Introduction

The pedigree routines came out of a simple need – to quickly draw a Pedigree structure on the screen, within R, that was “good enough” to help with debugging the actual routines of interest, which were those for fitting mixed effecs Cox models to large family data. As such the routine had compactness and automation as primary goals; complete annotation (monozygous twins, multiple types of affected status) and most certainly elegance were not on the list. Other software could do that much better.

It therefore came as a major surprise when these routines proved useful to others. Through their constant feedback, application to more complex pedigrees, and ongoing requests for one more feature, the routine has become what it is today. This routine is still not suitable for really large pedigrees, nor for heavily inbred ones such as in animal studies, and will likely not evolve in that way. The authors fondest hope is that others will pick up the project.

Pedigree Constructor

Arguments

The Pedigree function is the first step, creating an object of class Pedigree. It accepts the following input

ped_df A dataframe containing the columns
- id A numeric or character vector of subject identifiers.
- dadid The identifier of the father.
- momid The identifier of the mother.
- famid Optional, a numeric or character vector of family identifiers.
- sex The gender of the individual. This can be a numeric variable with codes of 1=male, 2=female, 3=unknown, or NA=unknown. A character or factor variable can also be supplied containing the above; the string may be truncated and of arbitrary case.
- fertility Optional, will be transformed to an ordered factor using the fertility_to_factor() function. infertile_choice_na, infertile, fertile The following values are recognized:
  - character() or factor() : “fertile”, “infertile”, “steril”, “infertile_choice”, “infertile_na”, “infertile_choice_na”
  - numeric() : 1 = “fertile”, 0 = “infertile”
  - logical() : TRUE = “fertile”, FALSE = “infertile”
- miscarriage Optional, will be transformed to an factor using the miscarriage_to_factor() function. TOP, SAB, ECT, FALSE The following values are recognized:
  - character() or factor() : “spontaneous”, “spontaenous abortion”, “termination”, “terminated”, “termination of pregnancy”, “ectopic”, “ectopic pregnancy”, “false”
  - numeric() : 0 = “FALSE”, NA = “FALSE”
  - logical() : FALSE = “FALSE”
- deceased Optional, a logical variable with 0=alive and 1=dead.
- avail Optional, a logical variable with 0=unavailable and 1=available.
- evaluated Optional, a logical variable with 0=not evaluated and 1=evaluated.
- consultand Optional, a logical variable with 0=not consultand and 1=consultand.
- proband Optional, a logical variable with 0=not proband and 1=proband.
- affected Optional, a logical variable with 0=unaffected and 1=affected.
- carrier Optional, a logical variable with 0=not a carrier 1=carrier and NA=unknown.
- asymptomatic Optional, a logical variable with 0=not asymptomatic 1=asymptomatic and NA=asymptomatic.
- adopted Optional, a logical variable with 0=not adopted 1=adopted.
rel_df Optional, a data frame with three columns or four columns.
- id1 identifier values of the subject pairs
- id2 identifier values of the subject pairs
- code relationship codification : 1=Monozygotic twin, 2=Dizygotic twin, 3=twin of unknown zygosity, 4=Spouse.
- famid Optional, a numeric or character vector of family identifiers.
cols_ren_ped Optional, a named list for the renaming of the ped_df dataframe
cols_ren_rel Optional, a named list for the renaming of the rel_df dataframe
normalize Optional, a logical to know if the data should be normalised.
hints Optional, a list containing the horder in which to plot the individuals and the matrix of the spouse.

Notes

Note that a factor variable is not listed as one of the choices for the subject identifier. This is on purpose. Factors were designed to accomodate character strings whose values came from a limited class – things like race or gender, and are not appropriate for a subject identifier. All of their special properties as compared to a character variable turn out to be backwards for this case, in particular a memory of the original level set when subscripting is done.

However, due to the awful decision early on in S to automatically turn every character into a factor, unless you stood at the door with a club to head the package off, most users have become ingrained to the idea of using them for every character variable.

(I encourage you to set the global option stringsAsFactors = FALSE to turn off autoconversion, it will measurably improve your R experience).

Therefore, to avoid unnecessary hassle for our users the code will accept a factor as input for the id variables, but the final structure does not retain it. Gender and relation do become factors. Status follows the pattern of the survival routines and remains an integer.

