B-LORE – Bayesian multiple logistic Regression for GWAS Meta-analysis

B-LORE

:: DESCRIPTION

B-LORE (Bayesian LOgistic REgression) is a command line tool that creates summary statistics from multiple logistic regression on GWAS data, and combines the summary statistics from multiple studies in a meta-analysis. It can also incorporate functional information about the SNPs from other external sources. Several genetic regions, or loci are preselected for analysis with B-LORE.

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs
  • C Compiler
  • Python
:: DOWNLOAD

B-LORE

:: MORE INFORMATION

Citation:

PLoS Genet. 2018 Dec 31;14(12):e1007856. doi: 10.1371/journal.pgen.1007856. eCollection 2018 Dec.
Bayesian multiple logistic regression for case-control GWAS.
Banerjee S, Zeng L, Schunkert H, Söding J.

traseR 1.14.0 – GWAS Trait-associated SNP Enrichment Analyses in Genomic Intervals

traseR 1.14.0

:: DESCRIPTION

traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results.

::DEVELOPER

li chen<li.chen at emory.edu>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • R/ BioConductor

:: DOWNLOAD

 traseR

:: MORE INFORMATION

Citation:

traseR: an R package for performing trait-associated SNP enrichment analysis in genomic intervals.
Chen L, Qin Z.
Bioinformatics. 2015 Dec 18. pii: btv741.

GWASpower/QT 1.0 – Statistical Power Calculation software designed for GWAS

GWASpower/QT 1.0

:: DESCRIPTION

GWASpower/QT is a statistical power calculation software designed for genome wide association studies (GWAS) with quantitative traits in natural populations. It allows users to input the effect size as heritability measures, instead of the phenotype means of each genotype of the genetic marker, which is often unavailable in exploratory experiments such as GWAS. Input parameters are heritability (required), type 1 error rate (required), total sample size (required), linkage disequilibrium (optional) and other covariates (optional). The software returns the statistical power and a plot of a family of power curves

::DEVELOPER

GWASpower/QT team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 GWASpower/QT

:: MORE INFORMATION

PC-select – Calculation of GWAS Association Statistics

PC-select

:: DESCRIPTION

PC-select calculates GWAS association statistics using a data-adaptive GRM that improves power over standard mixed models while simultaneously avoiding confounding from population stratification.

::DEVELOPER

Berger Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

PC-select

:: MORE INFORMATION

Citation:

Genetics. 2014 Jul;197(3):1045-9. doi: 10.1534/genetics.114.164285. Epub 2014 Apr 29.
Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.
Tucker G, Price AL, Berger B

aSPU 1.39 – Adaptive Gene- and Pathway-Trait Association Testing with GWAS

aSPU 1.39

:: DESCRIPTION

aSPU is an R package for adaptive sum of powered score test(ASPU) in genetic association studies.

::DEVELOPER

Il-Youp Kwak

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX / Window
  • R

:: DOWNLOAD

 aSPU

:: MORE INFORMATION

Citation

Adaptive Gene- and Pathway-Trait Association Testing with GWAS Summary Statistics.
Kwak IY, Pan W.
Bioinformatics. 2015 Dec 10. pii: btv719.

PINBPA 1.1.8 – Cytoscape app for Network Analysis of GWAS data

PINBPA 1.1.8

:: DESCRIPTION

PINBPA (Protein interaction network-based pathway analysis) for genome-wide association studies (GWAS) has been developed as a Cytoscape app, to enable analysis of GWAS data in a network fashion.

::DEVELOPER

PINBPA team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/Windows/MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 PINBPA

:: MORE INFORMATION

Citation

PINBPA: Cytoscape app for network analysis of GWAS data.
Wang L, Matsushita T, Madireddy L, Mousavi P, Baranzini S.
Bioinformatics. 2014 Sep 25. pii: btu644

CPAG 0.2 – Cross-Phenotype Analysis of GWAS

CPAG 0.2

:: DESCRIPTION

CPAG can estimate disease and trait similarity, identify informative disease clusters, and carry out pathway enrichment analysis. It also provides visualization of these results in the form of hierarchical clustering trees, heatmaps, and networks.

::DEVELOPER

Rawls Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOS

:: DOWNLOAD

 CPAG

:: MORE INFORMATION

Citation:

CPAG: software for leveraging pleiotropy in GWAS to reveal similarity between human traits links plasma fatty acids and intestinal inflammation.
Wang L, Oehlers SH, Espenschied ST, Rawls JF, Tobin DM, Ko DC.
Genome Biol. 2015 Sep 15;16:190. doi: 10.1186/s13059-015-0722-1.

PSIKO v2 – Infer Population Stratification on various levels in GWAS

PSIKO v2

:: DESCRIPTION

PSIKO (Population Structure Inference using Kernel-pca and Optimisation) is a software tool written in C++ for quick and accurate estimation of individual ancestry coefficients of a dataset exhibiting population structure.

::DEVELOPER

The UEA Computational Biology Laboratory at the University of East Anglia (UEA)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • g++

:: DOWNLOAD

PSIKO

:: MORE INFORMATION

Citation

PSIKO2: a fast and versatile tool to infer population stratification on various levels in GWAS.
Popescu AA, Huber KT.
Bioinformatics. 2015 Jul 2. pii: btv396.

gdsfmt 1.4.0 / SNPRelate 0.9.19 – CoreArray Genomic Data Structure (GDS) R Interface / Parallel Computing Toolset for GWAS

gdsfmt 1.4.0 / SNPRelate 0.9.19

:: DESCRIPTION

gdsfmt and SNPRelate are high-performance computing R packages for multi-core symmetric multiprocessing computer architectures. They are used to accelerate two key computations is GWAS: principal component analysis (PCA) and relatedness analysis using identity-by-descent (IBD) measures. The kernels of our algorithms are written in C/C++, and have been highly optimized. Benchmarks show the uniprocessor implementations of PCA and IBD are ~8 to 50 times faster than the implementations provided by the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs respectively, and can be sped up to 30~300 folds by utilizing eight cores. SNPRelate can analyze tens of thousands of samples, with millions of SNPs.

::DEVELOPER

Xiuwen Zheng

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • R
  • BioConductor

:: DOWNLOAD

  gdsfmt / SNPRelate

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Dec 1;28(24):3326-8. doi: 10.1093/bioinformatics/bts606. Epub 2012 Oct 11.
A high-performance computing toolset for relatedness and principal component analysis of SNP data.
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS.

LocusTrack v1 – Integrated Visualisation of GWAS Results and Genomic Annotation

LocusTrack v1

:: DESCRIPTION

LocusTrack is a web-based application that annotates and creates plots of regional GWAS results and incorporates user-specified tracks that display annotations such as linkage disequilibrium (LD), phylogenetic conservation, chromatin state, and other genomic and regulatory elements.

::DEVELOPER

LocusTrack team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 LocusTrack

:: MORE INFORMATION

Citation

LocusTrack: Integrated visualization of GWAS results and genomic annotation.
Cuellar-Partida G, Renteria ME, MacGregor S.
Source Code Biol Med. 2015 Feb 3;10:1. doi: 10.1186/s13029-015-0032-8.