simDNAmixtures

The goal of simDNAmixtures is to provide an easy to use workflow for simulating single source or mixed forensic DNA profiles. These simulations are used in research and validation related to probabilistic genotyping systems and also in studies on relationship testing.

The simDNAmixtures package supports simulation of:

• Autosomal STR profiles (e.g. GlobalFiler™)

• Autosomal SNP profiles (e.g. Kintelligence or FORCE)

• YSTR profiles (e.g. Yfiler™ Plus)

Genotypes of the sample contributors may be provided as inputs. For autosomal profiles (STRs or SNPs) it is also possible to sample genotypes according to allele frequencies and a pedigree.

Installation

To install simDNAmixtures from CRAN:

install.packages("simDNAmixtures")

Alternatively, you can install the development version of simDNAmixtures from GitHub with:

# install.packages("devtools")
devtools::install_github("mkruijver/simDNAmixtures")

Example (GlobalFiler™ mixture of two siblings)

This example demonstrates how a mixed STR profile comprising two siblings can be simulated. More comprehensive examples of how to set up a simulation study can be found in the vignettes.

The first step is to define a pedigree with the two siblings and their parents using the pedtools package.

library(simDNAmixtures)
library(pedtools)

ped_fs <- nuclearPed(children = c("S1", "S2"))
plot(ped_fs)

Further, we load provided data including allele frequencies and data related to the GlobalFiler™ kit such as the locus names, size regression and stutter model.

# load allele frequencies
freqs <- read_allele_freqs(system.file("extdata","FBI_extended_Cauc_022024.csv",
                           package = "simDNAmixtures"))

# load kit data
gf <- gf_configuration()

We are now ready to sample a mixed STR profile. A gamma model is used with $\mu$ sampled uniformly between 50 and 5000 rfu and a coefficient of variation between 5 and 35%.

set.seed(1)

sampling_parameters <- list(min_mu = 50., max_mu = 5e3,
                           min_cv = 0.05, max_cv = 0.35,
                           degradation_shape1 = 0, degradation_shape2 = 0)

mixtures <- sample_mixtures(n = 1, contributors = c("S1", "S2"), 
                            pedigree = ped_fs, freqs = freqs,
                            sampling_parameters = sampling_parameters,
                            model_settings = gf$gamma_settings,
                            sample_model = sample_gamma_model)

The simulation results are stored in the mixtures object. Note that the results_directory argument to the sample_mixtures function may be used to automatically write results to disk. Below we print the simulated mixture data stored as mixtures$samples[[1]]$mixture.

Locus	Allele	Height	Size
D3S1358	14	216	117.33
D3S1358	15	4159	121.40
vWA	14	1512	168.84
vWA	17	180	180.95
vWA	18	2554	184.99
vWA	19	695	189.02
D16S539	9	1315	243.61
D16S539	10	1003	247.64
D16S539	12	138	255.70
D16S539	13	1990	259.73
CSF1PO	10	635	298.34
CSF1PO	11	1195	302.30
CSF1PO	12	1879	306.26
TPOX	8	847	349.70
TPOX	9	1185	353.72
TPOX	11	102	361.78
TPOX	12	1616	365.81
AMEL	X	1549	98.50
AMEL	Y	2616	104.50
D8S1179	10	1571	134.96
D8S1179	13	995	147.26
D8S1179	14	702	151.36
D21S11	29	267	203.65
D21S11	30	1952	207.69
D21S11	30.2	254	208.50
D21S11	31.2	1910	212.54
D18S51	12	374	281.63
D18S51	13	2473	285.67
D18S51	14	1789	289.71
D2S441	9	83	81.31
D2S441	10	3538	85.37
D2S441	11	851	89.42
D19S433	13	145	145.75
D19S433	13.2	971	146.55
D19S433	14	2355	149.74
TH01	8	2821	195.22
TH01	9	2190	199.38
FGA	21	97	255.94
FGA	22	1328	260.01
FGA	23	1614	264.08
FGA	24	73	268.15
D22S1045	11	2201	97.51
D22S1045	14	126	106.47
D22S1045	15	1513	109.46
D5S818	10	324	150.82
D5S818	11	1955	154.87
D5S818	12	995	158.92
D5S818	13	1777	162.97
D13S317	11	184	222.97
D13S317	12	4363	227.02
D7S820	11	2551	282.34
D7S820	12	660	286.32
SE33	17	1425	358.71
SE33	18	903	362.77
SE33	19	225	366.84
SE33	29.2	269	408.32
SE33	30.2	2077	412.39
D10S1248	13	1831	105.53
D10S1248	14	656	109.53
D1S1656	12	873	172.23
D1S1656	13	773	176.45
D1S1656	16	714	189.10
D1S1656	17.3	630	194.58
D12S391	17	132	228.10
D12S391	18	902	232.07
D12S391	19	124	236.04
D12S391	20	1475	240.01
D12S391	22	1053	247.96
D2S1338	17	672	304.78
D2S1338	19	1505	312.82
D2S1338	22	705	324.87
D2S1338	24	894	332.91

The genotypes of the two contributors are available as mixtures$samples[[1]]$contributor_genotypes.

Sample Name Locus Allele1 Allele2 S1 D3S1358 15 15 S1 vWA 18 19 S1 D16S539 9 13 S1 CSF1PO 11 12 S1 TPOX 8 12 S1 AMEL X Y S1 D8S1179 10 14 S1 D21S11 30 31.2 S1 D18S51 13 14 S1 D2S441 10 10 S1 D19S433 13.2 14 S1 TH01 8 9 S1 FGA 23 23 S1 D22S1045 11 15 S1 D5S818 11 13 S1 D13S317 12 12 S1 D7S820 11 12 S1 SE33 17 30.2 S1 D10S1248 13 14 S1 D1S1656 12 13 S1 D12S391 20 22 S1 D2S1338 19 22

Sample Name Locus Allele1 Allele2 S2 D3S1358 15 15 S2 vWA 14 18 S2 D16S539 10 13 S2 CSF1PO 10 12 S2 TPOX 9 12 S2 AMEL X Y S2 D8S1179 10 13 S2 D21S11 30 31.2 S2 D18S51 13 14 S2 D2S441 10 11 S2 D19S433 14 14 S2 TH01 8 9 S2 FGA 22 23 S2 D22S1045 11 15 S2 D5S818 11 12 S2 D13S317 12 12 S2 D7S820 11 11 S2 SE33 18 30.2 S2 D10S1248 13 13 S2 D1S1656 16 17.3 S2 D12S391 18 20 S2 D2S1338 17 24