Introduction to the pedtools package

Magnus Dehli Vigeland


The purpose of this vignette is to show how to work with pedigrees and marker data in pedtools.

To get started, install the current CRAN version of the package:


Alternatively, you may want the latest development version from GitHub:


Now you should be able to load pedtools.


1 Pedigrees

In pedtools and all other pedsuite packages, pedigrees are stored as ped objects. We start by explaining briefly what these objects look like, and their basic constructor. If you are reading this vignette simply to learn how to create a particular pedigree, you may want to skip ahead to section 1.3 where we describe practical shortcuts to common pedigree structures.

1.1 The ped class

The constructor function
The most direct way to create a pedigree in pedtools is with the ped() constructor. This takes as input 4 vectors of equal length:

In other words, the j’th pedigree member has label id[j], father fid[j], mother mid[j], and gender given by sex[j].

For example, the following creates a family trio, i.e. father, mother and child:

ped(id = 1:3, fid = c(0,0,1), mid = c(0,0,2), sex = c(1,2,2))
#>  id fid mid sex
#>   1   *   *   1
#>   2   *   *   2
#>   3   1   2   2

In this example the child (id=3) is female, since the associated entry in sex is 2. Note that missing parents are printed as *. Individuals without parents are called founders of the pedigree, while the nonfounders have both parents specified. It is not allowed to have exactly one parent.

Instead of numerical labels as above, we could have used character strings. Let us create the trio again, with more informative labels, and store it in a variable named trio.

trio = ped(id = c("fa", "mo", "girl"), fid = c("","","fa"), mid = c("","","mo"), sex = c(1,2,2))
#>    id fid mid sex
#>    fa   *   *   1
#>    mo   *   *   2
#>  girl  fa  mo   2

The special strings "0", "" and NA are all interpreted as a missing parent.

How pedigrees are stored internally
From the way it is printed, the object trio appears to be a data frame, but this is not exactly true. Rather it is an object of class ped, which is basically a list. We can see the actual content of trio by unclassing it:

#> $ID
#> [1] "fa"   "mo"   "girl"
#> $FIDX
#> [1] 0 0 1
#> $MIDX
#> [1] 0 0 2
#> $SEX
#> [1] 1 2 2
#> [1] ""
#> [1] FALSE

In most cases it is not recommended for regular users to interact directly with the internal slots of a ped, since this can have unfortunate consequences unless you know exactly what you are doing. Instead, one should use accessor functions like labels(), getMarkers() and founderInbreeding(). The most important accessors are described within this vignette, while others are documented in the help page ?ped_utils.

1.2 Basic pedigree plots

To plot a pedigree, simply use plot().


Under the hood, pedtools::plot() imports the excellent alignment algorithm from the kinship2 package. The plotting itself is done within pedtools, which has some advantages over kinship2, including the ability to plot singletons. A wealth of plot options are available. An overview can be found in the documentation ?plot.ped, but a couple of quick examples should get you started. Here is a demonstration of symbol colours and styles:

plot(trio, hatched = "fa", hatchDensity = 15, 
     fill = c(mo = "pink", girl = "lightblue"), 
     col = c("green", "red", "blue"), 
     lwd = 3:1, lty = 1:3)

And here is an example of how to put text annotation around the symbols:

plot(trio, margin = c(1,3,1,1), 
     textAnnot = list(
       topright = list(1:3, cex = 0.8, col = 2, font = 2, offset = 0.1),
       left     = list(c(girl = "comment"), cex = 1.5, col = 4, offset = 1, srt = 20),
       inside   = c("?", "?", "!")))

See Section 2.2 for how to add marker genotypes to pedigree plots.

1.3 Built-in pedigree structures

Rather than using the ped() function directly, it is usually quicker and safer to build pedigrees step by step, applying the arsenal of utility functions offered by pedtools. A typical workflow is as follows:

  1. Choose one of the basic pedigree structures as starting point.
  2. Add/remove individuals as needed.
  3. Modify attributes like genders and labels.

You will find several examples below, but first let us list the available tools for each of the 3 steps.

