Compare Two Pedigrees

Compare an inferred pedigree (Ped2) to a previous or simulated pedigree (Ped1), including comparison of sibship clusters and sibship grandparents.

Usage

PedCompare(
  Ped1 = NULL,
  Ped2 = NULL,
  DumPrefix = c("F0", "M0"),
  SNPd = NULL,
  Symmetrical = TRUE,
  minSibSize = "1sib1GP",
  Plot = TRUE
)

Arguments

Ped1

first (e.g. original) pedigree, dataframe with columns id-dam-sire; only the first 3 columns will be used.

Ped2

second pedigree, e.g. newly inferred SeqOUT$Pedigree or SeqOUT$PedigreePar, with columns id-dam-sire.

DumPrefix

character vector with the prefixes identifying dummy individuals in Ped2. Use 'F0' ('M0') to avoid matching to regular individuals with IDs starting with 'F' ('M'), provided Ped2 has fewer than 999 dummy females (males).

SNPd

character vector with IDs of genotyped individuals. If NULL, defaults to the IDs occurring in both Ped1 and Ped2 and not starting with any of the prefixes in DumPrefix.

Symmetrical

when determining the category of individuals (Genotyped/Dummy/X), use the 'highest' category across the two pedigrees (TRUE, default) or only consider Ped1 (Symmetrical = FALSE).

minSibSize

minimum requirements to be considered 'dummifiable', passed to getAssignCat:

'1sib' : sibship of size 1, with or without grandparents. The latter aren't really a sibship, but can be useful in some situations.
'1sib1GP': sibship of size 1 with at least 1 grandparent (default)
'2sib': at least 2 siblings, with or without grandparents (default prior to version 2.4)

Plot

show square Venn diagrams of counts?

Value

A list with

Counts: A 7 x 5 x 2 named numeric array with the number of matches and mismatches, see below
Counts.detail: a large numeric array with number of matches and mismatches, with more detail for all possible combination of categories
MergedPed: A dataframe with side-by-side comparison of the two pedigrees
ConsensusPed: A consensus pedigree, with Pedigree 2 taking priority over Pedigree 1
DummyMatch: Dataframe with all dummy IDs in Pedigree 2 (id.2), and the best-matching individual in Pedigree 1 (id.1). Also includes the class of the dam & sire, as well as counts of offspring per outcome class (off.Match, off.Mismatch, etc.)
Mismatch: A subset of MergedPed with mismatches between Ped1 and Ped2, as defined below
Ped1only: as Mismatches, with parents in Ped1 that were not assigned in Ped2
Ped2only: as Mismatches, with parents in Ped2 that were missing in Ped1

'MergedPed', 'Mismatch', 'Ped1only' and 'Ped2only' provide the following columns:

id: All ids in both Pedigree 1 and 2. For dummy individuals, this is the id in pedigree 2
dam.1, sire.1: parents in Pedigree 1
dam.2, sire.2: parents in Pedigree 2
id.r, dam.r, sire.r: The real id of dummy individuals or parents in Pedigree 2, i.e. the best-matching non-genotyped individual in Pedigree 1, or "nomatch". If a sibship in Pedigree 1 is divided over 2 sibships in Pedigree 2, the smaller one will be denoted as "nomatch"
id.dam.cat, id.sire.cat: the category of the individual (first letter) and highest category of the dam (sire) in Pedigree 1 or 2: G=Genotyped, D=(potential) dummy, X=none. Individual, one-letter categories are generated by getAssignCat. Using the 'best' category from both pedigrees makes comparison between two inferred pedigrees symmetrical and more intuitive.
dam.class, sire.class: classification of dam and sire: Match, Mismatch, P1only, P2only, or '_' when no parent is assigned in either pedigree

The first dimension of Counts denotes the following categories:

GG: Genotyped individual, assigned a genotyped parent in either pedigree
GD: Genotyped individual, assigned a dummy parent, or at least 1 genotyped sibling or a genotyped grandparent in Pedigree 1)
GT: Genotyped individual, total
DG: Dummy individual, assigned a genotyped parent (i.e., grandparent of the sibship in Pedigree 2)
DD: Dummy individual, assigned a dummy parent (i.e., avuncular relationship between sibships in Pedigree 2)
DT: Dummy total
TT: Total total, includes all genotyped individuals, plus non-genotyped individuals in Pedigree 1, plus non-replaced dummy individuals (see below) in Pedigree 2

The second dimension of Counts gives the outcomes:

Total: The total number of individuals with a parent assigned in either or both pedigrees
Match: The same parent is assigned in both pedigrees (non-missing). For dummy parents, it is considered a match if the inferred sibship which contains the most offspring of a non-genotyped parent, consists for more than half of this individual's offspring.
Mismatch: Different parents assigned in the two pedigrees. When a sibship according to Pedigree 1 is split over two sibships in Pedigree 2, the smaller fraction is included in the count here.
P1only: Parent in Pedigree 1 but not 2; includes non-assignable parents (e.g. not genotyped and no genotyped offspring).
P2only: Parent in Pedigree 2 but not 1.

