MULTIPOW 2.1 – Power for Multi-stage Genome-wide Association Studies

MULTIPOW 2.1

:: DESCRIPTION

MULTIPOW calculates the power for both joint and replication-based analysis of general multi-stage genetic association studies.

::DEVELOPER

Peter Kraft

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX/ Windowsjava
  • R package

:: DOWNLOAD

 MULTIPOW

:: MORE INFORMATION

Citation

Adv Genet. 2008;60:465-504. doi: 10.1016/S0065-2660(07)00417-8.
Study designs for genome-wide association studies.
Kraft P, Cox DG.

RegionalP 1.0 – Region-based meta-analysis of Genome-wide Association Studies in genetically Diverse Populations

RegionalP 1.0

:: DESCRIPTION

RegionalP works by quantifying the degree of over-representation of associated SNPs in a pre-defined genomic region, given a specific definition of statistical significance.

::DEVELOPER

Saw Swee Hock School of Public Health

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 RegionalP

:: MORE INFORMATION

Citation

Eur J Hum Genet. 2012 Apr;20(4):469-75. doi: 10.1038/ejhg.2011.219. Epub 2011 Nov 30.
A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations.
Wang X, Liu X, Sim X, Xu H, Khor CC, Ong RT, Tay WT, Suo C, Poh WT, Ng DP, Liu J, Aung T, Chia KS, Wong TY, Tai ES, Teo YY.

EPISNPmpi 4.2 / epiSNP 4.2 – Epistasis Testing in Genome-wide Association Studies

EPISNPmpi 4.2

:: DESCRIPTION

EPISNPmpi is a parallel computing program for epistasis testing in genome-wide association studies for supercomputers and commodity clusters.

epiSNP is a computer package of serial computing programs for genome-wide testing of SNP epistasis and single-locus effects of complex or quantitative traits.

::DEVELOPER

JOHN GARBE AND YANG DA , Department of Animal Science, University of Minnesota

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows

:: DOWNLOAD

 EPISNPmpi ,  epiSNP

:: MORE INFORMATION

Citation

Ma L., H.B. Runesha, D. Dvorkin, J.R. Garbe, and Y. Da. (2008)
Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies.
BMC Bioinformatics 9:315.

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

YAMAS 2014 – Meta-analysis of Genome Wide Association Studies

YAMAS 2014

:: DESCRIPTION

YAMAS (Yet Another Meta-Analysis Software) is a software for meta-analysis of genome wide association studies.

::DEVELOPER

YAMAS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 YAMAS

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012 Sep 12;13:231. doi: 10.1186/1471-2105-13-231.
Quick, “imputation-free” meta-analysis with proxy-SNPs.
Meesters C1, Leber M, Herold C, Angisch M, Mattheisen M, Drichel D, Lacour A, Becker T.

IPGWAS 3.4 – Integrated Pipeline for Genome-Wide Association Studies

IPGWAS 3.4

:: DESCRIPTION

IPGWAS is an integrated pipeline for rational quality control and association analysis of genome-wide genetic studies.

::DEVELOPER

IPGWAS team

:: SCREENSHOTS

IPGWAS

:: REQUIREMENTS

  • Linux / WIndows/ MacOsX
  • Perl

:: DOWNLOAD

 IPGWAS

:: MORE INFORMATION

Citation

Biochem Biophys Res Commun. 2012 Jun 8;422(3):363-8. doi: 10.1016/j.bbrc.2012.04.117. Epub 2012 Apr 30.
IPGWAS: an integrated pipeline for rational quality control and association analysis of genome-wide genetic studies.
Fan YH1, Song YQ.

MultiMeta 0.1 – Meta-analysis of Multivariate Genome Wide Association Studies

MultiMeta 0.1

:: DESCRIPTION

The R package MultiMeta provides an implementation of the inverse-variance based method for meta-analysis, generalized to an n-dimensional setting.

::DEVELOPER

Dragana Vuckovic <dragana.vuckovic at burlo.trieste.it>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • R

:: DOWNLOAD

 MultiMeta

:: MORE INFORMATION

Citation:

MultiMeta: an R package for meta-analyzing multi-phenotype genome-wide association studies.
Vuckovic D, Gasparini P, Soranzo N, Iotchkova V.
Bioinformatics. 2015 Apr 22. pii: btv222

repfdr 1.2.3 – Replicability Analysis for Multiple Studies of High Dimension

repfdr 1.2.3

:: DESCRIPTION

repfdr is a tool for replicability analysis for genome-wide association studies.

::DEVELOPER

Ruth Heller

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • R

:: DOWNLOAD

 repfdr

:: MORE INFORMATION

Citation:

repfdr: A tool for replicability analysis for genome-wide association studies.
Heller R, Yaacoby S, Yekutieli D.
Bioinformatics. 2014 Jul 9. pii: btu434.

LDetect – Automated Analysis of Multiple Genome-wide Association Studies

LDetect

:: DESCRIPTION

LDetect is a method to identify approximately independent blocks of linkage disequilibrium (LD) in the human genome.

::DEVELOPER

LDetect team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 LDetect

:: MORE INFORMATION

Citation

Approximately independent linkage disequilibrium blocks in human populations.
Berisa T, Pickrell JK.
Bioinformatics. 2015 Sep 22. pii: btv546

FINEMAP 1.4 – Efficient Variable Selection using summary data from Genome-wide Association Studies

FINEMAP 1.4

:: DESCRIPTION

FINEMAP is a computationally efficient program for fine-mapping in genomic regions associated with complex diseases and traits via a shotgun stochastic search algorithm (Hans et al., 2007).

::DEVELOPER

Christian Benner

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsX / Linux

:: DOWNLOAD

 FINEMAP

:: MORE INFORMATION

Citation

FINEMAP: Efficient variable selection using summary data from genome-wide association studies.
Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M.
Bioinformatics. 2016 Jan 14. pii: btw018.