Basic pedigrees
The following pedigree structures serve as starting points for pedigree constructions. For parameters and details, see ?ped_basic.

There are also more specialized structures, including double cousins, selfing pedigrees, and consecutive matings between full siblings. Look them up in ?ped_complex if you are interested.

Add/remove/extract individuals
The functions below are used to modify an existing ped object by adding/removing individuals, or extracting a sub-pedigree. For details, see ?ped_modify.

Edit labels and attributes
The following functions modify various attributes of a ped object. See ?ped_modify for parameters and details.

1.4 Examples of pedigree construction

Example 1: Trio

As our first example we will recreate the trio pedigree without using the ped() constructor. To give a hint of the flexibility, we show 3 alternative ways to code this.

Alternative A
The obvious starting point is nuclearPed(), with nch = 1 to indicate 1 child. By default, this creates a trio with numeric labels (father=1; mother=2; child=3) and a male child. Hence we fix the gender with swapSex(), and edit the labels with relabel():

trio2 = nuclearPed(nch = 1)
trio2 = swapSex(trio2, ids = 3)
trio2 = relabel(trio2, new = c("fa", "mo", "girl"))

Pedigree building with pedtools works very well with R’s pipe operator |>. For example, the above commands could be written in one chain like this:

trio2 = nuclearPed(1) |> 
  swapSex(3) |> 
  relabel(new = c("fa", "mo", "girl"))

Alternative B
Even quicker than the pipe version is the following one-liner, where all details are specified directly in the call to nuclearPed().

trio3 = nuclearPed(father = "fa", mother = "mo", children = "girl", sex = 2)

Alternative C
Here is another possibility. We start by creating the father as a singleton, and then add the daughter:

trio4 = singleton("fa") |> 
  addDaughter("fa", id = "girl") |> 
  relabel(old = "1", new = "mo")
#> Creating new mother: 1

Note that addDaughter() automatically created the mother as “1”, so we needed to relabel her.

Example 2: An inbred child

This time we will create this inbred family:

Alternative A
One approach is to first create the pedigree consisting of individuals 1-6, with halfSibPed(), and then use addSon() to add the inbred child.

x1 = halfSibPed(nch1 = 1, nch2 = 2, sex1 = 1, sex2 = 2:1) |> 
  addSon(parents = 4:5)

Alternative B
We could also view the half siblings 4 and 5 as half cousins of degree 0. The halfCousinPed() function accepts an option child = TRUE adding an inbred child. The labels will be different with this approach, so we need to relabel at the end.

x2 = halfCousinPed(0, child = T) |> 
  addSon(parents = 2:3) |> 

We can see that the two alternatives produce the same result:

identical(x1, x2)
#> [1] TRUE

1.4.1 Example 3: A complex family tree

Here we consider the family tree below, extending both upwards and downwards from a single person. We will use this example to demonstrate the mergePed() function, which “glues” together two pedigrees by the indicated members.

# Top part
x = ancestralPed(g = 2) # bottom person is "7"

# Bottom part
y = halfCousinPed(degree = 1) # top person is "2"
y = swapSex(y, 4)
#> Changing sex of spouses as well: 5

# Merge
z = mergePed(x, y, by = c("7" = "2"), relabel = TRUE)

Note the argument by = c("7" = "2"), which means that individual “7” in x should be identified with “2” in y. As seen in the plot below, this individual ends up as “8” after relabelling in the final result.

plotPedList(list(x, y, z))

1.5 Pedigree subsets

Many situations call for selecting all pedigree members sharing some property, e.g., all females, or all descendants of some person. Several utilities in pedtools exist to help with such tasks. Generally these come in two flavours: 1) members with certain global property, and 2) members with a certain relationship to a given individual.

Pedigree members with a certain property
Each of the following functions returns a vector specifying the members with the given property.

By default, the output of these functions is a character vector containing ID labels. However, adding the option internal = TRUE will give you an integer vector instead, reporting the internal indices of the members. This is frequently used in the source code of pedtools, but is usually not intended for end users of the package.

Relatives of a given individual
The functions below take as input a ped object and the label of a single member. They return a vector of all members with the given relation to that individual.

