Quick Example of Simulated Kinships with Partial Parentage

nprcgenekeepr: an R Package for the Genetic Management of Colonies

R. Mark Sharp, Ph.D.

2026-11-12

Introduction

This vignette provides a quick example of how to estimate kinship coefficients using simulation. The same setup is run with 10, 100 and 500 simulations to illustrate the type of kinship coefficient estimate variance you can expect with a simple pedigree exhibiting partial parentage for some of the members.

Anticipation of further uses of this kinship estimation method

Retention of founder alleles is a primary driving force behind this package of utilities. This example is intentionally overly simplistic to clearly illustrate this one aspect of the methodology used with realistic pedigrees.

In this example, some of the potential parents are not members of the example pedigree and thus, by definition, have a kinship coefficient of 0.0 with all other pedigree members.

In practice most or all of the potential parents will be members of the same pedigree and will potentially have nonzero kinship coefficients with other pedigree members.

Identification of potential parents

This example writes its lists of potential parents by hand. getPotentialParents builds them from a pedigree: for each animal born at the center that has a birth date and a missing sire or dam, it lists the animals old enough, and present at the right time, to be that parent, from the birth and exit dates and the species’ breeding ages and gestation length. Each element of its result holds id, sires and dams, as the lists in allSimParents below do, so the result can be passed to createSimKinships as it is. The Potential Parents tab of the Shiny application shows the same lists.

A planned capability will also use timestamped transactional location data to identify potential parents for animals with a missing parent.

Creation of example pedigree¹

The example uses the package’s small pedigree, smallPed, which is shown after the setup code below. All of its animals have identifiers that are single letters of the alphabet.

Each simParent_n list names one animal (id) and the animals that could be its sire (sires) and its dam (dams). In each simulated pedigree, a parent that is not recorded is drawn at random from the animal’s list, and a recorded parent is always kept. This example gives lists to B, E, J, K and N, which have neither parent recorded, and to A, which has no recorded dam. A’s sire, Q, is recorded, so the Q in simParent_1 changes nothing. O and Q have no list and stay founders. Potential parents can come from the pedigree itself or from outside it: the identifiers with an underscore, such as s1_1 and d3_1, are not in smallPed.

To give some potential parents more weight than others, list them more than once: each is drawn in proportion to the number of times it is listed.

knitr::opts_chunk$set(echo = TRUE)
library(knitr) # nolint: undesirable_function_linter
library(nprcgenekeepr) # nolint: undesirable_function_linter
library(stringi) # nolint: undesirable_function_linter
## kableExtra is optional: it styles the tables when it is installed.
kable_styling <- if (requireNamespace("kableExtra", quietly = TRUE)) { # nolint: object_name_linter
  kableExtra::kable_styling
} else {
  function(kable_input, ...) kable_input
}

ped <- nprcgenekeepr::smallPed
simParent_1 <- list( # nolint: object_name_linter
  id = "A", # nolint: object_name_linter
  sires = "Q",
  dams = c("d1_1", "d1_2", "d1_3", "d1_4")
)
simParent_2 <- list( # nolint: object_name_linter
  id = "B", # nolint: object_name_linter
  sires = c("s1_1", "s1_2", "s1_3"),
  dams = c("d1_1", "d1_2", "d1_3", "d1_4")
)
simParent_3 <- list( # nolint: object_name_linter
  id = "E", # nolint: object_name_linter
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
simParent_4 <- list( # nolint: object_name_linter
  id = "J", # nolint: object_name_linter
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "B")
)
simParent_5 <- list( # nolint: object_name_linter
  id = "K", # nolint: object_name_linter
  sires = c("A", "C", "s1_1"),
  dams = c("d3_1", "d1_2")
)
simParent_6 <- list( # nolint: object_name_linter
  id = "N", # nolint: object_name_linter
  sires = c("A", "C", "s1_2"),
  dams = c("d3_1", "B")
)
allSimParents <- list(
  simParent_1, simParent_2, simParent_3,
  simParent_4, simParent_5, simParent_6
)

extractKValue <- function(kValue, id1, id2, simulation) {
  row <- kValue$id_1 == id1 & kValue$id_2 == id2
  vapply(paste0("sim_", simulation), function(column) kValue[[column]][row],
         numeric(1L))
}

ped[, c("id", "sire", "dam", "sex")]