The third dimension Counts separates between maternal and paternal assignments, where e.g. paternal 'DT' is the assignment of fathers to both maternal and paternal sibships (i.e., to dummies of both sexes).

In 'ConsensusPed', the priority used is parent.r (if not "nomatch") > parent.2 > parent.1. The columns 'id.cat', dam.cat' and 'sire.cat' have two additional levels compared to 'MergedPed':

G: Genotyped
D: Dummy individual (in Pedigree 2)
R: Dummy individual in pedigree 2 replaced by best matching non-genotyped individual in pedigree 1
U: Ungenotyped, Unconfirmed (parent in Pedigree 1, with no dummy match in Pedigree 2)
X: No parent in either pedigree

Details

The comparison is divided into different classes of `assignable' parents (getAssignCat). This includes cases where the focal individual and parent according to Ped1 are both Genotyped (G-G), as well as cases where the non-genotyped parent according to Ped1 can be lined up with a sibship Dummy parent in Ped2 (G-D), or where the non-genotyped focal individual in Ped1 can be matched to a dummy individual in Ped2 (D-G and D-D). If SNPd is NULL (the default), and DumPrefix is set to NULL, the intersect between the IDs in Pedigrees 1 and 2 is taken as the vector of genotyped individuals.

Assignable

Note that 'assignable' may be overly optimistic. Some parents from Ped1 indicated as assignable may never be assigned by sequoia, for example parent-offspring pairs where it cannot be determined which is the older of the two, or grandparents that are indistinguishable from full avuncular (i.e. genetics inconclusive because the candidate has no parent assigned, and ageprior inconclusive).

Dummifiable

Considered as potential dummy individuals are all non-genotyped individuals in Pedigree 1 who have, according to either pedigree, at least 2 genotyped offspring, or at least one genotyped offspring and a genotyped parent.

Mismatches

Perhaps unexpectedly, cases where all siblings are correct but a dummy parent rather than the genotyped Ped1-parent are assigned, are classified as a mismatch (for each of the siblings). These are typically due to a too low assumed genotyping error rate, a wrong parental birth year, or some other issue that requires user inspection. To identify these cases, ComparePairs may be of help.

Genotyped 'mystery samples'

If Pedigree 2 includes samples for which the ID is unknown, the behaviour of PedCompare depends on whether the temporary IDs for these samples are included in SNPd. If they are included, matching (actual) IDs in Pedigree 1 will be flagged as mismatches (because the IDs differ). If they are not included in SNPd, or SNPd is not explicitly provided, matches are accepted, as the situation is indistinguishable from comparing dummy parents across pedigrees.

This is of course all conditional on relatives of the mystery sample being assigned in Pedigree 2.

Author

Jisca Huisman, jisca.huisman@gmail.com

Examples

compare <- PedCompare(Ped_griffin, SeqOUT_griffin$Pedigree)

compare$Counts["TT",,]  # totals only; 45 dams & 47 sires non-assigned
#>           parent
#> class      dam sire
#>   Total    167  163
#>   Match    125  122
#>   Mismatch   0    0
#>   P1only    42   41
#>   P2only     0    0
compare$Counts[,,"dam"]  # dams only
#>     class
#> cat  Total Match Mismatch P1only P2only
#>   GG    65    65        0      0      0
#>   GD    56    41        0     15      0
#>   GT   125   106        0     19      0
#>   DG    23    14        0      9      0
#>   DD    12     5        0      7      0
#>   DT    39    19        0     20      0
#>   TT   167   125        0     42      0