2 Markers

The other main theme of the pedtools package (pedigrees being the first) are marker genotypes.

2.1 Creating marker objects

Marker objects created with the marker() function. For example, the following command makes an empty marker associated with the trio pedigree:

#>    id  <NA>
#>    fa   -/-
#>    mo   -/-
#>  girl   -/-
#> * * * * * *  
#> Position: NA 
#> Mutation: none 
#> Frequencies:
#>    1   2
#>  0.5 0.5

As shown in the output, the marker is indeed empty: All pedigree members have missing genotypes, and there is no assigned name or position. By default, markers are diallelic, with alleles 1 and 2, with equal frequencies. For a more interesting example, let us make a SNP named “snp1”, with alleles “A” and “B”. The father is homozygous “A/A”, while the mother is heterozygous. We store it in a variable m1 for later use.

m1 = marker(trio, fa = "A/A", mo = "A/B", name = "snp1")

This illustrates several points. Firstly, individual genotypes may be specified using the ID labels. The different alleles occurring in the genotypes is interpreted as the complete set of alleles for the marker. Finally, these are assigned equal frequencies. Of course, this behaviour can be overridden, by declaring alleles frequencies explicitly:

marker(trio, fa = "A/A", mo = "A/B", afreq = c(A = .2, B = .3, C = .5))
#>    id  <NA>
#>    fa   A/A
#>    mo   A/B
#>  girl   -/-
#> * * * * * *  
#> Position: NA 
#> Mutation: none 
#> Frequencies:
#>    A   B   C
#>  0.2 0.3 0.5

The markers chromosome can be declared using the chrom argument, and similarly its position by posMb (megabases). Markers with unknown chromosome are treated as autosomal. To define an X-linked marker, put chrom = "X". the fact that males are hemizygous on X (i.e. they have only one allele) is reflected in the printout of such markers:

m2 = marker(trio, fa = "A/A", mo = "A/B", chrom = "X", name = "snpX")
#>    id  snpX
#>    fa     A
#>    mo   A/B
#>  girl   -/-
#> * * * * * *  
#> Position: chr = X, Mb = NA 
#> Mutation: none 
#> Frequencies:
#>    A   B
#>  0.5 0.5

A side note: It may come as a surprise that you don’t need quotes around the ID labels (which are characters!) in the above commands. This is because marker() uses non-standard evaluation (NSE), a peculiarity of the R language which often leads to less typing and more readable code.1 Unfortunately, this doesn’t work with numerical ID labels. Thus to assign a genotype to someone labelled “1” you need quotes, as in marker(trio, "1" = "A/A").

2.2 Plotting pedigrees with marker data

Including marker data in a pedigree plot is straightforward:

plot(trio, marker = m1)

The appearance of the genotypes can be tweaked in various ways, as documented in ?plot.ped. Here’s an example:

plot(trio, marker = m1, showEmpty = T, missing = "?", sep = "")

2.3 Markers attached to pedigrees

In most applications it is useful to attach markers to their ped object. In particular for bigger projects with many markers, this makes it easier to manipulate the dataset as a unit.

To attach a marker object m (which could be a list of several markers) to a pedigree x, there are two main options:

The difference between these is that setMarkers() replaces all existing markers, while addMarkers() appends m to the existing ones. In our trio example the two are equivalent since there are no existing markers.

trio = setMarkers(trio, list(m1, m2))
#>    id fid mid sex snp1 snpX
#>    fa   *   *   1  A/A    A
#>    mo   *   *   2  A/B  A/B
#>  girl  fa  mo   2  -/-  -/-

There is a handy shortcut, addMarker() (without the ‘s’), allowing you to create and attach a single marker in one go. The command addMarker(x, ...) is essentially equivalent to setMarkers(x, marker(x, ...)). It is also well adapted to piping, as in this example:

nuclearPed(1) |> 
  addMarker(name = "myMarker", alleles = c("a", "b", "c")) |>
  setGenotype(id = 3, geno = "a/c")
#>  id fid mid sex myMarker
#>   1   *   *   1      -/-
#>   2   *   *   2      -/-
#>   3   1   2   1      a/c