##    id sire  dam sex
## 1   A    Q <NA>   M
## 2   B <NA> <NA>   F
## 3   C    A    B   M
## 4   D    A    B   M
## 5   E <NA> <NA>   F
## 6   F    D    E   F
## 7   G    D    E   F
## 8   H    A    B   M
## 9   I    A    J   F
## 10  J <NA> <NA>   F
## 11  K <NA> <NA>   F
## 12  L    C    K   F
## 13  M    A    N   M
## 14  N <NA> <NA>   F
## 15  O <NA> <NA>   F
## 16  P    M    O   F
## 17  Q <NA> <NA>   M

Small Example

This is the simulation. Only the pairs whose kinship varies between the simulated pedigrees are counted, and the first 10 of them are printed.

Before running these simulations, take time to look at the help pages of the package functions used here to see what they expect as arguments and what they return. (extractKValue is defined in the setup code above, not in the package.)

?createSimKinships
?kinshipMatricesToKValues
?countKinshipValues
?summarizeKinshipValues

# Only set this seed if you want to get the same simulation results each time.
set.seed(1L)
n <- 10L
simKinships <- createSimKinships(ped, allSimParents, pop = ped$id, n = n)
kValues <- kinshipMatricesToKValues(simKinships)
extractKValue(kValues, id1 = "A", id2 = "F", simulation = 1L:n)

##  sim_1  sim_2  sim_3  sim_4  sim_5  sim_6  sim_7  sim_8  sim_9 sim_10 
##  0.125  0.125  0.125  0.125  0.250  0.125  0.125  0.250  0.250  0.125

counts <- countKinshipValues(kValues)
counts$kIds[1L:3L]

## [[1]]
## [1] "A" "A"
## 
## [[2]]
## [1] "A" "B"
## 
## [[3]]
## [1] "A" "C"

counts$kValues[1L:3L]

## [[1]]
## [1] 0.5
## 
## [[2]]
## [1] 0
## 
## [[3]]
## [1] 0.25

counts$kCounts[1L:3L]

## [[1]]
## [1] 10
## 
## [[2]]
## [1] 10
## 
## [[3]]
## [1] 10

stats_10 <- summarizeKinshipValues(counts)
nrow(stats_10[stats_10$sd > 0.0, ])

## [1] 78

kable(head(stats_10[stats_10$sd > 0.0, ], 10L), longtable = TRUE) |>
  kable_styling(
    latex_options = c("striped", "repeat_header"),
    repeat_header_method = "replace",
    repeat_header_text = "\\textit{(continued)}"
  )

	id_1	id_2	min	secondQuartile	mean	median	thirdQuartile	max	sd
5	A	E	0.000	0.000	0.0750	0.0000	0.250	0.250	0.1207615
6	A	F	0.125	0.125	0.1625	0.1250	0.250	0.250	0.0603807
7	A	G	0.125	0.125	0.1625	0.1250	0.250	0.250	0.0603807
9	A	I	0.250	0.250	0.3125	0.3125	0.375	0.375	0.0658808
10	A	J	0.000	0.000	0.1250	0.1250	0.250	0.250	0.1317616
14	A	N	0.000	0.000	0.0500	0.0000	0.000	0.250	0.1054093
21	B	E	0.000	0.000	0.1500	0.2500	0.250	0.250	0.1290994
22	B	F	0.125	0.125	0.2000	0.2500	0.250	0.250	0.0645497
23	B	G	0.125	0.125	0.2000	0.2500	0.250	0.250	0.0645497
25	B	I	0.000	0.000	0.0750	0.1250	0.125	0.125	0.0645497