Column renaming

Based on the dataframe given for ped_df and rel_df and their corresponding named list, the columns are renamed for them to be used correctly. The renaming is done as follow

rel_df <- data.frame(
    indId1 = c("110", "204"),
    indId2 = c("112", "205"),
    code = c(1, 2),
    family = c("1", "2")
)
cols_ren_rel <- list(
    id1 = "indId1",
    id2 = "indId2",
    famid = "family"
)

## Rename columns rel
old_cols <- as.vector(unlist(cols_ren_rel))
new_cols <- names(cols_ren_rel)
cols_to_ren <- match(old_cols, names(rel_df))
names(rel_df)[cols_to_ren[!is.na(cols_to_ren)]] <-
    new_cols[!is.na(cols_to_ren)]
print(rel_df)

##   id1 id2 code famid
## 1 110 112    1     1
## 2 204 205    2     2

Normalisation

If the normalisation process is selected normalize = TRUE, then both dataframe will be checked by their dedicated normalization function. It will ensure that all modalities are written correctly and set up the right way. If a famid column is present in the dataframe, then it will be aggregated to the id of each individual and separated by an ’’_’’ to ensure the uniqueness of the individuals identifiers.

library(Pedixplorer)
data("sampleped")
cols <- c("sex", "id", "avail")
summary(sampleped[cols])

##       sex             id                avail       
##  Min.   :1.000   Length:55          Min.   :0.0000  
##  1st Qu.:1.000   Class :character   1st Qu.:0.0000  
##  Median :2.000   Mode  :character   Median :0.0000  
##  Mean   :1.564                      Mean   :0.4364  
##  3rd Qu.:2.000                      3rd Qu.:1.0000  
##  Max.   :3.000                      Max.   :1.0000

pedi <- Pedigree(sampleped)
summary(as.data.frame(ped(pedi))[cols])

##       sex          id              avail        
##  male   :26   Length:55          Mode :logical  
##  female :27   Class :character   FALSE:31       
##  unknown: 2   Mode  :character   TRUE :24

Errors present after the normalisation process

If any error is detected after the normalisation process, then the normalised dataframe is gave back to the user with errors column added describing the encountered problems.

rel_wrong <- rel_df
rel_wrong$code[2] <- "A"
df <- Pedigree(sampleped, rel_wrong)

## Warning in .local(obj, ...): The relationship informations are not valid. Here is the normalised
## relationship informations with the identified problems

print(df)

##     id1   id2    code famid              error
## 1 1_110 1_112 MZ twin     1               <NA>
## 2 2_204 2_205    <NA>     2 code-not-recognise

Validation

Now that the data for the Pedigree object creation are ready, they are given to a new Pedigree object, trigerring the validation process.

This validation step will check up for many errors such as:

All necessary columns are present
No duplicated id
All momid and dadid are present in id
sex column only contain “male”, “female”, “unknown” values
fertility column only contain “fertile”, “infertile”, “infertile_choice_na”
miscarriage column only contain “SAB”, “TOP”, “ECT”, “FALSE”
deceased, avail, evaluated, consultand, proband, affected, carrier, asymptomatic, adopted only contains 0, 1 or NA values
Father are males and Mother are females
Twins have same parents and MZ twins have same sex
Hints object is valid and ids contained is in the Ped object
…

Pedigree Class

After validation an \(S4\) object is generated. This new concept make it possible to easily setup methods for this new type of object. The controls of the parameters is also more precise.

The Pedigree object contains 4 slots, each of them contains a different \(S4\) object containing a specific type of information used for the Pedigree construction.

ped a Ped object for the Pedigree information with at least the following slots:
- id the identifiers of the individuals
- dadid the identifiers of the fathers
- momid the identifiers of the mothers
- sex the gender of each individuals
rel a Rel object describing all special relationship beetween individuals that can’t be descibed in the ped slot. The minimal slots needed are :
- id1 the identifiers of the 1st individuals
- id2 the identifiers of the 2nd individuals
- code factor describing the type of relationship (“MZ twin”, “DZ twin”, “UZ twin”, “Spouse”)
scales a Scales object with two slots :
- fill a dataframe describing which modalities in which columns correspond to an affected individuals. Plotting information such as colour, angle and density are also provided
- border a dataframe describing which modalities in which columns to use to plot the border of the plot elements.
hints a Hints object with two slots :
- horder numeric vector for the ordering of the individuals plotting
- spouse a matrix of the spouses

For more information on each object:

help(Ped)
help(Rel)
help(Scales)
help(Hints)

Pedigree accessors

As the Pedigree object is now an \(S4\) class, we have made available a number of accessors. Most of them can be used as a getter or as a setter to modify a value in the correponding slot of the object

