ASOIAF.Rmd

---
title: "ASOIAF: How related are Jon and Danny?"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ASOIAF: How related are Jon and Danny?}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

Just how closely related are Jon Snow and Daenerys Targaryen? According to the lore of *A Song of Ice and Fire*, Daenerys is Jon's paternal aunt. This would suggest a theoretical genetic relatedness of 0.25, assuming a simple pedigree and no inbreeding. But with tangled ancestries and potentially missing information, how confident can we be in that estimate?

In this vignette, we use the `BGmisc` package to reconstruct the *ASOIAF* pedigree, handle incomplete parentage data, and compute additive genetic and common nuclear relatedness. We'll focus on Jon and Daenerys as a case study, but the methods generalize to any characters in the provided dataset.

## Load Packages and Data

We begin by loading the required libraries and examining the structure of the built-in `ASOIAF` pedigree.


```{r echo=TRUE, message=FALSE, warning=FALSE}
library(BGmisc)
library(tidyverse)
library(ggpedigree)

data(ASOIAF)
```

The ASOIAF dataset includes character IDs, names, family identifiers, and  parent identifiers for a subset of characters drawn from the *A Song of Ice and Fire* canon.

```{r}
head(ASOIAF)
```

## Prepare and Validate Sex Codes

Many pedigree-based algorithms rely on  biological sex for downstream calculationss and visualization. We use `checkSex()` to inspect the sex variable, repairing inconsistencies programmatically.


```{r}
df_got <- checkSex(ASOIAF,
  code_male = 1,
  code_female = 0,
  verbose = FALSE, repair = TRUE
)
```


## Compute Relatedness Matrices

With validated pedigree data, we can now compute two distinct relationship matrices:

- Additive genetic relatedness (add): Proportion of shared additive genetic variance between individuals.

- Common nuclear relatedness (cn): Indicates shared full-sibling (nuclear family) environments.

These are derived using ped2add() and ped2cn(), respectively. Both functions rely on internal graph traversal and adjacency structures. In this case:

- We specify isChild_method = "partialparent" to allow inclusion of dyads where one parent is unknown.

- We choose adjacency_method = "direct" for the additive matrix to optimize for computational speed.

- For the common nuclear matrix, we use adjacency_method = "indexed", which is slower but necessary for resolving sibling-group structures.

- We set `sparse = FALSE` to return full (dense) matrices rather than compressed sparse formats.


```{r}
add <- ped2com(df_got,
  isChild_method = "partialparent",
  component = "additive",
  adjacency_method = "direct",
  sparse = TRUE
)

mt <- ped2com(df_got,
  isChild_method = "partialparent",
  component = "mitochondrial",
  adjacency_method = "direct",
  sparse = TRUE
)

cn <- ped2cn(df_got,
  isChild_method = "partialparent",
  adjacency_method = "indexed",
  sparse = TRUE
)
```

## Convert to Pairwise Format

For interpretability, we convert these square matrices into long-format tables using `com2links()`. This function returns a dataframe where each row represents a unique pair of individuals, including their additive and common nuclear coefficients. 

```{r}
df_links <- com2links(
  writetodisk = FALSE,
  ad_ped_matrix = add, cn_ped_matrix = cn, mit_ped_matrix = mt,
  drop_upper_triangular = TRUE
) # %>%
#  filter(ID1 != ID2)
```

The function can return the entire matrix or just the lower triangular part, which is often sufficient for our purposes. Setting `drop_upper_triangular = TRUE` ensures we only retain one entry per dyad, since the matrices are symmetric. We also keep the data in memory by setting `writetodisk = FALSE`.

## Locate Jon and Daenerys

We next identify the rows in the pairwise relatedness table that correspond to Jon Snow and Daenerys Targaryen. First, we retrieve their individual IDs:


```{r}
# Find the IDs of Jon Snow and Daenerys Targaryen

jon_id <- df_got %>%
  filter(name == "Jon Snow") %>%
  pull(ID)

dany_id <- df_got %>%
  filter(name == "Daenerys Targaryen") %>%
  pull(ID)
```

Then we isolate their dyad:

```{r}
jon_dany_row <- df_links %>%
  filter(ID1 == jon_id | ID2 == jon_id) %>%
  filter(ID1 %in% dany_id | ID2 %in% dany_id)

jon_dany_row
```

This table contains the additive and nuclear relatedness estimates for Jon and Daenerys. If the pedigree reflects their canonical aunt-nephew relationship and is free from inbreeding, we’d expect to see an additive coefficient close to 0.25. However, the value is `r jon_dany_row$addRel[1]`, indicating a more complex relationship.

## Plotting the Pedigree with Incomplete Parental Information

Many real-world and fictional pedigrees contain individuals with unknown or partially known parentage. In such cases, plotting tools typically fail unless these gaps are handled. We use `checkParentIDs()` to:

- Identify individuals with one known parent and one missing

- Create "phantom" placeholders for the missing parent

- Optionally repair and harmonize parent fields

To facilitate plotting, we check for individuals with one known parent but a missing other. For those cases, we assign a placeholder ID to the missing parent.

```{r}
df_repaired <- checkParentIDs(df_got,
  addphantoms = TRUE,
  repair = TRUE,
  parentswithoutrow = FALSE,
  repairsex = FALSE
) %>% mutate(
  famID = 1,
  affected = case_when(
    ID %in% c(jon_id, dany_id, "365") ~ 1,
    TRUE ~ 0
  )
)
```

This code creates new IDs for individuals with one known parent and a missing other. It checks if either `momID` or `dadID` is missing, and if so, it assigns a new ID based on the row number. This allows us to visualize the pedigree even when some parental information is incomplete.



## Visualize the Pedigree

We can now visualize the repaired pedigree using the `plotPedigree()` function. This function generates a plot of the pedigree, with individuals colored based on their affected status. In this case, we highlight Jon and Daenerys as "affected" individuals. Otherwise they would be difficult to distinguish from the rest of the pedigree.

```{r, message=FALSE, warning=FALSE}
plotPedigree(df_repaired, affected = df_repaired$affected, verbose = FALSE)

ggPedigree(df_repaired,  status_col = "affected",  personID_col = "ID",  code_male = "M", 
           config = list(unaffected =  0,affected = 1,
                         ped_width=15))

```