A larger simulation

set.seed(1L)
n <- 100L
simKinships <- createSimKinships(ped, allSimParents, pop = ped$id, n = n)
kValues <- kinshipMatricesToKValues(simKinships)
extractKValue(kValues, id1 = "A", id2 = "F", simulation = 1L:10L)

##  sim_1  sim_2  sim_3  sim_4  sim_5  sim_6  sim_7  sim_8  sim_9 sim_10 
##  0.125  0.125  0.125  0.125  0.250  0.125  0.125  0.250  0.250  0.125

counts <- countKinshipValues(kValues)
counts$kIds[1L:3L]

## [[1]]
## [1] "A" "A"
## 
## [[2]]
## [1] "A" "B"
## 
## [[3]]
## [1] "A" "C"

counts$kValues[1L:3L]

## [[1]]
## [1] 0.5
## 
## [[2]]
## [1] 0
## 
## [[3]]
## [1] 0.25

counts$kCounts[1L:3L]

## [[1]]
## [1] 100
## 
## [[2]]
## [1] 100
## 
## [[3]]
## [1] 100

stats_100 <- summarizeKinshipValues(counts)
nrow(stats_100[stats_100$sd > 0.0, ])

## [1] 94

kable(head(stats_100[stats_100$sd > 0.0, ], 10L), longtable = TRUE) |>
  kable_styling(
    latex_options = c("striped", "repeat_header"),
    repeat_header_method = "replace",
    repeat_header_text = "\\textit{(continued)}"
  )

	id_1	id_2	min	secondQuartile	mean	median	thirdQuartile	max	sd
5	A	E	0.000	0.000	0.08250	0.000	0.250	0.250	0.1181454
6	A	F	0.125	0.125	0.16625	0.125	0.250	0.250	0.0590727
7	A	G	0.125	0.125	0.16625	0.125	0.250	0.250	0.0590727
9	A	I	0.250	0.250	0.29875	0.250	0.375	0.375	0.0612759
10	A	J	0.000	0.000	0.09750	0.000	0.250	0.250	0.1225518
11	A	K	0.000	0.000	0.07000	0.000	0.250	0.250	0.1128152
12	A	L	0.125	0.125	0.16000	0.125	0.250	0.250	0.0564076
14	A	N	0.000	0.000	0.08000	0.000	0.250	0.250	0.1172065
21	B	E	0.000	0.000	0.13250	0.250	0.250	0.250	0.1254034
22	B	F	0.125	0.125	0.19125	0.250	0.250	0.250	0.0627017

A much larger simulation

set.seed(1L)
n <- 500L
simKinships <- createSimKinships(ped, allSimParents, pop = ped$id, n = n)
kValues <- kinshipMatricesToKValues(simKinships)
extractKValue(kValues, id1 = "A", id2 = "F", simulation = 1L:10L)

##  sim_1  sim_2  sim_3  sim_4  sim_5  sim_6  sim_7  sim_8  sim_9 sim_10 
##  0.125  0.125  0.125  0.125  0.250  0.125  0.125  0.250  0.250  0.125

counts <- countKinshipValues(kValues)
counts$kIds[1L:3L]

## [[1]]
## [1] "A" "A"
## 
## [[2]]
## [1] "A" "B"
## 
## [[3]]
## [1] "A" "C"

counts$kValues[1L:3L]

## [[1]]
## [1] 0.5
## 
## [[2]]
## [1] 0
## 
## [[3]]
## [1] 0.25

counts$kCounts[1L:3L]

## [[1]]
## [1] 500
## 
## [[2]]
## [1] 500
## 
## [[3]]
## [1] 500

stats_500 <- summarizeKinshipValues(counts)
nrow(stats_500[stats_500$sd > 0.0, ])

