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Collect Identity-by-Descent (IBD) segments (EXPERIMENTAL)

Source: R/tree-sequences.R
ts_ibd.Rd

This function iterates over a tree sequence and returns IBD tracts between pairs of individuals or nodes

Usage

ts_ibd(
  ts,
  coordinates = FALSE,
  within = NULL,
  between = NULL,
  squash = FALSE,
  minimum_length = NULL,
  maximum_time = NULL,
  sf = TRUE
)

Arguments

ts

Tree sequence object of the class slendr_ts

coordinates

Should coordinates of all detected IBD tracts be reported? If FALSE (the default), only the total length of shared IBD segments and their numbers are reported. If TRUE, coordinates of each segment will be returned (but note that this can have a massive impact on memory usage). See details for more information.

within

A character vector with individual names or an integer vector with node IDs indicating a set of nodes within which to look for IBD segments.

between

A list of lists of character vectors with individual names or integer vectors with node IDs, indicating a set of nodes between which to look for shared IBD segments.

squash

Should adjacent IBD segments for pairs of nodes be squashed if they only differ by their 'genealogical paths' but not by their MRCA? Default is FALSE. For more context, see https://github.com/tskit-dev/tskit/issues/2459. This option is EXPERIMENTAL!

minimum_length

Minimum length of an IBD segment to return in results. This is useful for reducing the total amount of IBD returned (but see Details).

maximum_time

Oldest MRCA of a node to be considered as an IBD ancestor to return that IBD segment in results. This is useful for reducing the total amount of IBD returned.

sf

If IBD segments in a spatial tree sequence are being analyzed, should the returned table be a spatial sf object? Default is TRUE.

Value

A data frame with IBD results (either coordinates of each IBD segment shared by a pair of nodes, or summary statistics about the total IBD sharing for that pair)

Details

This function is considered experimental. For full control over IBD segment detection in tree-sequence data, users can (and perhaps, for the time being, should) rely on the tskit method ibd_segments (see https://tskit.dev/tskit/docs/stable/python-api.html#tskit.TreeSequence.ibd_segments).

Iternally, this function leverages the tskit TreeSequence method ibd_segments. However, note that the ts_ibd function always returns a data frame of IBD tracts, it does not provide an option to iterate over individual IBD segments as shown in the official tskit documentation at https://tskit.dev/tskit/docs/stable/ibd.html. In general, R handles heavy iteration poorly, and this function does not attempt to serve as a full wrapper to ibd_segments.

Unfortunately, the distinction between "squashed IBD" (what many would consider to be the expected definition of IBD) and tskit’s IBD which is defined via distinct genealogical paths (see https://github.com/tskit-dev/tskit/issues/2459 for a discussion of the topic), makes the meaning of the filtering parameter of the ibd_segments() method of tskit minimum_length somewhat unintuitive. As of this moment, this function argument filters on IBD segments on the tskit level, not the level of the squashed IBD segments!

Examples

init_env()
#> The interface to all required Python modules has been activated.

# load an example model with an already simulated tree sequence
slendr_ts <- system.file("extdata/models/introgression_slim.trees", package = "slendr")
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))

# load the tree-sequence object from disk
ts <- ts_read(slendr_ts, model)

# find IBD segments between specified Neanderthals and Europeans
ts_ibd(
  ts,
  coordinates = TRUE,
  between = list(c("NEA_1", "NEA_2"), c("EUR_1", "EUR_2")),
  minimum_length = 40000
)
#> # A tibble: 32 × 13
#>    node1 node2 length  mrca node1_time node2_time tmrca   left  right name1
#>    <int> <int>  <dbl> <dbl>      <dbl>      <dbl> <dbl>  <dbl>  <dbl> <chr>
#>  1     0    16  87198    82      70000          0 20001  65949 153147 NEA_1
#>  2     0    16  85735    82      70000          0 20001 221193 306928 NEA_1
#>  3     0    17  68822    82      70000          0 20001  65949 134771 NEA_1
#>  4     0    17  53084    82      70000          0 20001 227724 280808 NEA_1
#>  5     0    18  87198    82      70000          0 20001  65949 153147 NEA_1
#>  6     0    18  85735    82      70000          0 20001 221193 306928 NEA_1
#>  7     0    19  81722    82      70000          0 20001  71425 153147 NEA_1
#>  8     0    19  58054    79      70000          0  2340 227185 285239 NEA_1
#>  9     1    16  87198    82      70000          0 20001  65949 153147 NEA_1
#> 10     1    16  85735    82      70000          0 20001 221193 306928 NEA_1
#> # ℹ 22 more rows
#> # ℹ 3 more variables: name2 <chr>, pop1 <fct>, pop2 <fct>