# Pedixplorer tutorial

Louis Le Nezet

#### 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 `Pedigree` object `vignette("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':
    ##
    ##     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:

- ped: a `Ped` object with the pedigree information `help(Ped)`.
- rel: a `Rel` object with the relationship information `help(Rel)`.
- scales: a `Scales` object of 2 dataframe with the filling and borders informations for the plot `help(Scales)`.
- hints: a `Hints` objects with 2 slots indicating the horder and the spouse to organise the pedigree structure `help(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                               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)

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     1

    ped2 <- 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  .     .      .    .     .     .     .     .      0.5

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)

## 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 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)

### 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)

## 8.1 Align by Input Order

The plot method does a decent job aligning subjects given the order of the subjects when the Pedigree object is made, and sometimes has to make two copies of a subject. If we change the order of the subjects when creating the Pedigree, we can help the plot method reduce the need to duplicate subjects, as Figure~ no longer has subject 110 duplicated.

    df1reord <- df1[c(35:41, 1:34), ]
    ped1reord <- Pedigree(df1reord, relate1)
    plot(ped1reord, cex = 0.7)

## 8.2 Plot colors and scales

The `Pedigree` object contains a `Scales` object that can be modified to change the colors and patterns used in the plot. To make it easy for the user to modify it a function `generate_colors()` is available. This function will generate a color palette for the filling and the bordering of the plot. This function transform a given column of the dataframe into a factor and generate a color palette for each level of the factor. The user can then modify the colors and the patterns used for the filling and the bordering of the plot.

To do so you can do as follow:

    scales(ped1)

    ## An object of class "Scales"
    ## Slot "fill":
    ##   order column_values    column_mods mods            labels affected  fill density angle
    ## 1     1     affection affection_mods    0 Healthy <= to 0.5    FALSE white      NA    NA
    ## 2     1     affection affection_mods    1 Affected > to 0.5     TRUE   red      NA    NA
    ## 3     1     affection affection_mods   NA              <NA>       NA  grey      NA    NA
    ##
    ## Slot "border":
    ##   column_values column_mods mods        labels border
    ## 1         avail  avail_mods   NA            NA   grey
    ## 2         avail  avail_mods    1     Available  green
    ## 3         avail  avail_mods    0 Non Available  black

    # Remove proband and asymptomatic status as they need to be
    # affected and unaffected respectively for the new status
    proband(ped(ped1)) <- FALSE
    asymptomatic(ped(ped1)) <- FALSE
    ped1 <- generate_colors(
        ped1, col_aff = "num",
        add_to_scale = TRUE, is_num = TRUE,
        keep_full_scale = TRUE, breaks = 2,
        colors_aff = c("blue", "green"),
        colors_unaff = c("yellow", "brown"),
        threshold = 3, sup_thres_aff = FALSE
    )

    plot(ped1, cex = 0.7)

    # To modify a given scale you can do as follow
    fill(ped1)

    ##   order column_values    column_mods mods                       labels affected    fill density
    ## 1     1     affection affection_mods    0            Healthy <= to 0.5    FALSE   white      NA
    ## 2     1     affection affection_mods    1            Affected > to 0.5     TRUE     red      NA
    ## 3     1     affection affection_mods   NA                         <NA>       NA    grey      NA
    ## 4     2           num       num_mods    1      Affected < to 3 : (1,2]     TRUE #0000FF      NA
    ## 5     2           num       num_mods    2 Affected < to 3 : [-0.002,1]     TRUE #00FF00      NA
    ## 6     2           num       num_mods    3    Healthy >= to 3 : (4.5,6]    FALSE #FFFF00      NA
    ## 7     2           num       num_mods    4    Healthy >= to 3 : [3,4.5]    FALSE #A52A2A      NA
    ## 8     2           num       num_mods   NA                      NA : NA       NA    grey      NA
    ##   angle
    ## 1    NA
    ## 2    NA
    ## 3    NA
    ## 4    NA
    ## 5    NA
    ## 6    NA
    ## 7    NA
    ## 8    NA

    fill(ped1)$fill[4] <- "#970b6d"
    fill(ped1)$density[5] <- 30
    fill(ped1)$angle[5] <- 45
    border(ped1)$border <- c("red", "black", "orange")
    plot(
        ped1, cex = 0.7, legend = TRUE,
        leg_loc = c(6, 16, 1, 1.8), leg_cex = 0.5,
    )