## [1] 94

kable(head(stats_500[stats_500$sd > 0.0, ], 10L), longtable = TRUE) |>
  kable_styling(
    latex_options = c("striped", "repeat_header"),
    repeat_header_method = "replace",
    repeat_header_text = "\\textit{(continued)}"
  )

	id_1	id_2	min	secondQuartile	mean	median	thirdQuartile	max	sd
5	A	E	0.000	0.000	0.08650	0.000	0.250	0.250	0.1190424
6	A	F	0.125	0.125	0.16825	0.125	0.250	0.250	0.0595212
7	A	G	0.125	0.125	0.16825	0.125	0.250	0.250	0.0595212
9	A	I	0.250	0.250	0.29200	0.250	0.375	0.375	0.0591015
10	A	J	0.000	0.000	0.08400	0.000	0.250	0.250	0.1182030
11	A	K	0.000	0.000	0.08000	0.000	0.250	0.250	0.1167358
12	A	L	0.125	0.125	0.16500	0.125	0.250	0.250	0.0583679
14	A	N	0.000	0.000	0.08200	0.000	0.250	0.250	0.1174888
21	B	E	0.000	0.000	0.12550	0.250	0.250	0.250	0.1251242
22	B	F	0.125	0.125	0.18775	0.250	0.250	0.250	0.0625621

Comparing the values and variation found for the various kinship values:

stats_short <- stats_10[stats_10$sd > 0.0, ]
stats_long <- stats_500[stats_500$sd > 0.0, ]

## Align the short and long summaries by animal pair: the sd > 0 sets can
## differ in size between simulation lengths, so join on the ID pair rather
## than by row position.
merged <- merge(stats_short, stats_long, by = c("id_1", "id_2"),
                suffixes = c("_short", "_long"))

comparison <- data.frame(
  id_1 = merged$id_1,
  id_2 = merged$id_2,
  meanKin_short = merged$mean_short,
  meanKin_long = merged$mean_long,
  meanKinDelta = abs(merged$mean_short - merged$mean_long),
  sdKin_short = merged$sd_short,
  sdKin_long = merged$sd_long,
  sdKinDelta = abs(merged$sd_short - merged$sd_long)
)

kable(comparison,
  longtable = TRUE,
  digits = c(0L, 0L, 4L, 4L, 4L, 4L, 4L, 4L),
  caption = stri_c(
    "Comparison of estimated kinships between simulations ",
    "of 10 (short) and 500 (long)"
  )
) |>
  kable_styling(
    latex_options = c("striped", "repeat_header"),
    repeat_header_method = "replace",
    repeat_header_text = "\\textit{(continued)}",
    font_size = 10L
  )

Comparison of estimated kinships between simulations of 10 (short) and 500 (long)

