SNaP Software Documentation

SNaP is a simulation program for haplotypic and genotypic data of single nucleotide polymorphisms (SNP)

SNaP stands as a robust simulation program designed for the analysis of haplotypic and genotypic data related to single nucleotide polymorphisms (SNPs).

The SNaP software generates data sets of sequences of single nucleotide polymorphisms (SNPs) and corresponding phenotypic expressions. It is as- sumed that the SNPs occur in independent blocks of linkage disequilibrium (LD).

Each of these blocks has the potential to harbor a causative SNP that can influence an optional phenotypic expression. This expression can manifest as either a qualitative trait (affection status) or a quantitative trait (QT). These features find application in diverse scenarios, ranging from haplotype-based association analysis to QTL (Quantitative Trait Locus) analysis.

SNaP Documentation

The program allows for

  • an unlimited number of unrelated individuals and nuclear families
  • a random or fixed number of children in the families
  • a random assignment of a phenotype conditional on the genotype at one or more SNP loci
  • a quantitative trait (QT) or a disease affection status as phenotypic expression
  • global, sex-specific, age-specific, and sex-age-specific penetrance and phenotypic mean values
  • two sampling schemes
  • a simple sequence design via LD blocks
  • SNPs that are potentially mono-allelic or stringently bi-allelic
  • several genotypic and haplotypic data output formats and other format options
  • some post-processing goodies like the introduction of genotyping errors and the removal of causative SNPs
Download SNaP Software

Parts of this work were done at the Rockefeller University, Laboratory of Statistical Genetics, New York, NY, U.S.A., and at the Max Delbrück Center for Molecular Medicine, Department of Bioinformatics, Berlin, Germany

When you use SNaP in publications, please cite the following reference:

Nothnagel M (2002). Simulation of LD block-structured SNP haplotype data and its use for the analysis of case-control data by supervised learning methods. Am J Hum Genet 71 (Suppl.)(4):A2363.