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Simulate SNP genotype data from a pedigree, with optional missingness, genotyping errors, and non-genotyped parents.


  nSnp = 400,
  ParMis = c(0, 0),
  MAF = 0.3,
  CallRate = 0.99,
  SnpError = 5e-04,
  ErrorFV = function(E) c((E/2)^2, E - (E/2)^2, E/2),
  ErrorFM = NULL,
  ReturnStats = FALSE,
  quiet = FALSE



dataframe, pedigree with the first three columns being id - dam - sire, additional columns are ignored.


number of SNPs to simulate.


single number or vector length two with proportion of parents with fully missing genotype. Ignored if CallRate is a named vector. NOTE: default changed from 0.4 (up to version 2.8.5) to 0 (from version 2.9).


either a single number with minimum minor allele frequency, and allele frequencies will be sampled uniformly between this minimum and 0.5, OR a vector with minor allele frequency at each locus. In both cases, this is the MAF among pedigree founders, the MAF in the sample will deviate due to drift.


either a single number for the mean call rate (genotyping success), OR a vector with the call rate at each SNP, OR a named vector with the call rate for each individual. In the third case, ParMis is ignored, and individuals in the pedigree (as id or as parent) not included in this vector are presumed non-genotyped.


either a single value which will be combined with ErrorFV, or a length 3 vector with probabilities (observed given actual) hom|other hom, het|hom, and hom|het; OR a vector or 3XnSnp matrix with the genotyping error rate(s) for each SNP.


function taking the error rate (scalar) as argument and returning a length 3 vector with hom->other hom, hom->het, het->hom.


function taking the error rate (scalar) as argument and returning a 3x3 matrix with probabilities that actual genotype i (rows) is observed as genotype j (columns). See below for details. To use, set ErrorFV = NULL


in addition to the genotype matrix, return the input parameters and mean & quantiles of MAF, error rate and call rates.


suppress messages.


If ReturnStats=FALSE (the default), a matrix with genotype data in sequoia's input format, encoded as 0/1/2/-9.

If ReturnStats=TRUE, a named list with three elements: list 'ParamsIN', matrix 'SGeno', and list 'StatsOUT':


Frequency in 'observed' genotypes of '1' allele


Allele frequency in 'actual' (without genotyping errors & missingness)


Error rate per SNP (actual /= observed AND observed /= missing)


Non-missing per SNP


Error rate per individual


Non-missing per individual


For founders, i.e. individuals with no known parents, genotypes are drawn according to the provided MAF and assuming Hardy-Weinberg equilibrium. Offspring genotypes are generated following Mendelian inheritance, assuming all loci are completely independent. Individuals with one known parent are allowed: at each locus, one allele is inherited from the known parent, and the other drawn from the genepool according to the provided MAF.

Genotyping errors

If SnpError is a length 3 vector, genotyping errors are generated following a length 3 vector with probabilities that 1) an actual homozygote is observed as the other homozygote, 2) an actual homozygote is observed as a heterozygote, and 3) an heterozygote is observed as an homozygote. The only assumption made is that the two alleles can be treated equally, i.e. observing actual allele $A$ as $a$ is as likely as observing actual $a$ as $A$.

If SnpError is a single value, by default this is interpreted as a locus-level error rate (rather than allele-level), and equals the probability that a homozygote is observed as heterozygote, and the probability that a heterozygote is observed as either homozygote (i.e., the probability that it is observed as AA = probability that observed as aa = SnpError/2). The probability that one homozygote is observed as the other is (SnpError/2\()^2\). How this single value is rendered into a 3x3 error matrix is fully flexible and specified via ErrorFM; see link{ErrToM} for details.

The default values of SnpError=5e-4 and ErrorFM='version2.0' correspond to the length 3 vector c((5e-4/2)^2, 5e-4/2, 5e-4*(1-5e-4/2)).

A beta-distribution is used to simulate variation in the error rate between SNPs, the shape parameter of this distribution can be specified via MkGenoErrors. It is also possible to specify the error rate per SNP.

Call Rate

Variation in call rates across SNPs is assumed to follow a highly skewed (beta) distribution, with many SNPs having call rates close to 1, and a narrowing tail of lower call rates. The first shape parameter defaults to 1 (but see MkGenoErrors), and the second shape parameter is defined via the mean as CallRate. For 99.9% of SNPs to have a call rate of 0.8 (0.9; 0.95) or higher, use a mean call rate of 0.969 (0.985; 0.993).

Variation in call rate between samples can be specified by providing a named vector to CallRate. Otherwise, variation in call rate and error rate between samples occurs only as side-effect of the random nature of which individuals are hit by per-SNP errors and drop-outs. Finer control is possible by first generating an error-free genotype matrix, and then calling MkGenoErrors directly on (subsets of) the matrix.


This simulation is highly simplistic and assumes that all SNPs segregate completely independently, that the SNPs are in Hardy-Weinberg equilibrium in the pedigree founders. It assumes that genotyping errors are not due to heritable mutations of the SNPs, and that missingness is random and not e.g. due to heritable mutations of SNP flanking regions. Results based on this simulated data will provide an minimum estimate of the number of SNPs required, and an optimistic estimate of pedigree reconstruction performance.

See also

The wrapper EstConf for repeated simulation and pedigree reconstruction; MkGenoErrors for fine control over the distribution of genotyping errors in simulated data; ErrToM for more information about genotyping error patterns.


Jisca Huisman,


Geno_A <- SimGeno(Pedigree = Ped_griffin, nSnp=200, ParMis=c(0.1, 0.6),
                  MAF = 0.25, SnpError = 0.001)

Geno_B <- SimGeno(Pedigree = Ped_HSg5, nSnp = 100, ParMis = 0.2,
                 SnpError = c(0.01, 0.04, 0.1))

Geno_C <- SimGeno(Pedigree = Ped_griffin, nSnp=200, ParMis=0, CallRate=0.6,
                  SnpError = 0.05, ErrorFV=function(E) c(E/10, E/10, E))

# genotype matrix with duplicated samples:
Dups_grif <- data.frame(ID1 = c('i006_2001_M', 'i021_2002_M', 'i064_2004_F'))
Dups_grif$ID2 <- paste0(Dups_grif$ID1, '_2')
Err <- c(0.01, 0.04, 0.1)
Geno_act <- SimGeno(Ped_griffin, nSnp=500, ParMis=0, CallRate=1, SnpError=0)
Geno_sim <- MkGenoErrors(Geno_act, SnpError=Err, CallRate=0.99)
Geno_dups <- MkGenoErrors(Geno_act[Dups_grif$ID1, ], SnpError=Err,
rownames(Geno_dups) <- Dups_grif$ID2
Geno_sim <- rbind(Geno_sim, Geno_dups)