id_1	id_2	meanKin_short	meanKin_long	meanKinDelta	sdKin_short	sdKin_long	sdKinDelta
A	E	0.0750	0.0865	0.0115	0.1208	0.1190	0.0017
A	F	0.1625	0.1683	0.0058	0.0604	0.0595	0.0009
A	G	0.1625	0.1683	0.0058	0.0604	0.0595	0.0009
A	I	0.3125	0.2920	0.0205	0.0659	0.0591	0.0068
A	J	0.1250	0.0840	0.0410	0.1318	0.1182	0.0136
A	N	0.0500	0.0820	0.0320	0.1054	0.1175	0.0121
B	E	0.1500	0.1255	0.0245	0.1291	0.1251	0.0040
B	F	0.2000	0.1878	0.0123	0.0645	0.0626	0.0020
B	G	0.2000	0.1878	0.0123	0.0645	0.0626	0.0020
B	I	0.0750	0.0583	0.0167	0.0645	0.0624	0.0021
B	J	0.1500	0.1165	0.0335	0.1291	0.1248	0.0043
B	N	0.0500	0.1205	0.0705	0.1054	0.1250	0.0196
C	E	0.1125	0.1060	0.0065	0.0922	0.0846	0.0077
C	F	0.1812	0.1780	0.0033	0.0461	0.0423	0.0038
C	G	0.1812	0.1780	0.0033	0.0461	0.0423	0.0038
C	I	0.1938	0.1751	0.0186	0.0355	0.0407	0.0052
C	J	0.1375	0.1003	0.0373	0.0710	0.0814	0.0104
C	N	0.0500	0.1013	0.0513	0.0874	0.0876	0.0002
D	E	0.1125	0.1060	0.0065	0.0922	0.0846	0.0077
D	F	0.3062	0.3030	0.0033	0.0461	0.0423	0.0038
D	G	0.3062	0.3030	0.0033	0.0461	0.0423	0.0038
D	I	0.1938	0.1751	0.0186	0.0355	0.0407	0.0052
D	J	0.1375	0.1003	0.0373	0.0710	0.0814	0.0104
D	N	0.0500	0.1013	0.0513	0.0874	0.0876	0.0002
E	F	0.3062	0.3030	0.0033	0.0461	0.0423	0.0038
E	G	0.3062	0.3030	0.0033	0.0461	0.0423	0.0038
E	H	0.1125	0.1060	0.0065	0.0922	0.0846	0.0077
E	I	0.0875	0.0740	0.0135	0.0791	0.0751	0.0039
E	J	0.1000	0.0615	0.0385	0.0791	0.0761	0.0030
E	K	0.0625	0.0290	0.0335	0.0659	0.0528	0.0131
E	L	0.0875	0.0675	0.0200	0.0323	0.0530	0.0207
E	M	0.0375	0.0432	0.0057	0.0604	0.0595	0.0009
E	N	0.0375	0.0562	0.0188	0.0604	0.0716	0.0112
E	P	0.0187	0.0216	0.0029	0.0302	0.0298	0.0004
E	Q	0.0375	0.0432	0.0057	0.0604	0.0595	0.0009
F	F	0.5562	0.5530	0.0032	0.0461	0.0423	0.0038
F	G	0.3062	0.3030	0.0033	0.0461	0.0423	0.0038
F	H	0.1812	0.1780	0.0033	0.0461	0.0423	0.0038
F	I	0.1406	0.1246	0.0161	0.0423	0.0473	0.0050
F	J	0.1187	0.0809	0.0379	0.0547	0.0655	0.0108
F	K	0.0312	0.0345	0.0033	0.0329	0.0444	0.0114
F	L	0.1062	0.1062	0.0000	0.0161	0.0322	0.0161
F	M	0.0813	0.0841	0.0029	0.0302	0.0298	0.0004
F	N	0.0437	0.0788	0.0350	0.0593	0.0675	0.0082
F	P	0.0406	0.0421	0.0014	0.0151	0.0149	0.0002
F	Q	0.0813	0.0841	0.0029	0.0302	0.0298	0.0004
G	G	0.5562	0.5530	0.0032	0.0461	0.0423	0.0038
G	H	0.1812	0.1780	0.0033	0.0461	0.0423	0.0038
G	I	0.1406	0.1246	0.0161	0.0423	0.0473	0.0050
G	J	0.1187	0.0809	0.0379	0.0547	0.0655	0.0108
G	K	0.0312	0.0345	0.0033	0.0329	0.0444	0.0114
G	L	0.1062	0.1062	0.0000	0.0161	0.0322	0.0161
G	M	0.0813	0.0841	0.0029	0.0302	0.0298	0.0004
G	N	0.0437	0.0788	0.0350	0.0593	0.0675	0.0082
G	P	0.0406	0.0421	0.0014	0.0151	0.0149	0.0002
G	Q	0.0813	0.0841	0.0029	0.0302	0.0298	0.0004
H	I	0.1938	0.1751	0.0186	0.0355	0.0407	0.0052
H	J	0.1375	0.1003	0.0373	0.0710	0.0814	0.0104
H	N	0.0500	0.1013	0.0513	0.0874	0.0876	0.0002
I	I	0.5625	0.5420	0.0205	0.0659	0.0591	0.0068
I	J	0.3125	0.2920	0.0205	0.0659	0.0591	0.0068
I	K	0.0063	0.0524	0.0461	0.0198	0.0671	0.0474
I	L	0.1000	0.1138	0.0137	0.0198	0.0394	0.0196
I	M	0.1562	0.1460	0.0103	0.0329	0.0296	0.0034
I	N	0.0562	0.0670	0.0108	0.0952	0.0735	0.0218
I	P	0.0781	0.0730	0.0051	0.0165	0.0148	0.0017
I	Q	0.1562	0.1460	0.0103	0.0329	0.0296	0.0034
J	K	0.0125	0.0248	0.0122	0.0395	0.0499	0.0103
J	L	0.0750	0.0625	0.0125	0.0395	0.0499	0.0104
J	M	0.0625	0.0420	0.0205	0.0659	0.0591	0.0068
J	N	0.0625	0.0520	0.0105	0.1062	0.0728	0.0334
J	P	0.0312	0.0210	0.0102	0.0329	0.0296	0.0034
J	Q	0.0625	0.0420	0.0205	0.0659	0.0591	0.0068
K	N	0.0250	0.0275	0.0025	0.0527	0.0518	0.0009
L	N	0.0375	0.0644	0.0269	0.0527	0.0540	0.0013
M	N	0.0250	0.0410	0.0160	0.0527	0.0587	0.0060
N	P	0.0125	0.0205	0.0080	0.0264	0.0294	0.0030
N	Q	0.0250	0.0410	0.0160	0.0527	0.0587	0.0060