## 8.3 Render interactive

The plot can be made interactive by setting the `ggplot_gen` argument to `TRUE`. This will generate a ggplot object that can be passed down to `plotly::ggplotly()` to make the plot interactive.

    plot_list <- plot(
        ped1,
        symbolsize = 0.8, # Reduce the symbole size
        title = "My pedigree", # Add a title
        lwd = 0.5, # Set the line width
        cex = 0.8, # Set the text size
        ggplot_gen = TRUE, # Use ggplot2 to draw the Pedigree
        tips = c(
            "id", "avail",
            "affection",
            "num"
        ) # Add some information in the tooltip
    )

    plotly::ggplotly(
        plot_list$ggplot,
        tooltip = "text"
    ) %>%
        plotly::layout(hoverlabel = list(bgcolor = "darkgrey"))

# 9 Pedigree Utility Functions

## 9.1 Ped as a data.frame

A main features of a `Pedigree` object are vectors with an element for each subject. It is sometimes useful to extract these vectors from the Pedigree object into a `data.frame` with basic information that can be used to construct a new `Pedigree` object. This is possible with the `as.data.frame()` method, as shown below.

    dfped2 <- as.data.frame(ped(ped2))
    dfped2

## 9.2 Automatic filtering

The `useful_inds()` allows to filter a huge and complex pedigree easily by providing the informative individuals and the maximal distance from them to the other individuals. The informative individuals are provided through the `informative` argument and can take the following values:

- `AvAf` (available and affected)
- `AvOrAf` (available or affected)
- `Av` (available only)
- `Af` (affected only)
- `All` (all individuals)
- A numeric/character vector of individuals id
- A boolean

They will be the individuals from which the distance will be compute. This distance correspond to :

\[ minDist = log2(\frac{1}{\max(kinship)}) \]

Therefore, the minimum distance is 0 when the maximum kinship is 1 and is infinite when the maximum kinship is 0. For siblings, the kinship value is 0.5 and the minimum distance is 1. Each time the kinship degree is divided by 2, the minimum distance is increased by 1. This distance can be understood as the number of step needed to link two individuals on a pedigree.

Therefore the threshold `max_dist` can be interpreted as the size of a circle around the informative individuals. All individuals inside the circle will be kept and the others disregarded.

The `useful_inds()` function is used as follow and return the same `Pedigree` object but with the `useful` column updated in the `Ped` object :

    data(sampleped)
    ped1 <- Pedigree(sampleped)
    ped1 <- is_informative(ped1, informative = c("1_110", "1_120"))
    ped1 <- useful_inds(ped1, max_dist = 1)
    print(useful(ped(ped1)))

    ##  [1] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
    ## [17] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
    ## [33] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
    ## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE

    ped_filtered <- ped1[useful(ped(ped1))]
    plot(ped_filtered)

## 9.3 Subsetting and Trimming

Pedigrees with large size can be a bottleneck for programs that run calculations on them. The Pedixplorer package contains some routines to identify which subjects to remove. We show how a subject (e.g. subject 210) can be removed from ped2, and how the Pedigree object is changed by verifying that the `Rel` object no longer has the twin relationship between subjects 210 and 211, as indicated by `id1` and `id2`.

    ped2_rm210 <- ped2[-10]
    rel(ped2_rm210)

    ## 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>

    rel(ped2)

    ## Rel object with 4 relationshipswith 1 MZ twin, 1 DZ twin, 1 UZ twin, 1 Spouse:
    ##           id1         id2     code       famid
    ##   <character> <character> <factor> <character>
    ## 1       2_210       2_211  MZ twin           2
    ## 2       2_204       2_208  DZ twin           2
    ## 3       2_212       2_213  UZ twin           2
    ## 4       2_211       2_212   Spouse           2

