Contents

1 Introduction

This document is a tutorial for the Pedixplorer package, with examples of creating Pedigree objects and kinship matrices and other pedigree utilities.

The Pedixplorer package is an updated version of the Kinship2 package, featuring a change in maintainer and repository from CRAN to Bioconductor for continued development and support.

It contains the routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. It also includes tools for pedigree drawing and filtering which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome.

Supplementary vignettes are available to explain:

2 Installation

The Pedixplorer package is available on Bioconductor and can be installed with the following command:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("Pedixplorer")

The package can then be loaded with the following command:

library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(Pedixplorer)
## 
## Attaching package: 'Pedixplorer'
## The following object is masked from 'package:dplyr':
## 
##     id
## The following object is masked from 'package:ggplot2':
## 
##     rel

3 The Pedigree S4 object

The Pedigree object is a list of dataframes that describe the family structure. It contains the following components:

4 Basic Usage

4.1 Example Data

Two datasets are provided within the Pedixplorer package: + minnbreast: 17 families from a breast cancer study + sampleped: two sample pedigrees, with 41 and 14 subjects and the special relationship of these two pedigrees in relped.

This vignette uses the two pedigrees in sampleped. For more information on these datasets, see help(minnbreast) and help(sampleped).

4.2 Pedigree

First, we load sampleped and look at some of the values in the dataset, and create a Pedigree object using the Pedigree() function. This function automaticaly detect the necessary columns in the dataframe. If necessary you can modify the columns names with cols_ren. To create a Pedigree object, with multiple families, the dataframe just need a family column in the ped_df dataframe. When this is the case, the famid column will be pasted to the id of each individuals separated by an underscore to create a unique id for each individual in the Pedigree object.

data("sampleped")
print(sampleped[1:10, ])
##    famid  id dadid momid sex affection avail num fertility miscarriage evaluated consultand proband
## 1      1 101  <NA>  <NA>   1         0     0   2                               1         NA        
## 2      1 102  <NA>  <NA>   2         1     0   3                                         NA        
## 3      1 103   135   136   1         1     0   2                                         NA        
## 4      1 104  <NA>  <NA>   2         0     0   4                                         NA        
## 5      1 105  <NA>  <NA>   1        NA     0   6                                         NA        
## 6      1 106  <NA>  <NA>   2        NA     0   1                                         NA        
## 7      1 107  <NA>  <NA>   1         1     0  NA                                         NA        
## 8      1 108  <NA>  <NA>   2         0     0   0                                         NA        
## 9      1 109   101   102   2         0     1   3                                          1        
## 10     1 110   103   104   1         1     1   2                            true         NA        
##    carrier asymptomatic adopted dateofbirth dateofdeath deceased
## 1       NA           NA      NA  1968-01-22                   NA
## 2       NA           NA      NA  1975-06-27                   NA
## 3       NA           NA      NA  1975-08-14                   NA
## 4       NA           NA      NA                               NA
## 5       NA           NA      NA                               NA
## 6       NA           NA      NA                               NA
## 7       NA           NA      NA                               NA
## 8       NA           NA      NA                               NA
## 9       NA            1      NA                               NA
## 10      NA           NA      NA                               NA
pedi <- Pedigree(sampleped[c(3, 4, 10, 35, 36), ])
print(pedi)
## Pedigree object with: 
## Ped object with 5 individuals and 5 metadata columns:
##                    id       dadid       momid       famid       sex fertility miscarriage  deceased
## col_class <character> <character> <character> <character> <ordered>  <factor>    <factor> <logical>
## 1_103           1_103       1_135       1_136           1      male   fertile       FALSE      <NA>
## 1_104           1_104        <NA>        <NA>           1    female   fertile       FALSE      <NA>
## 1_110           1_110       1_103       1_104           1      male   fertile       FALSE      <NA>
## 1_135           1_135        <NA>        <NA>           1      male   fertile       FALSE      TRUE
## 1_136           1_136        <NA>        <NA>           1    female   fertile       FALSE      TRUE
##               avail evaluated consultand   proband  affected   carrier asymptomatic   adopted
## col_class <logical> <logical>  <logical> <logical> <logical> <logical>    <logical> <logical>
## 1_103         FALSE     FALSE      FALSE     FALSE      TRUE      <NA>         <NA>     FALSE
## 1_104         FALSE     FALSE      FALSE     FALSE     FALSE      <NA>         <NA>     FALSE
## 1_110          TRUE      TRUE      FALSE     FALSE      TRUE      <NA>         <NA>     FALSE
## 1_135         FALSE     FALSE      FALSE     FALSE      <NA>      <NA>         <NA>     FALSE
## 1_136         FALSE     FALSE      FALSE     FALSE      <NA>      <NA>         <NA>     FALSE
##           dateofbirth dateofdeath    useful       kin     isinf num_child_tot num_child_dir
## col_class <character> <character> <logical> <numeric> <logical>     <numeric>     <numeric>
## 1_103      1975-08-14        <NA>      <NA>      <NA>      <NA>             1             1
## 1_104            <NA>        <NA>      <NA>      <NA>      <NA>             1             1
## 1_110            <NA>        <NA>      <NA>      <NA>      <NA>             0             0
## 1_135      1925-05-04  2021-01-22      <NA>      <NA>      <NA>             1             1
## 1_136      1932-07-19  2024-02-17      <NA>      <NA>      <NA>             1             1
##           num_child_ind |   affection         num       error affection_mods  avail_mods
## col_class     <numeric>   <character> <character> <character>    <character> <character>
## 1_103                 0             1           2        <NA>              1           0
## 1_104                 0             0           4        <NA>              0           0
## 1_110                 0             1           2        <NA>              1           1
## 1_135                 0          <NA>           5        <NA>             NA           0
## 1_136                 0          <NA>           6        <NA>             NA           0
## Rel object with 0 relationshipswith 0 MZ twin, 0 DZ twin, 0 UZ twin, 0 Spouse:
##          id1         id2                     code       famid
##  <character> <character> <c("ordered", "factor")> <character>