stats_short <- stats_100[stats_100$sd > 0.0, ]
stats_long <- stats_500[stats_500$sd > 0.0, ]

## Align the short and long summaries by animal pair: the sd > 0 sets can
## differ in size between simulation lengths, so join on the ID pair rather
## than by row position.
merged <- merge(stats_short, stats_long, by = c("id_1", "id_2"),
                suffixes = c("_short", "_long"))

comparison <- data.frame(
  id_1 = merged$id_1,
  id_2 = merged$id_2,
  meanKin_short = merged$mean_short,
  meanKin_long = merged$mean_long,
  meanKinDelta = abs(merged$mean_short - merged$mean_long),
  sdKin_short = merged$sd_short,
  sdKin_long = merged$sd_long,
  sdKinDelta = abs(merged$sd_short - merged$sd_long)
)

kable(comparison,
  longtable = TRUE,
  digits = c(0L, 0L, 4L, 4L, 4L, 4L, 4L, 4L),
  caption = stri_c(
    "Comparison of estimated kinships between simulations ",
    "of 100 (short) and 500 (long)"
  )
) |>
  kable_styling(
    latex_options = c("striped", "repeat_header"),
    repeat_header_method = "replace",
    repeat_header_text = "\\textit{(continued)}",
    font_size = 10L
  )

Comparison of estimated kinships between simulations of 100 (short) and 500 (long)