# inspect non-assigned in Ped2, id genotyped, dam might-be-dummy
PedM <- compare$MergedPed  # for brevity
PedM[PedM$id.dam.cat=='GD' & PedM$dam.class=='P1only',]
#>              id       dam.1      sire.1 dam.2      sire.2        id.r dam.r
#> 45  i028_2002_M i005_2001_F        <NA>  <NA>        <NA> i028_2002_M  <NA>
#> 48  i033_2002_F i013_2001_F        <NA>  <NA>        <NA> i033_2002_F  <NA>
#> 49  i034_2002_F i011_2001_F        <NA>  <NA>        <NA> i034_2002_F  <NA>
#> 56  i044_2003_F i011_2001_F i019_2001_M  <NA>        <NA> i044_2003_F  <NA>
#> 64  i055_2003_M i012_2001_F i040_2002_M  <NA> i040_2002_M i055_2003_M  <NA>
#> 66  i059_2003_M i013_2001_F i036_2002_M  <NA> i036_2002_M i059_2003_M  <NA>
#> 68  i062_2004_M i012_2001_F i058_2003_M  <NA>        <NA> i062_2004_M  <NA>
#> 71  i067_2004_F i005_2001_F i029_2002_M  <NA> i029_2002_M i067_2004_F  <NA>
#> 75  i072_2004_M i048_2003_F i010_2001_M  <NA>        <NA> i072_2004_M  <NA>
#> 76  i073_2004_M i060_2003_F i003_2001_M  <NA>        <NA> i073_2004_M  <NA>
#> 105 i113_2006_M i087_2005_F i053_2003_M  <NA>        <NA> i113_2006_M  <NA>
#> 106 i114_2006_M i060_2003_F i090_2005_M  <NA>        <NA> i114_2006_M  <NA>
#> 115 i127_2007_M i087_2005_F i085_2005_M  <NA>        <NA> i127_2007_M  <NA>
#> 119 i132_2007_F i093_2005_F i117_2006_M  <NA>        <NA> i132_2007_F  <NA>
#> 135 i157_2008_F i125_2007_F i082_2005_M  <NA>        <NA> i157_2008_F  <NA>
#>     sire.r id.dam.cat id.sire.cat dam.class sire.class
#> 45    <NA>         GD          GX    P1only          _
#> 48    <NA>         GD          GX    P1only          _
#> 49    <NA>         GD          GX    P1only          _
#> 56    <NA>         GD          GD    P1only     P1only
#> 64    <NA>         GD          GG    P1only      Match
#> 66    <NA>         GD          GG    P1only      Match
#> 68    <NA>         GD          GD    P1only     P1only
#> 71    <NA>         GD          GG    P1only      Match
#> 75    <NA>         GD          GD    P1only     P1only
#> 76    <NA>         GD          GX    P1only     P1only
#> 105   <NA>         GD          GD    P1only     P1only
#> 106   <NA>         GD          GD    P1only     P1only
#> 115   <NA>         GD          GD    P1only     P1only
#> 119   <NA>         GD          GD    P1only     P1only
#> 135   <NA>         GD          GD    P1only     P1only
# zoom in on specific dam
PedM[which(PedM$dam.1=="i011_2001_F"), ]
#>             id       dam.1      sire.1 dam.2 sire.2        id.r dam.r sire.r
#> 49 i034_2002_F i011_2001_F        <NA>  <NA>   <NA> i034_2002_F  <NA>   <NA>
#> 56 i044_2003_F i011_2001_F i019_2001_M  <NA>   <NA> i044_2003_F  <NA>   <NA>
#>    id.dam.cat id.sire.cat dam.class sire.class
#> 49         GD          GX    P1only          _
#> 56         GD          GD    P1only     P1only
# no sire for 'i034_2002_F' -> impossible to tell if half-sibs or avuncular

# overview of all non-genotyped -- dummy matches
head(compare$DummyMatch)
#>    id.2        id.1 Sex dam.class sire.class off.Match off.Mismatch off.P1only
#> 1 F0001 i115_2006_F   1    P1only      Match         4            0          1
#> 2 F0002 i140_2007_F   1     Match      Match         2            0          0
#> 3 F0003 i101_2006_F   1     Match      Match        11            0          3
#> 4 F0004 i032_2002_F   1         _     P1only         3            0          0
#> 5 F0005 i037_2002_F   1         _          _         2            0          0
#> 6 F0006 i155_2008_F   1     Match      Match         3            0          0
#>   off.P2only
#> 1          0
#> 2          0
#> 3          0
#> 4          0
#> 5          0
#> 6          0

# success of paternity assignment, if genotyped mother correctly assigned
dimnames(compare$Counts.detail)
#> $id.cat
#> [1] "G" "D" "X"
#> 
#> $dam.cat
#> [1] "G" "D" "X"
#> 
#> $sire.cat
#> [1] "G" "D" "X"
#> 
#> $dam.class
#> [1] "Match"    "Mismatch" "P1only"   "P2only"   "_"       
#> 
#> $sire.class
#> [1] "Match"    "Mismatch" "P1only"   "P2only"   "_"       
#> 
compare$Counts.detail["G","G",,"Match",]
#>         sire.class
#> sire.cat Match Mismatch P1only P2only  _
#>        G    41        0      0      0  0
#>        D    14        0      7      0  0
#>        X     0        0      1      0  2

# default before version 3.5: minSibSize = '2sib'
compare_2s <- PedCompare(Ped_griffin, SeqOUT_griffin$Pedigree,
                         minSibSize = '2sib')

compare_2s$Counts[,,"dam"]  # note decrease in Total 'dummies
#>     class
#> cat  Total Match Mismatch P1only P2only
#>   GG    65    65        0      0      0
#>   GD    51    38        0     13      0
#>   GT   125   106        0     19      0
#>   DG     9     8        0      1      0
#>   DD     7     4        0      3      0
#>   DT    17    12        0      5      0
#>   TT   167   125        0     42      0
with(compare_2s$MergedPed, table(id.dam.cat, dam.class))
#>           dam.class
#> id.dam.cat Match P1only  _
#>         DD     4      3  0
#>         DG     8      1  0
#>         DX     0      1 10
#>         GD    38     13  0
#>         GG    65      0  0
#>         GX     3      6 17
#>         XD     0      4  0
#>         XG     6      9  0
#>         XX     1      5  6
# some with id.cat = 'X' or dam.cat='X' are nonetheless dam.class='Match'