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INTERSNP is a software for genome-wide interaction analysis (GWIA) of case-control SNP data and quantitative traits. SNPs are selected for joint analysis using a priori information. Sources of information to define meaningful strategies can be statistical evidence (single marker association at a moderate level, computed from the own data) and genetic/biologic relevance (genomic location, function class or pathway information).

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New Version: v1.13.1 (4 June 2013).

Recent features:

Liability models:
  • Test for supra-multiplicativity of SNP sets.
INTERSNP-RARE:

  • Rare variant association tests: weighted FISHER-RARE (adapted from Fisher, 1925), Collapsing (Li and Leal, 2008), weighted burden test (e.g. Zawistowski et al., 2010)
  • Various variant weighting schemes: reciprocal allele SD, Beta-distribution, logistic weights (Wu et al., 2011)
  • Count-based and intervalfile-based binning methods
  • Fixed and Variable Threshold (VT) analysis (Price et al., 2010)
  • Imputation of minor allele frequencies in presence of missing data
  • Speed: parallelized permutation framework
  • Efficiency: major improvement in pretesting
  • Fully integrated in INTERSNP
  • Considerably improved performance and memory efficiency

  • Our software product INTERSNP implements:
    • A logistic regression framework as well as log-linear models for joint analysis of multiple SNPs
    • All PLINK input formats (ped/map, tped/tfam, bed/bim/fam) can be used
    • Automatic handling of SNP annotation and pathway information
    • Methods to account for multiple testing, in particular, Monte-Carlo simulations to judge genome-wide significance
    • A linear regression framework for analysis of quantitative traits
    • Pathway Association Analysis (SNP ratio, Fisher score, Gene ratio, Fisher Max, Fisher MaxPlus)
    • Genome-wide Haplotype Analysis
    • Pre-tests for quick analysis
    • Pathway Association Analysis with interaction-ratio

    INTERSNP started as a project of the Institute f. Medical Biometry, Informatics and Epidemiology, University of Bonn, Germany, and is now maintained and extended by the AG Genomic Mathematics in Neuroepidemiology at the German Center for Neurodegenerative Diseases, DZNE
    http://www.dzne.de/standorte/bonn/forschergruppen/becker.html


    Citation:
    Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T (2009) INTERSNP: Genome-wide Interaction Analysis Guided by a priori Information. Bioinformatics. 2009 Dec 15;25(24):3275-81