id_1	id_2	meanKin_short	meanKin_long	meanKinDelta	sdKin_short	sdKin_long	sdKinDelta
A	E	0.0825	0.0865	0.0040	0.1181	0.1190	0.0009
A	F	0.1663	0.1683	0.0020	0.0591	0.0595	0.0004
A	G	0.1663	0.1683	0.0020	0.0591	0.0595	0.0004
A	I	0.2988	0.2920	0.0068	0.0613	0.0591	0.0022
A	J	0.0975	0.0840	0.0135	0.1226	0.1182	0.0043
A	K	0.0700	0.0800	0.0100	0.1128	0.1167	0.0039
A	L	0.1600	0.1650	0.0050	0.0564	0.0584	0.0020
A	N	0.0800	0.0820	0.0020	0.1172	0.1175	0.0003
B	E	0.1325	0.1255	0.0070	0.1254	0.1251	0.0003
B	F	0.1912	0.1878	0.0035	0.0627	0.0626	0.0001
B	G	0.1912	0.1878	0.0035	0.0627	0.0626	0.0001
B	I	0.0625	0.0583	0.0042	0.0628	0.0624	0.0004
B	J	0.1250	0.1165	0.0085	0.1256	0.1248	0.0008
B	N	0.1100	0.1205	0.0105	0.1247	0.1250	0.0003
C	E	0.1075	0.1060	0.0015	0.0871	0.0846	0.0025
C	F	0.1788	0.1780	0.0008	0.0435	0.0423	0.0012
C	G	0.1788	0.1780	0.0008	0.0435	0.0423	0.0012
C	I	0.1806	0.1751	0.0055	0.0386	0.0407	0.0021
C	J	0.1113	0.1003	0.0110	0.0772	0.0814	0.0041
C	K	0.0350	0.0400	0.0050	0.0564	0.0584	0.0020
C	L	0.2675	0.2700	0.0025	0.0282	0.0292	0.0010
C	N	0.0950	0.1013	0.0063	0.0925	0.0876	0.0049
D	E	0.1075	0.1060	0.0015	0.0871	0.0846	0.0025
D	F	0.3038	0.3030	0.0008	0.0435	0.0423	0.0012
D	G	0.3038	0.3030	0.0008	0.0435	0.0423	0.0012
D	I	0.1806	0.1751	0.0055	0.0386	0.0407	0.0021
D	J	0.1113	0.1003	0.0110	0.0772	0.0814	0.0041
D	K	0.0350	0.0400	0.0050	0.0564	0.0584	0.0020
D	L	0.1425	0.1450	0.0025	0.0282	0.0292	0.0010
D	N	0.0950	0.1013	0.0063	0.0925	0.0876	0.0049
E	F	0.3038	0.3030	0.0008	0.0435	0.0423	0.0012
E	G	0.3038	0.3030	0.0008	0.0435	0.0423	0.0012
E	H	0.1075	0.1060	0.0015	0.0871	0.0846	0.0025
E	I	0.0762	0.0740	0.0023	0.0730	0.0751	0.0021
E	J	0.0700	0.0615	0.0085	0.0801	0.0761	0.0040
E	K	0.0388	0.0290	0.0097	0.0581	0.0528	0.0053
E	L	0.0731	0.0675	0.0056	0.0533	0.0530	0.0003
E	M	0.0412	0.0432	0.0020	0.0591	0.0595	0.0004
E	N	0.0625	0.0562	0.0062	0.0743	0.0716	0.0027
E	P	0.0206	0.0216	0.0010	0.0295	0.0298	0.0002
E	Q	0.0412	0.0432	0.0020	0.0591	0.0595	0.0004
F	F	0.5538	0.5530	0.0007	0.0435	0.0423	0.0012
F	G	0.3038	0.3030	0.0008	0.0435	0.0423	0.0012
F	H	0.1788	0.1780	0.0008	0.0435	0.0423	0.0012
F	I	0.1284	0.1246	0.0039	0.0426	0.0473	0.0048
F	J	0.0906	0.0809	0.0097	0.0637	0.0655	0.0018
F	K	0.0369	0.0345	0.0024	0.0436	0.0444	0.0008
F	L	0.1078	0.1062	0.0016	0.0321	0.0322	0.0001
F	M	0.0831	0.0841	0.0010	0.0295	0.0298	0.0002
F	N	0.0788	0.0788	0.0000	0.0720	0.0675	0.0045
F	P	0.0416	0.0421	0.0005	0.0148	0.0149	0.0001
