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Introduction

Unlike Tolstoy, where only happy families are alike, all pedigrees are alike – or at least, all simulated pedigrees are alike. The simulatePedigree function generates a pedigree with a user-specified number of generations and individuals per generation. This function provides users the opportunity to test family models in pedigrees with a customized pedigree length and width.

These pedigrees can be simulated as a function of several parameters, including the number of children per mate, generations, sex ratio of newborns, and mating rate. Given that large family pedigrees are difficult to collect or access, simulated pedigrees serve as an efficient tool for researchers. These simulated pedigrees are useful for building family-based statistical models, and evaluating their statistical properties, such as power, bias, and computational efficiency.

To illustrate this functionality, let us generate a pedigree. This pedigree has a total of four generations (Ngen), in which each person who “mates”, grows a family with four offspring (kpc). In our scenario, the number of male and female newborns is equal, but can be adjusted via (sexR). In this illustration 70% of individuals will mate and bear offspring (marR). Such a pedigree structure can be simulated by running the following code:

## Loading Required Libraries
library(BGmisc)

set.seed(5)
df_ped <- simulatePedigree(
  kpc = 4,
  Ngen = 4,
  sexR = .5,
  marR = .7
)
summary(df_ped)
#>      fam                  ID              gen            dadID       
#>  Length:57          Min.   : 10011   Min.   :1.000   Min.   : 10012  
#>  Class :character   1st Qu.: 10036   1st Qu.:3.000   1st Qu.: 10024  
#>  Mode  :character   Median :100312   Median :3.000   Median : 10037  
#>                     Mean   : 59171   Mean   :3.298   Mean   : 42859  
#>                     3rd Qu.:100416   3rd Qu.:4.000   3rd Qu.:100311  
#>                     Max.   :100432   Max.   :4.000   Max.   :100320  
#>                                                      NA's   :13      
#>      momID             spt             sex           
#>  Min.   : 10011   Min.   : 10011   Length:57         
#>  1st Qu.: 10022   1st Qu.: 10025   Class :character  
#>  Median : 10036   Median : 10036   Mode  :character  
#>  Mean   : 42859   Mean   : 40124                     
#>  3rd Qu.:100316   3rd Qu.:100311                     
#>  Max.   :100318   Max.   :100320                     
#>  NA's   :13       NA's   :33

The simulation output is a data.frame with 57 rows and 7 columns. Each row corresponds to a simulated individual.

df_ped[21, ]
#>      fam     ID gen dadID momID    spt sex
#> 21 fam 1 100312   3 10024 10022 100317   M

The columns represents the individual’s family ID, the individual’s personal ID, the generation the individual is in, the IDs of their father and mother, the ID of their spouse, and the biological sex of the individual, respectively.

Summarizing Pedigrees

summarizeFamilies(df_ped, famID = "fam")$family_summary
#>       fam count gen_mean gen_median gen_min gen_max    gen_sd spt_mean
#>    <char> <int>    <num>      <num>   <num>   <num>     <num>    <num>
#> 1:  fam 1    57 3.298246          3       1       4 0.8229935  40123.5
#>    spt_median spt_min spt_max   spt_sd
#>         <num>   <num>   <num>    <num>
#> 1:    10035.5   10011  100320 43476.96

Plotting Pedigree

Pedigrees are visual diagrams that represent family relationships across generations. They are commonly used in genetics to trace the inheritance of specific traits or conditions. This vignette will guide you through visualizing simulated pedigrees using the plotPedigree function. This function is a wrapper function for Kinship2’s base R plotting.

Single Pedigree Visualization

To visualize a single simulated pedigree, use the plotPedigree() function.