For more information on the Pedigree() function, see help(Pedigree).

The Pedigree object can be subset to individual pedigrees by their family id. The Pedigree object has a print, summary and plot method, which we show below. The print method prints the Ped and Rel object of the pedigree. The summary method prints a short summary of the pedigree. Finally the plot method displays the pedigree.

pedi <- Pedigree(sampleped)
print(famid(ped(pedi)))
##  [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [25] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "2" "2" "2" "2" "2" "2" "2"
## [49] "2" "2" "2" "2" "2" "2" "2"
ped1 <- pedi[famid(ped(pedi)) == "1"]
summary(ped1)
## Pedigree object with 
## [1] "Ped object with 41 individuals and 5 metadata columns"
## [1] "Rel object with 0 relationshipswith 0 MZ twin, 0 DZ twin, 0 UZ twin, 0 Spouse"
plot(ped1, cex = 0.7)

Pedigree of family 1

You can add a title and a legend to the plot with the following command:

plot(
    ped1, title = "Pedigree 1",
    legend = TRUE, leg_loc = c(0.45, 0.9, 0.8, 1),
    cex = 0.7, leg_symbolsize = 0.04
)

Pedigree of family 1 with legend

4.3 Pedigree Shiny application

A shiny application is available to create, interact and plot pedigrees. To launch the application, use the following command:

ped_shiny()

The use is simple:

  • You first need to import a dataset and select the columns to use.
  • You can then select the affection informations and the colors associated to them.
  • If different families are present in the dataset, you can select which one to plot.
  • Before the plot is displayed, you can filter the pedigree by selecting the informatives subjects to keep and their relatives. If the pedigree is then splited in multiple families, you can select which to plot.
  • Finally the plot is displayed and you can make it interactive and download the resulting image.

5 Fixing Pedigree Issues

To “break” the pedigree, we can manipulate the sex value to not match the parent value (in this example, we change 203 from a male to a female, even though 203 is a father). To do this, we first subset datped2, locate the id column, and match it to a specific id (in this case, 203). Within id 203, then locate in the sex column. Assign this subset to the incorrect value of 2 (female) to change the original/correct value of 1 (male).

