GVCHAP 2.1 – Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers

GVCHAP 2.1

:: DESCRIPTION

GVCHAP is a computing pipeline for genomic prediction and variance component estimation using haplotypes and SNP markers.

::DEVELOPER

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

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/ Linux

:: DOWNLOAD

GVCHAP

:: MORE INFORMATION

Citation

Prakapenka D, Wang C, Liang Z, Bian C, Tan C, Da Y.
GVCHAP: A Computing Pipeline for Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers.
Front Genet. 2020 Apr 7;11:282. doi: 10.3389/fgene.2020.00282. PMID: 32318093; PMCID: PMC7154123.

HAPGEN 2.2.0 – Simulate Case Control Datasets at SNP Markers

HAPGEN 2.2.0

:: DESCRIPTION

HAPGEN simulates case control datasets at SNP markers. The new version can now simulate multiple disease SNPs on a single chromosome, on the assumption that each disease SNP acts independently and are in Hardy-Weinberg equilibrium. We also supply a R package that can simulate interaction between the disease SNPs.

::DEVELOPER

Jonathan Marchini

:: SCREENSHOTS

N/A

:: REQUIREMENTS

MacOsX / Linux

:: DOWNLOAD

HAPGEN

:: MORE INFORMATION

Citation

Chris C. A. Spencer, Zhan Su, Peter Donnelly, Jonathan Marchini (2009)
Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip.
PLoS Genet 5(5).

LRTag – Select SNP Markers across Multiple Populations

LRTag

:: DESCRIPTION

LRTag is a tool that is capable of efficiently selecting a near optimal set of SNP markers across multiple populations. The tool is named “LRTag” because the underlying algorithm is based on Lagrangian Relaxation.

::DEVELOPER

Yonghui Wu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/Windows
C Compiler

:: DOWNLOAD

LRTag

:: MORE INFORMATION

Citation:

Lan Liu, Yonghui Wu, Stefano Lonardi, Tao Jiang
Efficient Algorithms for genome-wide tagSNP selection across populations via the linkage disequilibrium criterion
Proc LSS Comput Syst Bioinform Conf. August, 2007. Vol. 6, p. 67-78.