biomedical Informatics (IBMI)

:: SCREENSHOTS

gwaspi

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Java

:: DOWNLOAD

 GWAS Pathway Identifier

:: MORE INFORMATION

metaCCA 1.20.0 – Multivariate meta-analysis of GWAS using Canonical Correlation Analysis

metaCCA 1.20.0

:: DESCRIPTION

metaCCA performs multivariate analysis of a single or multiple genome-wide association studies based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype.

::DEVELOPER

Matti Pirinen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R/ BioConductor

:: DOWNLOAD

metaCCA

:: MORE INFORMATION

Citation

Cichonska A, Rousu J, Marttinen P, Kangas AJ, Soininen P, Lehtimäki T, Raitakari OT, Järvelin MR, Salomaa V, Ala-Korpela M, Ripatti S, Pirinen M.
metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.
Bioinformatics. 2016 Jul 1;32(13):1981-9. doi: 10.1093/bioinformatics/btw052. Epub 2016 Feb 19. PMID: 27153689; PMCID: PMC4920109.

simGWAS 0.2.0-2 – Simulation of case-control GWAS Summary Statistics

simGWAS 0.2.0-2

:: DESCRIPTION

simGWAS is a fast method for simulation of large scale case-control GWAS (genome-wide association study) summary statistics

::DEVELOPER

Wallace group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • linux/ Windows/ MacOsX
  • R

:: DOWNLOAD

simGWAS

:: MORE INFORMATION

Citation

Fortune MD, Wallace C.
simGWAS: a fast method for simulation of large scale case-control GWAS summary statistics.
Bioinformatics. 2019 Jun 1;35(11):1901-1906. doi: 10.1093/bioinformatics/bty898. PMID: 30371734; PMCID: PMC6546134.

dmGWAS 3.0 – Genome-wide Association Studies (GWAS) Analysis

dmGWAS 3.0

:: DESCRIPTION

dmGWAS is designed to identify significant protein-protein interaction (PPI) modules and, from which, the candidate genes for complex diseases by an integrative analysis of GWAS dataset(s) and PPI network.

::DEVELOPER

Bioinformatics and Systems Medicine Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 dmGWAS

:: MORE INFORMATION

Citation:

EW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles.
Wang Q, Yu H, Zhao Z, Jia P.
Bioinformatics. 2015 Mar 24. pii: btv150.

Jia P, Zheng S, Long J, Zheng W, and Zhao Z (2011)
dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks.
Bioinformatics 27(1):95-102

glad – Gene Length Bias Detection in GWAS Datasets

glad

:: DESCRIPTION

glad is a software by randomly generating genomic intervals across the accessible genomic region to estimate the background distribution of P values at the gene level.

::DEVELOPER

Bioinformatics and Systems Medicine Laboratory

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

glad

:: MORE INFORMATION

Citation:

Int J Comput Biol Drug Des. 2010;3(4):297-310. doi: 10.1504/IJCBDD.2010.038394. Epub 2011 Feb 4.
Assessing gene length biases in gene set analysis of Genome-Wide Association Studies.
Jia P1, Tian J, Zhao Z.

JAMP 1.0 – multivariate GWAS Analysis

JAMP 1.0

:: DESCRIPTION

JAMP (Joint Genetic Association of Multiple Phenotypes) uses raw data as input and evaluates the multivariate evidence for association of multiple phenotypes for a SNP. It also provides a family-wise corrected P-value.

::DEVELOPER

CTG (Complex Traits Genetics) Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Python
  • PLINK

:: DOWNLOAD

 JAMP

:: MORE INFORMATION

JAG 1.1 – Gene-set Analysis in GWAS Datasets

JAG 1.1

:: DESCRIPTION

JAG (Joint Association of Genetic Variants) is a free open source tool to run gene-set analysis in GWAS data. It uses raw data as input and includes bot self-contained and competitive tests.

::DEVELOPER

CTG (Complex Traits Genetics) Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Python

:: DOWNLOAD

 JAG

:: MORE INFORMATION

TATES – multivariate GWAS based on P-values from GWAS

TATES

:: DESCRIPTION

TATES uses p-values as input and evaluates the evidence that at least one phenotype from multiple phenotypes is associated with a SNP.

::DEVELOPER

CTG (Complex Traits Genetics) Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Fortran Compiler

:: DOWNLOAD

 TATES

:: MORE INFORMATION

Citation

PLoS Genet. 2013;9(1):e1003235. doi: 10.1371/journal.pgen.1003235. Epub 2013 Jan 24.
TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies.
van der Sluis S1, Posthuma D, Dolan CV.

Exit mobile version