To further break the pedigree, we can delete subjects who seem irrelevant to the pedigree (in this example, we delete 209 because he is a married-in father). To do this, we subset datped2 and use the which() function to locate and delete the specified subject (in this case, 209). Reassign this code to datped22 to drop the specified subject entirely.

datped2 <- sampleped[sampleped$famid == 2, ]
datped2[datped2$id %in% 203, "sex"] <- 2
datped2 <- datped2[-which(datped2$id %in% 209), ]

An error occurs when the Pedigree() function notices that id 203 is not coded to be male (1) but is a father. To correct this, we simply employ the fix_parents() function to adjust the sex value to match either momid or dadid. fix_parents() will also add back in any deleted subjects, further fixing the Pedigree.

tryout <- try({
    ped2 <- Pedigree(datped2)
})
## Error in validObject(.Object) : 
##   invalid class "Ped" object: dadid values '2_209' should be in '2_201', '2_202', '2_203', '2_204', '2_205'...
fixped2 <- with(datped2, fix_parents(id, dadid, momid, sex))
fixped2
##     id momid dadid    sex famid
## 1  201  <NA>  <NA>   male     1
## 2  202  <NA>  <NA> female     1
## 3  203  <NA>  <NA>   male     1
## 4  204   202   201 female     1
## 5  205   202   201   male     1
## 6  206   202   201 female     1
## 7  207   202   201 female     1
## 8  208   202   201 female     1
## 9  210   204   203   male     1
## 10 211   204   203   male     1
## 11 212   208   209 female     1
## 12 213   208   209   male     1
## 13 214   208   209   male     1
## 14 209  <NA>  <NA>   male     1
ped2 <- Pedigree(fixped2)
plot(ped2)

Pedigree of family 2

If the fix is straightforward (changing one sex value based on either being a mother or father), fix_parents() will resolve the issue. If the issue is more complicated, say if 203 is coded to be both a father and a mother, fix_parents() will not know which one is correct and therefore the issue will not be resolved.

6 Kinship

A common use for pedigrees is to make a matrix of kinship coefficients that can be used in mixed effect models. A kinship coefficient is the probability that a randomly selected allele from two people at a given locus will be identical by descent (IBD), assuming all founder alleles are independent. For example, we each have two alleles per autosomal marker, so sampling two alleles with replacement from our own DNA has only \(p=0.50\) probability of getting the same allele twice.

6.1 Kinship for Pedigree object

We use kinship() to calculate the kinship matrix for ped2. The result is a special symmetrix matrix class from the Matrix R package, which is stored efficiently to avoid repeating elements.

kin2 <- kinship(ped2)
kin2[1:9, 1:9]
## 9 x 9 sparse Matrix of class "dsCMatrix"
##       1_201 1_202 1_203 1_204 1_205 1_206 1_207 1_208 1_209
## 1_201  0.50  .      .    0.25  0.25  0.25  0.25  0.25   .  
## 1_202  .     0.50   .    0.25  0.25  0.25  0.25  0.25   .  
## 1_203  .     .      0.5  .     .     .     .     .      .  
## 1_204  0.25  0.25   .    0.50  0.25  0.25  0.25  0.25   .  
## 1_205  0.25  0.25   .    0.25  0.50  0.25  0.25  0.25   .  
## 1_206  0.25  0.25   .    0.25  0.25  0.50  0.25  0.25   .  
## 1_207  0.25  0.25   .    0.25  0.25  0.25  0.50  0.25   .  
## 1_208  0.25  0.25   .    0.25  0.25  0.25  0.25  0.50   .  
## 1_209  .     .      .    .     .     .     .     .      0.5

For family 2, see that the row and column names match the id in the figure below, and see that each kinship coefficient with themselves is 0.50, siblings are 0.25 (e.g. 204-205), and pedigree marry-ins only share alleles IBD with their children with coefficient 0.25 (e.g. 203-210). The plot can be used to verify other kinship coefficients.

6.2 Kinship for Pedigree with multiple families

The kinship() function also works on a Pedigree object with multiple families. We show how to create the kinship matrix, then show a snapshot of them for the two families, where the row and columns names are the ids of the subject.

pedi <- Pedigree(sampleped)
adopted(ped(pedi)) <- FALSE # Remove adoption status
kin_all <- kinship(pedi)
kin_all[1:9, 1:9]
## 9 x 9 sparse Matrix of class "dsCMatrix"
##       1_101 1_102 1_103 1_104 1_105 1_106 1_107 1_108 1_109
## 1_101  0.50  .      .     .     .     .     .     .    0.25
## 1_102  .     0.50   .     .     .     .     .     .    0.25
## 1_103  .     .      0.5   .     .     .     .     .    .   
## 1_104  .     .      .     0.5   .     .     .     .    .   
## 1_105  .     .      .     .     0.5   .     .     .    .   
## 1_106  .     .      .     .     .     0.5   .     .    .   
## 1_107  .     .      .     .     .     .     0.5   .    .   
## 1_108  .     .      .     .     .     .     .     0.5  .   
## 1_109  0.25  0.25   .     .     .     .     .     .    0.50
kin_all[40:43, 40:43]
## 4 x 4 sparse Matrix of class "dsCMatrix"
##       1_140 1_141 2_201 2_202
## 1_140  0.50  0.25   .     .  
## 1_141  0.25  0.50   .     .  
## 2_201  .     .      0.5   .  
## 2_202  .     .      .     0.5
kin_all[42:46, 42:46]
## 5 x 5 sparse Matrix of class "dsCMatrix"
##       2_201 2_202 2_203 2_204 2_205
## 2_201  0.50  .      .    0.25  0.25
## 2_202  .     0.50   .    0.25  0.25
## 2_203  .     .      0.5  .     .   
## 2_204  0.25  0.25   .    0.50  0.25
## 2_205  0.25  0.25   .    0.25  0.50

