SimPed: A simulation program to generate haplotype and genotype data for pedigree structures. With the widespread availability of SNP genotype data, there is great interest in analyzing pedigree haplotype data. Intermarker linkage disequilibrium for microsatellite markers is usually low due to their physical distance; however, for dense maps of SNP markers, there can be strong linkage disequilibrium between marker loci. Linkage analysis (parametric and nonparametric) and family-based association studies are currently being carried out using dense maps of SNP marker loci. Monte Carlo methods are often used for both linkage and association studies; however, to date there are no programs available which can generate haplotype and/or genotype data consisting of a large number of loci for pedigree structures. SimPed is a program that quickly generates haplotype and/or genotype data for pedigrees of virtually any size and complexity. Marker data either in linkage disequilibrium or equilibrium can be generated for greater than 20,000 diallelic or multiallelic marker loci. Haplotypes and/or genotypes are generated for pedigree structures using specified genetic map distances and haplotype and/or allele frequencies. The simulated data generated by SimPed is useful for a variety of purposes, including evaluating methods that estimate haplotype frequencies for pedigree data, evaluating type I error due to intermarker linkage disequilibrium and estimating empirical p values for linkage and family-based association studies.

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References in zbMATH (referenced in 3 articles )

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  1. Papachristou, Charalampos: A population based confidence set inference method for SNPs that regulate quantitative phenotypes (2015)
  2. Duarte, Nubia E.; Giolo, Suely R.; Pereira, Alexandre C.; de Andrade, Mariza; Soler, Júlia P.: Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data (2014)
  3. Amato, Roberto; Pinelli, Michele; D’andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio: A novel approach to simulate gene-environment interactions in complex diseases (2010) ioport