# Plot the simulated pedigree
plotPedigree(df_ped)

#> Did not plot the following people: 10032
#> $plist
#> $plist$n
#> [1]  2  7 19 28
#> 
#> $plist$nid
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    2    1    0    0    0    0    0    0    0     0     0     0     0     0
#> [2,]    6    4    5    3    9    7    8    0    0     0     0     0     0     0
#> [3,]   18   17   19   22   21   26   23   10   12    13    14    16    15    24
#> [4,]   38   39   40   42   41   43   45   48   47    50    52    53    30    31
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [2,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [3,]    20    25    28    29    27     0     0     0     0     0     0     0
#> [4,]    32    33    34    35    36    37    44    46    49    51    54    55
#>      [,27] [,28]
#> [1,]     0     0
#> [2,]     0     0
#> [3,]     0     0
#> [4,]    56    57
#> 
#> $plist$pos
#>               [,1]      [,2]      [,3]      [,4]      [,5]      [,6]     [,7]
#> [1,]  1.550317e+01 16.503171  0.000000  0.000000  0.000000  0.000000  0.00000
#> [2,]  8.255043e+00  9.255043 14.147242 15.147242 18.805200 19.805200 20.80520
#> [3,]  2.351008e+00  3.351008  5.751008  6.751008  8.585014  9.585014 10.58501
#> [4,] -1.257081e-13  1.000000  2.000000  3.000000  4.000000  5.000000  6.00000
#>          [,8]     [,9]    [,10]    [,11]    [,12]    [,13]    [,14]    [,15]
#> [1,]  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000
#> [2,]  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000
#> [3,] 12.13453 13.13453 14.13453 15.13453 16.32945 17.32945 18.98794 19.98794
#> [4,]  7.00000  8.00000  9.00000 10.00000 11.00000 12.00000 13.00000 14.00000
#>         [,16]    [,17]    [,18]    [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,]  0.00000  0.00000  0.00000  0.00000     0     0     0     0     0     0
#> [2,]  0.00000  0.00000  0.00000  0.00000     0     0     0     0     0     0
#> [3,] 20.98794 21.98794 23.86104 24.86104     0     0     0     0     0     0
#> [4,] 15.00000 16.00000 17.00000 18.00000    19    20    21    22    23    24
#>      [,26] [,27] [,28]
#> [1,]     0     0     0
#> [2,]     0     0     0
#> [3,]     0     0     0
#> [4,]    25    26    27
#> 
#> $plist$fam
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0
#> [2,]    0    1    1    0    0    1    1    0    0     0     0     0     0     0
#> [3,]    0    1    1    0    1    0    1    3    3     3     0     0     3     5
#> [4,]    1    1    1    1    3    3    3    3    5     5     5     5    10    10
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [2,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [3,]     0     5     5     5     0     0     0     0     0     0     0     0
#> [4,]    10    10    12    12    12    12    15    15    15    15    18    18
#>      [,27] [,28]
#> [1,]     0     0
#> [2,]     0     0
#> [3,]     0     0
#> [4,]    18    18
#> 
#> $plist$spouse
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    1    0    0    0    0    0    0    0    0     0     0     0     0     0
#> [2,]    1    0    1    0    1    0    0    0    0     0     0     0     0     0
#> [3,]    1    0    1    0    1    0    0    0    0     1     0     1     0     0
#> [4,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [2,]     0     0     0     0     0     0     0     0     0     0     0     0
#> [3,]     1     0     0     1     0     0     0     0     0     0     0     0
#> [4,]     0     0     0     0     0     0     0     0     0     0     0     0
#>      [,27] [,28]
#> [1,]     0     0
#> [2,]     0     0
#> [3,]     0     0
#> [4,]     0     0
#> 
#> 
#> $x
#>  [1]  1.650317e+01  1.550317e+01  1.514724e+01  9.255043e+00  1.414724e+01
#>  [6]  8.255043e+00  1.980520e+01  2.080520e+01  1.880520e+01  1.213453e+01
#> [11]            NA  1.313453e+01  1.413453e+01  1.513453e+01  1.732945e+01
#> [16]  1.632945e+01  3.351008e+00  2.351008e+00  5.751008e+00  1.998794e+01
#> [21]  8.585014e+00  6.751008e+00  1.058501e+01  1.898794e+01  2.098794e+01
#> [26]  9.585014e+00  2.486104e+01  2.198794e+01  2.386104e+01  1.200000e+01
#> [31]  1.300000e+01  1.400000e+01  1.500000e+01  1.600000e+01  1.700000e+01
#> [36]  1.800000e+01  1.900000e+01 -1.257081e-13  1.000000e+00  2.000000e+00
#> [41]  4.000000e+00  3.000000e+00  5.000000e+00  2.000000e+01  6.000000e+00
#> [46]  2.100000e+01  8.000000e+00  7.000000e+00  2.200000e+01  9.000000e+00
#> [51]  2.300000e+01  1.000000e+01  1.100000e+01  2.400000e+01  2.500000e+01
#> [56]  2.600000e+01  2.700000e+01
#> 
#> $y
#>  [1]  1  1  2  2  2  2  2  2  2  3 NA  3  3  3  3  3  3  3  3  3  3  3  3  3  3
#> [26]  3  3  3  3  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
#> [51]  4  4  4  4  4  4  4
#> 
#> $boxw
#> [1] 0.5158615
#> 
#> $boxh
#> [1] 0.08681352
#> 
#> $call
#> kinship2::plot.pedigree(x = p3, cex = cex, col = col, symbolsize = symbolsize, 
#>     branch = branch, packed = packed, align = align, width = width, 
#>     density = density, angle = angle, keep.par = keep.par, pconnect = pconnect, 
#>     mar = mar)

In the resulting plot, biological males are represented by squares, while biological females are represented by circles, following the standard pedigree conventions.