6.3 Kinship for twins in Pedigree with multiple families

Specifying twin relationships in a Pedigree with multiple families object is complicated by the fact that the user must specify the family id to which the id1 and id2 belong. We show below the relation matrix requires the family id to be in the last column, with the column names as done below, to make the plotting and kinship matrices to show up with the monozygotic twins correctly. We show how to specify monozygosity for subjects 206 and 207 in ped2, and subjects 125 and 126 in ped1. We check it by looking at the kinship matrix for these pairs, which are correctly at 0.5.

data("relped")
relped
##   famid id1 id2 code
## 1     1 140 141    1
## 2     1 139 140    2
## 3     1 121 123    2
## 4     1 129 126    4
## 5     1 130 133    3
## 6     2 210 211    1
## 7     2 208 204    2
## 8     2 212 213    3
pedi <- Pedigree(sampleped, relped)
adopted(ped(pedi)) <- FALSE # Remove adoption status
kin_all <- kinship(pedi)
kin_all[24:27, 24:27]
## 4 x 4 sparse Matrix of class "dsCMatrix"
##        1_124  1_125  1_126  1_127
## 1_124 0.5000 0.0625 0.0625 0.0625
## 1_125 0.0625 0.5000 0.2500 0.1250
## 1_126 0.0625 0.2500 0.5000 0.1250
## 1_127 0.0625 0.1250 0.1250 0.5000
kin_all[46:50, 46:50]
## 5 x 5 sparse Matrix of class "dsCMatrix"
##       2_205 2_206 2_207 2_208 2_209
## 2_205  0.50  0.25  0.25  0.25   .  
## 2_206  0.25  0.50  0.25  0.25   .  
## 2_207  0.25  0.25  0.50  0.25   .  
## 2_208  0.25  0.25  0.25  0.50   .  
## 2_209  .     .     .     .      0.5

Note that subject 113 is not in ped1 because they are a marry-in without children in the Pedigree. Subject 113 is in their own Pedigree of size 1 in the kin_all matrix at index 41. We later show how to handle such marry-ins for plotting.

7 Optional Pedigree Informations

We use ped2 from sampleped to sequentially add optional information to the Pedigree object.

7.1 Deceased

The example below shows how to specify a deceased indicator. The sampleped data does not include such an
indicator, so we create one to indicate that the first generation of ped2, subjects 1 and 2, are deceased. The deceased indicator is used to cross out the individuals in the Pedigree plot.

df2 <- sampleped[sampleped$famid == 2, ]
names(df2)
##  [1] "famid"        "id"           "dadid"        "momid"        "sex"          "affection"   
##  [7] "avail"        "num"          "fertility"    "miscarriage"  "evaluated"    "consultand"  
## [13] "proband"      "carrier"      "asymptomatic" "adopted"      "dateofbirth"  "dateofdeath" 
## [19] "deceased"
df2$deceased <- c(1, 1, rep(0, 12))
ped2 <- Pedigree(df2)
summary(deceased(ped(ped2)))
##    Mode   FALSE    TRUE 
## logical      12       2
plot(ped2)

Pedigree of family 2 with different vital status

7.2 Labels

Here we show how to use the label argument in the plot method to add additional information under each subject. In the example below, we add names to the existing plot by adding a new column to the elementMetadata of the Ped object of the Pedigree.