The steps above also works by the `id` of the subjects themselves.
We provide `subset()`, which trims subjects from a pedigree by their `id` or other argument. Below is an example of removing subject 110, as done above, then we further trim the pedigree by a vector of subject ids. We check the trimming by looking at the `id` vector and the `Rel` object.

    ped2_trim210 <- subset(ped2, "2_210", keep = FALSE)
    id(ped(ped2_trim210))

    ##  [1] "2_201" "2_202" "2_203" "2_204" "2_205" "2_206" "2_207" "2_208" "2_209" "2_211" "2_212" "2_213"
    ## [13] "2_214"

    rel(ped2_trim210)

    ## Rel object with 3 relationshipswith 0 MZ twin, 1 DZ twin, 1 UZ twin, 1 Spouse:
    ##           id1         id2                     code       famid
    ##   <character> <character> <c("ordered", "factor")> <character>
    ## 1       2_204       2_208                  DZ twin           2
    ## 2       2_212       2_213                  UZ twin           2
    ## 3       2_211       2_212                   Spouse           2

    ped2_trim_more <- subset(ped2_trim210, c("2_212", "2_214"), keep = FALSE)
    id(ped(ped2_trim_more))

    ##  [1] "2_201" "2_202" "2_203" "2_204" "2_205" "2_206" "2_207" "2_208" "2_209" "2_211" "2_213"

    rel(ped2_trim_more)

    ## Rel object with 1 relationshipwith 0 MZ twin, 1 DZ twin, 0 UZ twin, 0 Spouse:
    ##           id1         id2                     code       famid
    ##   <character> <character> <c("ordered", "factor")> <character>
    ## 1       2_204       2_208                  DZ twin           2

## 9.4 Shrinking

An additional function in Pedixplorer is `shrink()`, which shrinks a pedigree to a specified bit size while maintaining the maximal amount of information for genetic linkage and association studies. Using an indicator for availability and affected status, it removes subjects in this order: + unavailable with no available descendants + available and are not parents + available who have missing affected status + available who are unaffected + available who are affected

We show how to shrink Pedigree 1 to bit size 30, which happens to be the bit size after removing only the unavailable subjects. We show how to extract the shrunken `Pedigree` object from the `shrink()` result, and plot it.

    set.seed(200)
    shrink1_b30 <- shrink(ped1, max_bits = 30)
    print(shrink1_b30[c(2:8)])

    ## $id_trim
    ##  [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123" "1_131" "1_132" "1_134"
    ## [13] "1_139" "2_205" "2_210" "2_213" "1_141" "1_140" "2_214" "2_212"
    ##
    ## $id_lst
    ## $id_lst$unavail
    ##  [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123" "1_131" "1_132" "1_134"
    ## [13] "1_139" "2_205" "2_210" "2_213"
    ##
    ## $id_lst$affect
    ## [1] "1_141" "1_140" "2_214" "2_212"
    ##
    ##
    ## $bit_size
    ## [1] 62 39 37 33 31 28
    ##
    ## $avail
    ##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
    ## [17]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
    ##
    ## $pedSizeOriginal
    ## [1] 55
    ##
    ## $pedSizeIntermed
    ## [1] 39
    ##
    ## $pedSizeFinal
    ## [1] 29

    plot(shrink1_b30$pedObj)

    ## Multiple families present, only plotting family 1

Now shrink Pedigree 1 to bit size 25, which requires removing subjects who are informative. If there is a tie between multiple subjects about who to remove, the method randomly chooses one of them. With this seed setting, the method removes subjects 140 then 141.

    set.seed(10)
    shrink1_b25 <- shrink(ped1, max_bits = 25)
    print(shrink1_b25[c(2:8)])