Visualizing Multiple Pedigrees Side-by-Side

If you wish to compare different pedigrees side by side, you can plot them together. For instance, let’s visualize pedigrees for families spanning three and four generations, respectively.

set.seed(8)
# Simulate a family with 3 generations
df_ped_3 <- simulatePedigree(Ngen = 3)

# Simulate a family with 4 generations
df_ped_4 <- simulatePedigree(Ngen = 4)

# Set up plotting parameters for side-by-side display
par(mfrow = c(1, 2))

# Plot the 3-generation pedigree
plotPedigree(df_ped_3, width = 3)
#> $plist
#> $plist$n
#> [1] 2 5 6
#> 
#> $plist$nid
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    2    1    0    0    0    0
#> [2,]    3    5    4    6    7    0
#> [3,]    8   10   11    9   12   13
#> 
#> $plist$pos
#>              [,1]     [,2] [,3] [,4] [,5] [,6]
#> [1,] 1.166667e+00 2.166667    0    0    0    0
#> [2,] 2.047042e-09 1.000000    2    3    4    0
#> [3,] 0.000000e+00 1.000000    2    3    4    5
#> 
#> $plist$fam
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    0    0    0    0    0    0
#> [2,]    1    1    0    0    1    0
#> [3,]    2    2    2    4    4    4
#> 
#> $plist$spouse
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    1    0    0    0    0    0
#> [2,]    0    1    0    1    0    0
#> [3,]    0    0    0    0    0    0
#> 
#> 
#> $x
#>  [1] 2.166667e+00 1.166667e+00 2.047042e-09 2.000000e+00 1.000000e+00
#>  [6] 3.000000e+00 4.000000e+00 0.000000e+00 3.000000e+00 1.000000e+00
#> [11] 2.000000e+00 4.000000e+00 5.000000e+00
#> 
#> $y
#>  [1] 1 1 2 2 2 2 2 3 3 3 3 3 3
#> 
#> $boxw
#> [1] 0.2060484
#> 
#> $boxh
#> [1] 0.05787568
#> 
#> $call
#> kinship2::plot.pedigree(x = p3, cex = cex, col = col, symbolsize = symbolsize, 
#>     branch = branch, packed = packed, align = align, width = width, 
#>     density = density, angle = angle, keep.par = keep.par, pconnect = pconnect, 
#>     mar = mar)

# Plot the 4-generation pedigree
plotPedigree(df_ped_4, width = 1)

#> $plist
#> $plist$n
#> [1]  2  5 10 12
#> 
#> $plist$nid
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,]    2    1    0    0    0    0    0    0    0     0     0     0
#> [2,]    3    5    4    6    7    0    0    0    0     0     0     0
#> [3,]    8    9   11   15   14   13   10   12   17    16     0     0
#> [4,]   18   21   23   22   25   26   19   20   24    27    28    29
#> 
#> $plist$pos
#>              [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]
#> [1,] 6.399999e+00 7.399999 0.000000 0.000000 0.000000 0.000000 0.000000
#> [2,] 3.299999e+00 4.299999 6.699999 7.699999 8.699999 0.000000 0.000000
#> [3,] 9.333331e-01 1.933333 2.933333 3.933333 4.933333 6.066666 7.066666
#> [4,] 1.854016e-14 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000
#>          [,8]     [,9]    [,10] [,11] [,12]
#> [1,] 0.000000 0.000000  0.00000     0     0
#> [2,] 0.000000 0.000000  0.00000     0     0
#> [3,] 8.066666 9.066666 10.06667     0     0
#> [4,] 7.000000 8.000000  9.00000    10    11
#> 
#> $plist$fam
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,]    0    0    0    0    0    0    0    0    0     0     0     0
#> [2,]    0    1    0    1    1    0    0    0    0     0     0     0
#> [3,]    0    1    1    1    0    0    3    3    3     0     0     0
#> [4,]    1    1    1    4    4    4    6    6    6     9     9     9
#> 
#> $plist$spouse
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,]    1    0    0    0    0    0    0    0    0     0     0     0
#> [2,]    1    0    1    0    0    0    0    0    0     0     0     0
#> [3,]    1    0    0    1    0    1    0    0    1     0     0     0
#> [4,]    0    0    0    0    0    0    0    0    0     0     0     0
#> 
#> 
#> $x
#>  [1] 7.399999e+00 6.399999e+00 3.299999e+00 6.699999e+00 4.299999e+00
#>  [6] 7.699999e+00 8.699999e+00 9.333331e-01 1.933333e+00 7.066666e+00
#> [11] 2.933333e+00 8.066666e+00 6.066666e+00 4.933333e+00 3.933333e+00
#> [16] 1.006667e+01 9.066666e+00 1.854016e-14 6.000000e+00 7.000000e+00
#> [21] 1.000000e+00 3.000000e+00 2.000000e+00 8.000000e+00 4.000000e+00
#> [26] 5.000000e+00 9.000000e+00 1.000000e+01 1.100000e+01
#> 
#> $y
#>  [1] 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
#> 
#> $boxw
#> [1] 0.4533065
#> 
#> $boxh
#> [1] 0.08681352
#> 
#> $call
#> kinship2::plot.pedigree(x = p3, cex = cex, col = col, symbolsize = symbolsize, 
#>     branch = branch, packed = packed, align = align, width = width, 
#>     density = density, angle = angle, keep.par = keep.par, pconnect = pconnect, 
#>     mar = mar)

By examining the side-by-side plots, you can contrast and analyze the structures of different families, tracing the inheritance of specific traits or conditions if needed.