:: DESCRIPTION
MSS (Maximal Segmental Score) is an R program for the analysis of genome-wide association data
::DEVELOPER
Cathy S.J. Fann lab,Institute of Biomedical Informatics, National Yang-Ming University, Taipei
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Using maximal segmental score in genome-wide association studies.
Lin YC, Hsiao CL, Hsieh AR, Lian IeB, Fann CS.
Genet Epidemiol. 2012 Sep;36(6):594-601. doi: 10.1002/gepi.21652.
:: DESCRIPTION
Genomizer is a platform independent Java program for the analysis of genome wide association experiments.The software implements the workflow of an association experiment, including data management, single-point and haplotype analysis, “lead” definition, and data visualization.
::DEVELOPER
Institute for Clinical Molecular Biology
:: SCREENSHOTS
::REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Franke A, Wollstein A, Teuber M, Wittig M, Lu T, Hoffmann K, Nurnberg P, Krawczak M, Schreiber S, Hampe J (2006).
GENOMIZER: an integrated analysis system for genome-wide association data.
Human Mutat 27(6):583-588
:: DESCRIPTION
Screen & Clean is a program that identifies associations between SNP allele count data and a continuous or binary phenotype. The core function is a screen that identifies the first K SNPs to enter an L1-penalized regression of the phenotype on the allele counts, where K is chosen by a stability criterion. The program includes several optional procedures that are turned off by default, including a pre-screen using marginal regression p-values, a second screen for pairwise interaction effects, and a multivariate regression clean of the screened SNPs. K may also be chosen directly by the user.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Wu, Devlin, Ringquist, Trucco, and Roeder
“Screen and Clean: A Tool for Identifying Interactions in Genome-Wide Association Studies”
Genet Epidemiol. 2010 April; 34(3): 275–285.
:: DESCRIPTION
GenoWAP is a post-GWAS prioritization method that integrates genomic functional annotation and GWAS test statistics. After prioritization, real disease-associated loci become easier to be identified.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
GenoWAP: GWAS Signal Prioritization Through Integrated Analysis of Genomic Functional Annotation.
Lu Q, Yao X, Hu Y, Zhao H.
Bioinformatics. 2015 Oct 25. pii: btv610
:: DESCRIPTION
IMPUTE is a program for estimating (“imputing”) unobserved genotypes in SNP association studies. The program is designed to work seamlessly with the output of the genotype calling program CHIAMO and the population genetic simulator HAPGEN, and it produces output that can be analyzed using the program SNPTEST.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Rubinacci S, Delaneau O, Marchini J.
Genotype imputation using the Positional Burrows Wheeler Transform.
PLoS Genet. 2020 Nov 16;16(11):e1009049. doi: 10.1371/journal.pgen.1009049. PMID: 33196638; PMCID: PMC7704051.
B. N. Howie, P. Donnelly and J. Marchini (2009)
A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.
PLoS Genetics 5(6): e1000529
:: DESCRIPTION
GWApower is a R package for assessing the power of genome-wide association studies using commercially available genotyping chips. The package encapsulates extensive simulation results generated by the program HAPGEN.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Spencer, C., Su, Z., Donnelly, P. and Marchini, J.
Designing Genome-Wide Association Studies: sample size, power, and the choice of genotyping chip.
PLoS Genet. 2009 May;5(5):e1000477
:: DESCRIPTION
QCTOOL is a command-line utility program for basic quality control of gwas datasets. It supports the same file formats used by the WTCCC studies, as well as the binary file format described here, and is designed to work seamlessly with SNPTEST and related tools. QCTOOL computes per-sample and per-SNP summary statistics, and uses these to filter out samples and SNPs from the dataset (either by removing them from the files or by writing exclusion lists).
::DEVELOPER
Gavin Band , Jonathan Marchini
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
PLATO (PLatform for the Analysis, Translation, and Organization of large-scale data) is a system for the analysis of genome-wide association data that will incorporate several analytical approaches as filters to allow a scientist to choose whatever analytical methods they wish to apply. PLATO (PLatform for the Analysis, Translation, and Organization of large-scale data) will incorporate a number of filters to select the important SNPs in a genome-wide association study. PLATO was designed to aid in retrieving, evaluating, formatting, and analyzing genotypic and clinical data from the latest large-scale genotyping studies. PLATO implements a battery of quality control procedures to assess the data.
::DEVELOPER
:: SCREENSHOTS
N/A
::REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Finding unique filter sets in plato: a precursor to efficient interaction analysis in gwas data.
Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD.
Pac Symp Biocomput. 2010:315-26.
:: DESCRIPTION
SNPRuler finds epistatic interactions in GWASs. SNPRuler first uses the predictive rule learning to narrow down possible interactions among SNPs and then captures true interactions using χ2 statistic test. The rule-based strategy in our non-parametric learning approach enables our new method to search for interaction patterns more efficiently than existing methods. We conduct extensive experiments on both simulated data and real genome-wide data. The experimental results demonstrate that our new learning method is a powerful tool in handling large-scale SNP data both in terms of speed and detection of potential interactions that were not identified before.
::DEVELOPER
Laboratory for Bioinformatics and Computational Biology, HKUST
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Xiang Wan et al.
Predictive rule inference for epistatic interaction detection in genome-wide association studies
Bioinformatics (2010) 26 (1): 30-37.
:: DESCRIPTION
SNPHarvester detects SNP–SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions.
::DEVELOPER
Laboratory for Bioinformatics and Computational Biology, HKUST
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies
Can Yang, Zengyou He, Xiang Wan, Qiang Yang, Hong Xue and Weichuan Yu
Bioinformatics (2009) 25 (4): 504-511.