    ## $id_trim
    ##  [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123" "1_131" "1_132" "1_134"
    ## [13] "1_139" "2_205" "2_210" "2_213" "1_130" "1_140" "1_141" "2_207" "1_126" "1_125"
    ##
    ## $id_lst
    ## $id_lst$unavail
    ##  [1] "1_101" "1_102" "1_107" "1_108" "1_111" "1_113" "1_121" "1_122" "1_123" "1_131" "1_132" "1_134"
    ## [13] "1_139" "2_205" "2_210" "2_213"
    ##
    ## $id_lst$affect
    ## [1] "1_130" "1_140" "1_141" "2_207" "1_126" "1_125"
    ##
    ##
    ## $bit_size
    ## [1] 62 39 37 35 31 29 27 23
    ##
    ## $avail
    ##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
    ## [17]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
    ##
    ## $pedSizeOriginal
    ## [1] 55
    ##
    ## $pedSizeIntermed
    ## [1] 39
    ##
    ## $pedSizeFinal
    ## [1] 28

    plot(shrink1_b25$pedObj)

    ## Multiple families present, only plotting family 1

# 10 Select Unrelateds

In this section we briefly show how to use `unrelated()` to find a set of the maximum number of unrelated available subjects from a pedigree. The input required is a `Pedigree` object and a vector indicating availability. In some pedigrees there are numerous sets of subjects that satisfy the maximum number of unrelateds, so the method randomly chooses from the set. We show two sets of subject ids that are selected by the routine and discuss below.

    ped2 <- Pedigree(df2)
    set.seed(10)
    set1 <- unrelated(ped2)
    set1

    ## [1] "2_203" "2_206"

    set2 <- unrelated(ped2)
    set2

    ## [1] "2_203" "2_214"

We can easily verify the sets selected by `unrelated()` by referring to Figure~ and see that subjects 203 and 206 are unrelated to everyone else in the pedigree except their children. Furthermore, we see in df2 that of these two, only subject 203 is available. Therefore, any set of unrelateds who are available must include subject 203 and one of the these subjects: 201, 204, 206, 207, 212 and 214, as indicated by the kinship matrix for Pedigree 2 subset to those with availability status of 1.

    kin2 <- kinship(ped2)
    is_avail <- id(ped(ped2))[avail(ped(ped2))]
    kin2

    ## 14 x 14 sparse Matrix of class "dsCMatrix"

    ##   [[ suppressing 14 column names '2_201', '2_202', '2_203' ... ]]

    ##
    ## 2_201 0.500 .     .    0.250 0.250 0.250 0.250 0.250 .    0.1250 0.1250 0.1250 0.1250 0.1250
    ## 2_202 .     0.500 .    0.250 0.250 0.250 0.250 0.250 .    0.1250 0.1250 0.1250 0.1250 0.1250
    ## 2_203 .     .     0.50 .     .     .     .     .     .    0.2500 0.2500 .      .      .
    ## 2_204 0.250 0.250 .    0.500 0.250 0.250 0.250 0.250 .    0.2500 0.2500 0.1250 0.1250 0.1250
    ## 2_205 0.250 0.250 .    0.250 0.500 0.250 0.250 0.250 .    0.1250 0.1250 0.1250 0.1250 0.1250
    ## 2_206 0.250 0.250 .    0.250 0.250 0.500 0.250 0.250 .    0.1250 0.1250 0.1250 0.1250 0.1250
    ## 2_207 0.250 0.250 .    0.250 0.250 0.250 0.500 0.250 .    0.1250 0.1250 0.1250 0.1250 0.1250
    ## 2_208 0.250 0.250 .    0.250 0.250 0.250 0.250 0.500 .    0.1250 0.1250 0.2500 0.2500 0.2500
    ## 2_209 .     .     .    .     .     .     .     .     0.50 .      .      0.2500 0.2500 0.2500
    ## 2_210 0.125 0.125 0.25 0.250 0.125 0.125 0.125 0.125 .    0.5000 0.2500 0.0625 0.0625 0.0625
    ## 2_211 0.125 0.125 0.25 0.250 0.125 0.125 0.125 0.125 .    0.2500 0.5000 0.0625 0.0625 0.0625
    ## 2_212 0.125 0.125 .    0.125 0.125 0.125 0.125 0.250 0.25 0.0625 0.0625 0.5000 0.2500 0.2500
    ## 2_213 0.125 0.125 .    0.125 0.125 0.125 0.125 0.250 0.25 0.0625 0.0625 0.2500 0.5000 0.2500
    ## 2_214 0.125 0.125 .    0.125 0.125 0.125 0.125 0.250 0.25 0.0625 0.0625 0.2500 0.2500 0.5000