For the Pedigree object

Get/Set slots : ped(), rel(), scales(), hints()
Wrapper to the Ped object: mcols()
Wrapper of the Scales object: fill(), border()
Wrapper of the Hints object: horder(), spouse()

For the Ped object

Identity input: id(), dadid(), momid(), famid(), sex()
Other infos used : fertility(), miscarriage(), deceased(), avail(), evaluated(), consultand(), proband(), affected(), carrier(), asymptomatic(), adopted()
Computed : isinf(), kin(), useful()
Metadata : mcols()

For the Rel object

id1(), id2(), code(), famid()

For the Scales object

fill(), border()

For the Hints object

horder(), spouse()

Focus on `mcols()`

The mcols() accessors is the one you should use to add more informations to your individuals.

pedi <- Pedigree(sampleped)
mcols(pedi)

## DataFrame with 55 rows and 5 columns
##     affection       num       error affection_mods avail_mods
##     <integer> <integer> <character>      <numeric>  <numeric>
## 1           0         2          NA              0          0
## 2           1         3          NA              1          0
## 3           1         2          NA              1          0
## 4           0         4          NA              0          0
## 5          NA         6          NA             NA          0
## ...       ...       ...         ...            ...        ...
## 51          0         2          NA              0          0
## 52          0         1          NA              0          1
## 53          0         3          NA              0          1
## 54          0         2          NA              0          0
## 55          1         0          NA              1          1

## Add new columns as a threshold if identifiers of individuals superior
## to a given threshold for example
mcols(pedi)$idth <- ifelse(as.numeric(
    stringr::str_split_i(id(ped(pedi)), "_", 2)
) < 200, "A", "B")
mcols(pedi)$idth

##  [1] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
## [25] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "B" "B" "B" "B" "B" "B" "B"
## [49] "B" "B" "B" "B" "B" "B" "B"

Pedigree methods

With this new S4 object comes multiple methods to ease the use of it:

plot()
summary()
print()
show()
as.list()
[
shrink()
generate_colors()
is_informative()
kindepth()
kinship()
make_famid()
upd_famid()
num_child()
unrelated()
useful_inds()

## We can change the family name based on an other column
pedi <- upd_famid(pedi, mcols(pedi)$idth)

## We can substract a given family
ped_a <- pedi[famid(ped(pedi)) == "A"]

## Plot it
plot(ped_a, cex = 0.5)

## Individuals:  A_113 won't be plotted

Pedigree plot

## Do a summary
summary(ped_a)

## $pedigree_summary
## [1] "Ped object with 41 individuals and 6 metadata columns"
## 
## $relationship_summary
## [1] "Rel object with 0 relationshipswith 0 MZ twin, 0 DZ twin, 0 UZ twin, 0 Spouse"

## Coerce it to a list
as.list(ped_a)[[1]][1:3]

## $id
##  [1] "A_101" "A_102" "A_103" "A_104" "A_105" "A_106" "A_107" "A_108" "A_109" "A_110" "A_111" "A_112"
## [13] "A_113" "A_114" "A_115" "A_116" "A_117" "A_118" "A_119" "A_120" "A_121" "A_122" "A_123" "A_124"
## [25] "A_125" "A_126" "A_127" "A_128" "A_129" "A_130" "A_131" "A_132" "A_133" "A_134" "A_135" "A_136"
## [37] "A_137" "A_138" "A_139" "A_140" "A_141"
## 
## $dadid
##  [1] NA      NA      "A_135" NA      NA      NA      NA      NA      "A_101" "A_103" "A_103" "A_103"
## [13] NA      "A_103" "A_105" "A_105" NA      "A_105" "A_105" "A_107" "A_110" "A_110" "A_110" "A_110"
## [25] "A_112" "A_112" "A_114" "A_114" "A_117" "A_119" "A_119" "A_119" "A_119" "A_119" NA      NA     
## [37] NA      "A_135" "A_137" "A_137" "A_137"
## 
## $momid
##  [1] NA      NA      "A_136" NA      NA      NA      NA      NA      "A_102" "A_104" "A_104" "A_104"
## [13] NA      "A_104" "A_106" "A_106" NA      "A_106" "A_106" "A_108" "A_109" "A_109" "A_109" "A_109"
## [25] "A_118" "A_118" "A_115" "A_115" "A_116" "A_120" "A_120" "A_120" "A_120" "A_120" NA      NA     
## [37] NA      "A_136" "A_138" "A_138" "A_138"

## Shrink it to keep only the necessary information
lst1_s <- shrink(ped_a, max_bits = 10)
plot(lst1_s$pedObj, cex = 0.5)