F	Q	0.0831	0.0841	0.0010	0.0295	0.0298	0.0002
G	G	0.5538	0.5530	0.0007	0.0435	0.0423	0.0012
G	H	0.1788	0.1780	0.0008	0.0435	0.0423	0.0012
G	I	0.1284	0.1246	0.0039	0.0426	0.0473	0.0048
G	J	0.0906	0.0809	0.0097	0.0637	0.0655	0.0018
G	K	0.0369	0.0345	0.0024	0.0436	0.0444	0.0008
G	L	0.1078	0.1062	0.0016	0.0321	0.0322	0.0001
G	M	0.0831	0.0841	0.0010	0.0295	0.0298	0.0002
G	N	0.0788	0.0788	0.0000	0.0720	0.0675	0.0045
G	P	0.0416	0.0421	0.0005	0.0148	0.0149	0.0001
G	Q	0.0831	0.0841	0.0010	0.0295	0.0298	0.0002
H	I	0.1806	0.1751	0.0055	0.0386	0.0407	0.0021
H	J	0.1113	0.1003	0.0110	0.0772	0.0814	0.0041
H	K	0.0350	0.0400	0.0050	0.0564	0.0584	0.0020
H	L	0.1425	0.1450	0.0025	0.0282	0.0292	0.0010
H	N	0.0950	0.1013	0.0063	0.0925	0.0876	0.0049
I	I	0.5488	0.5420	0.0067	0.0613	0.0591	0.0022
I	J	0.2988	0.2920	0.0068	0.0613	0.0591	0.0022
I	K	0.0469	0.0524	0.0055	0.0649	0.0671	0.0022
I	L	0.1138	0.1138	0.0000	0.0360	0.0394	0.0034
I	M	0.1494	0.1460	0.0034	0.0306	0.0296	0.0011
I	N	0.0638	0.0670	0.0033	0.0794	0.0735	0.0060
I	P	0.0747	0.0730	0.0017	0.0153	0.0148	0.0005
I	Q	0.1494	0.1460	0.0034	0.0306	0.0296	0.0011
J	K	0.0238	0.0248	0.0010	0.0493	0.0499	0.0006
J	L	0.0675	0.0625	0.0050	0.0467	0.0499	0.0032
J	M	0.0488	0.0420	0.0067	0.0613	0.0591	0.0022
J	N	0.0475	0.0520	0.0045	0.0728	0.0728	0.0001
J	P	0.0244	0.0210	0.0034	0.0306	0.0296	0.0011
J	Q	0.0488	0.0420	0.0067	0.0613	0.0591	0.0022
K	L	0.2675	0.2700	0.0025	0.0282	0.0292	0.0010
K	M	0.0350	0.0400	0.0050	0.0564	0.0584	0.0020
K	N	0.0288	0.0275	0.0013	0.0529	0.0518	0.0010
K	P	0.0175	0.0200	0.0025	0.0282	0.0292	0.0010
K	Q	0.0350	0.0400	0.0050	0.0564	0.0584	0.0020
L	L	0.5175	0.5200	0.0025	0.0282	0.0292	0.0010
L	M	0.0800	0.0825	0.0025	0.0282	0.0292	0.0010
L	N	0.0619	0.0644	0.0025	0.0537	0.0540	0.0003
L	P	0.0400	0.0412	0.0013	0.0141	0.0146	0.0005
L	Q	0.0800	0.0825	0.0025	0.0282	0.0292	0.0010
M	N	0.0400	0.0410	0.0010	0.0586	0.0587	0.0001
N	P	0.0200	0.0205	0.0005	0.0293	0.0294	0.0001
N	Q	0.0400	0.0410	0.0010	0.0586	0.0587	0.0001

Other ways to run the simulation

cumulateSimKinships runs the same simulation but returns only the mean, standard deviation, minimum and maximum of each kinship coefficient across the simulations, not every simulated kinship matrix. It and createSimKinships both take a twinRelations table (see ?reportGV), so animals declared identical (MZ) twins have their kinship corrected to genetic identity in every simulated pedigree.