    kin2[is_avail, is_avail]

    ## 8 x 8 sparse Matrix of class "dsCMatrix"
    ##       2_201 2_203 2_204 2_206 2_207  2_211  2_212  2_214
    ## 2_201 0.500  .    0.250 0.250 0.250 0.1250 0.1250 0.1250
    ## 2_203 .      0.50 .     .     .     0.2500 .      .
    ## 2_204 0.250  .    0.500 0.250 0.250 0.2500 0.1250 0.1250
    ## 2_206 0.250  .    0.250 0.500 0.250 0.1250 0.1250 0.1250
    ## 2_207 0.250  .    0.250 0.250 0.500 0.1250 0.1250 0.1250
    ## 2_211 0.125  0.25 0.250 0.125 0.125 0.5000 0.0625 0.0625
    ## 2_212 0.125  .    0.125 0.125 0.125 0.0625 0.5000 0.2500
    ## 2_214 0.125  .    0.125 0.125 0.125 0.0625 0.2500 0.5000

# 11 Session information

    sessionInfo()

    ## R version 4.5.0 RC (2025-04-04 r88126 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##   LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] LC_COLLATE=C                           LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] Pedixplorer_1.4.0 dplyr_1.1.4       plotly_4.10.4     ggplot2_3.5.2     BiocStyle_2.36.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.6          xfun_0.52             bslib_0.9.0           htmlwidgets_1.6.4
    ##  [5] lattice_0.22-7        crosstalk_1.2.1       quadprog_1.5-8        vctrs_0.6.5
    ##  [9] tools_4.5.0           generics_0.1.3        stats4_4.5.0          tibble_3.2.1
    ## [13] pkgconfig_2.0.3       Matrix_1.7-3          data.table_1.17.0     S4Vectors_0.46.0
    ## [17] readxl_1.4.5          lifecycle_1.0.4       compiler_4.5.0        stringr_1.5.1
    ## [21] shinytoastr_2.2.0     munsell_0.5.1         tinytex_0.57          httpuv_1.6.15
    ## [25] shinyWidgets_0.9.0    htmltools_0.5.8.1     sass_0.4.10           yaml_2.3.10
    ## [29] lazyeval_0.2.2        pillar_1.10.2         later_1.4.2           jquerylib_0.1.4
    ## [33] tidyr_1.3.1           DT_0.33               cachem_1.1.0          magick_2.8.6
    ## [37] mime_0.13             tidyselect_1.2.1      digest_0.6.37         stringi_1.8.7
    ## [41] colourpicker_1.3.0    purrr_1.0.4           bookdown_0.43         labeling_0.4.3
    ## [45] fastmap_1.2.0         grid_4.5.0            colorspace_2.1-1      cli_3.6.4
    ## [49] magrittr_2.0.3        withr_3.0.2           scales_1.3.0          promises_1.3.2
    ## [53] rmarkdown_2.29        httr_1.4.7            gridExtra_2.3         cellranger_1.1.0
    ## [57] shiny_1.10.0          evaluate_1.0.3        knitr_1.50            shinycssloaders_1.1.0
    ## [61] miniUI_0.1.1.1        viridisLite_0.4.2     rlang_1.1.6           Rcpp_1.0.14
    ## [65] xtable_1.8-4          glue_1.8.0            BiocManager_1.30.25   BiocGenerics_0.54.0
    ## [69] jsonlite_2.0.0        R6_2.6.1              plyr_1.8.9