Pedixplorer tutorial
31/08/2023
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:
- The
Pedigreeobjectvignette("pedigree_object", package = "Pedixplorer") - The alignment algorithm used to create the pedigree structure
vignette("pedigree_alignment", package = "Pedixplorer") - The kinship algorithm
vignette("pedigree_kinship", package = "Pedixplorer") - The plotting algorithm used to plot the pedigree
vignette("pedigree_plot", package = "Pedixplorer")
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':
##
## layoutlibrary(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, unionlibrary(Pedixplorer)##
## Attaching package: 'Pedixplorer'## The following object is masked from 'package:dplyr':
##
## id## The following object is masked from 'package:ggplot2':
##
## rel3 The Pedigree S4 object
The Pedigree object is a list of dataframes that describe the family
structure. It contains the following components:
- ped: a
Pedobject with the pedigree informationhelp(Ped). - rel: a
Relobject with the relationship informationhelp(Rel). - scales: a
Scalesobject of 2 dataframe with the filling and borders informations for the plothelp(Scales). - hints: a
Hintsobjects with 2 slots indicating the horder and the spouse to organise the pedigree structurehelp(Hints).
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 NApedi <- 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)
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
)
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 1ped2 <- Pedigree(fixped2)
plot(ped2)
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 . . . . . . . . 0.5For 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 . . . . . . 0.50kin_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 . . . 0.5kin_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 0.506.3 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 3pedi <- 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 0.5000kin_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 . . . . 0.5Note 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 2plot(ped2)
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)$Names <- c(
"John\nDalton", "Linda", "Jack", "Rachel", "Joe", "Deb",
"Lucy", "Ken", "Barb", "Mike", "Matt",
"Mindy", "Mark", "Marie\nCurie"
)
plot(ped2, label = "Names", cex = 0.7)
7.3 Affected Indicators
We show how to specify affected status with a single indicator and
multiple indicators. First, we use the affected indicator from
sampleped, which contains 0/1 indicators and NA as missing, and let it
it indicate blue eyes. Next, we create a vector as an indicator for
baldness. And add it as a second filling scale for the plot with
generate_colors(add_to_scale = TRUE). The plot shapes for each subject
is therefore divided into two equal parts and shaded differently to
indicate the two affected indicators.
mcols(ped2)$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)
)
# 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 twins2= Dizygotic twins3= Twins of unknown zygosity4= 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)
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)
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)
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)