Selecting and removing attached markers
Four closely related functions functions are useful for manipulating markers attached to a pedigree:

All of these have exactly the same arguments, described in more detail in ?marker_select. Let us do a couple of examples here. Recall that by now, our trio has two attached markers; the first is called “snp1”, and the other is on the X chromosome.

whichMarkers(trio, chrom = "X")
#> [1] 2
selectMarkers(trio, markers = "snp1")
#>    id fid mid sex snp1
#>    fa   *   *   1  A/A
#>    mo   *   *   2  A/B
#>  girl  fa  mo   2  -/-

2.4 Accessing and modifying individual markers

Internally, a marker object is stored as a matrix with two columns (one for each allele) and one row for each pedigree member. The matrix is numeric (for computational convenience) while the allele labels and other meta information are added as attributes. The most important of these are:

In addition to those listed above, there are two more attributes: pedmembers and sex. They store the ID labels and genders of the pedigree associated with the marker, and are only used to empower the printing method of marker objects.

Marker accessor functions
For each marker attribute listed above, there is a corresponding function with the same name for retrieving its content. These functions take as input either a marker object, or a ped object together with the name (or index) of an attached marker. This may sound a bit confusing, but a few examples will make it clear!

Recall that our marker “snp1” exists in two copies: One is stored in the variable m1, while the other is attached to trio. In both cases we can extract the allele frequencies with the function afreq().

#>   A   B 
#> 0.5 0.5
afreq(trio, marker = "snp1")
#>   A   B 
#> 0.5 0.5

We can also modify the frequencies using this syntax. To avoid confusion about the allele order, the frequencies must be named with the allele labels (just as in the output of afreq() above).

afreq(trio, marker = "snp1") = c(A = 0.9, B = 0.1)

In addition to the functions getting and setting marker attributes, there is one more important marker accessor, namely genotype(). This returns the genotype of a specified individual, and can also be used to modify genotypes. As the others, it can be applied to marker objects directly, or to pedigrees with attached markers. Here we show a few examples of the latter type:

# Girl is not genotyped
genotype(trio, "snpX", id = "girl")
#> [1] NA NA

# Set genotype
genotype(trio, "snpX", id = "girl") = "A/A"

# Inspect the result
#>    id fid mid sex snp1 snpX
#>    fa   *   *   1  A/A    A
#>    mo   *   *   2  A/B  A/B
#>  girl  fa  mo   2  -/-  A/A

2.5 Getting/setting/modifying many markers simultaneously

Several functions in pedtools are indented for modifying many (or all) markers at the same time. Their purpose and typical use cases are summarised in the table below. The argument x always denotes a ped object.
Use … When you want to … For example to …
getAlleles(x) extract all alleles as a matrix. do summary stats on the marker alleles
getFreqDatabase(x) extract allele frequencies as a data.frame in allelic ladder format. transfer to other objects, or write the database to a file
getMarkers(x) extract list of marker objects. Each marker is a N * 2 allele matrix (N = pedsize(x)) with locus annotations as attributes do computations
setAlleles(x, ...) replace the genotypes of x without changing the locus attributes. erase all genotypes
setFreqDatabase(x, db) replace all allele frequencies without changing the genotype data. The input is a data.frame in allelic ladder format. Conceptually equivalent to setMarkers(x, alleleMatrix = getAlleles(x), locusAnnotations = db). change the frequency database
setMarkers(x, ...) attach marker objects with or without genotype data. Locus attributes are indicated as a list; genotypes as a matrix or data.frame. prepare joint manipulation of a pedigree and marker data
Convert convert x to a data.frame, with pedigree columns in standard format followed by genotype columns. One column per marker, with genotype format a/b and missing alleles indicated as -. pretty-print ped objects
as.matrix(x) convert x to a numerical matrix, with additional info attached as attributes. modify a pedigree with marker data
transferMarkers(from, to) transfer genotypes and attributes between pedigree objects (or lists of such). transfer simulated marker data

  1. You may have come across NSE before, for instance when using subset() on a data.frame. To learn more about NSE, I recommend this book chapter by Hadley Wickham:↩︎