Tag: Whole Genome Association Studies

WGAViewer 1.25G – Genomic Annotation of Whole Genome Association Studies

WGAViewer 1.25G

:: DESCRIPTION

WGAViewer is a suite of JAVA software tools that provides a user-friendly interface to annotate, visualize, and help interpret the full set of P values emerging from a whole genome association (WGA) study.

::DEVELOPER

WGAViewer team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • Java 
:: DOWNLOAD

 WGAViewer

:: MORE INFORMATION

Citation

WGAViewer: software for genomic annotation of whole genome association studies.
Ge D, Zhang K, Need AC, Martin O, Fellay J, Urban TJ, Telenti A, Goldstein DB.
Genome Res. 2008 Apr;18(4):640-3. Epub 2008 Feb 6.

SNPAssoc 1.9-2 – SNPs-based Whole Genome Association Studies

SNPAssoc 1.9-2

:: DESCRIPTION

SNPassoc contains classes and methods to help the analysis of whole genome association studies. SNPassoc utilizes S4 classes and extends haplo.stats R package to facilitate haplotype analyses. The package is useful to carry out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis).

::DEVELOPER

Oncology Data Analytics Program (ODAP)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 SNPAssoc

:: MORE INFORMATION

Citation

Bioinformatics. 2007 Mar 1;23(5):644-5. Epub 2007 Jan 31.
SNPassoc: an R package to perform whole genome association studies.
González JR, Armengol L, Solé X, Guinó E, Mercader JM, Estivill X, Moreno V.

JAWAMix5 r2 – HDF5 based JAva implementation of Whole Genome Association Studies using Mixed models

JAWAMix5 r2

:: DESCRIPTION

JAWAMix5 is an out-of-core open-source toolkit for association mapping using high-throughput sequence data. Taking advantage of its HDF5-based implementation, JAWAMix5 stores genotype data on disk and accesses them as though stored in main memory. Therefore, it offers a scalable and fast analysis without concerns about memory usage, whatever the size of the dataset. We have implemented eight functions for association studies, including standard methods (linear models, mixed linear models, rare variants test, nested association mapping (NAM), and local variance component analysis), as well as a novel Bayesian local variance component analysis. Application to real data reveals new biological insights and demonstrates that JAWAMix5 is reasonably fast compared to traditional solutions that load the complete dataset into memory, and that the memory usage is efficient regardless of the dataset size.

::DEVELOPER

Quan Long

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Java

:: DOWNLOAD

 JAWAMix5

:: MORE INFORMATION

Citation

Bioinformatics. 2013 May 1;29(9):1220-2. doi: 10.1093/bioinformatics/btt122. Epub 2013 Mar 11.
JAWAMix5: an out-of-core HDF5-based java implementation of whole-genome association studies using mixed models.
Long Q, Zhang Q, Vilhjalmsson BJ, Forai P, Seren ü, Nordborg M.