As space permits, more lines and characters per line can be made using the a {/em } character to indicate a new line.

mcols(ped2)$Names <- c(
    "John\nDalton", "Linda", "Jack", "Rachel", "Joe", "Deb",
    "Lucy", "Ken", "Barb", "Mike", "Matt",
    "Mindy", "Mark", "Marie\nCurie"
)
plot(ped2, label = "Names", cex = 0.7)

Pedigree of family 2 with names label

7.3 Affected Indicators

We show how to specify affected status with a single indicator and multiple indicators. First, we use the affected indicator from sampleped, which contains 0/1 indicators and NA as missing, and let it it indicate blue eyes. Next, we create a vector as an indicator for baldness. And add it as a second filling scale for the plot with generate_colors(add_to_scale = TRUE). The plot shapes for each subject is therefore divided into two equal parts and shaded differently to indicate the two affected indicators.

mcols(ped2)$bald <- as.factor(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1))
ped2 <- generate_colors(ped2, col_aff = "bald", add_to_scale = TRUE)
# Increase down margin for the legend
op <- par(mai = c(1.5, 0.2, 0.2, 0.2))
plot(
    ped2, legend = TRUE,
    leg_loc = c(0.5, 6, 3.5, 4)
)

Family 2 pedigree with two affections

# Reset graphical parameter
par(op)

7.4 Special Relationships

Special pedigree relationships can be specified in a matrix as the rel_df argument in the Pedigree() constructor. There are 4 relationships that can be specified by numeric codes:

  • 1 = Monozygotic twins
  • 2 = Dizygotic twins
  • 3 = Twins of unknown zygosity
  • 4 = Spouse

The spouse relationship can indicate a marry-in when a couple does not have children together.

7.4.1 Twins

Below, we use the relationship dataset. We can specify in the code column if the individuals are monozygotic 1, dizygotic 2 or of unknown-zygosity 3 twins. The twin relationships are both represented with diverging lines from a single point. The monozygotic twins have an additional line connecting the diverging lines, while twins of unknown zygosity have a question mark.

## create twin relationships
data("relped")
rel(ped2) <- Rel(relped[relped$famid == 2, ])
plot(ped2)

Family 2 pedigree with special relationships

7.4.2 Inbreeding

Another special relationship is inbreeding. Inbreeding of founders implies the founders’ parents are related (the maternal and paternal genes descended from a single ancestral gene). One thing we can do is add more people to the pedigree to show this inbreeding.

To show that a pair of founders (subjects 201 and 202) are inbred, we must show that their parents are siblings. To do this, we create subjects 197 and 198 to be the parents of 201 and also create subjects 199 and 200 to be the parents of 202. To make subjects 198 and 199 siblings, we give them the same parents, creating subjects 195 and 196. This results in subjects 201 and 202 being first cousins, and therefore inbred.

indid <- 195:202
dadid <- c(NA, NA, NA, 196, 196, NA, 197, 199)
momid <- c(NA, NA, NA, 195, 195, NA, 198, 200)
sex <- c(2, 1, 1, 2, 1, 2, 1, 2)
ped3 <- data.frame(
    id = indid, dadid = dadid,
    momid = momid, sex = sex
)

ped4df <- rbind.data.frame(df2[-c(1, 2), 2:5], ped3)
ped4 <- Pedigree(ped4df)
plot(ped4)

Pedigree with inbreeding

7.4.3 Marry-ins

Spouse with no child can also be specified with the rel_df argument by setting the code value to spouse or 4. If we use the ped2 from earlier and add a new spouse relationship between the individuals 212 and 211 we get the following plot.

## create twin relationships
rel_df2 <- data.frame(
    id1 = "211",
    id2 = "212",
    code = 4,
    famid = "2"
)
new_rel <- c(rel(ped2), with(rel_df2, Rel(id1, id2, code, famid)))
rel(ped2) <- upd_famid(new_rel)
plot(ped2)

Pedigree with spouse with no children

8 Pedigree Plot Details

The plot method attempts to adhere to many standards in pedigree plotting, as presented by Bennet et al. 2008.

To show some other tricks with pedigree plotting, we use ped1 from sampleped, which has 41 subjects in 4 generations, including a generation with double first cousins. After the first marriage of 114, they remarried subject 113 without children between them. If we do not specify the marriage with the rel_df argument, the plot method excludes subject 113 from the plot. The basic plot of ped1 is shown in the figure below.

df1 <- sampleped[sampleped$famid == 1, ]
relate1 <- data.frame(
    id1 = 113,
    id2 = 114,
    code = 4,
    famid = 1
)
ped1 <- Pedigree(df1, relate1)
plot(ped1, cex = 0.7)