Inference overview#

The process of inferring a tree sequence from variation data is split into a number of different steps. We first review the requirements for input data and discuss how such data is imported into tsinfer’s sample data format. We then outline the three basic steps for inference: generating ancestors, matching ancestors and matching samples.

Data requirements#

Input haplotype data for tskit must satisfy the following requirements:

  • Data must be phased.

  • For each site used for inference we must know the ancestral state. Note that this is not necessarily the same as the REF column from VCF, and ancestral states must be computed using some other method. Sites with unknown ancestral states can be specified, but are not used for inference; instead, the ancestral state is imputed.

  • Only biallelic sites can be used for inference.

  • Missing data can be included in the haplotypes using the value tskit.MISSING_DATA (-1). The inferred tree sequence will have values imputed for the haplotypes at these sites.

Data model#

The data model for tsinfer is tightly integrated with tskit’s data model and uses the same concepts throughout. The intermediate file formats and APIs described here provide a bridge between this model and existing data sources. For convenience, we provide a brief description of concepts needed for importing data into tsinfer here. Please see the tskit documentation for more detailed information.


In tsinfer an individual defines one of the subjects for which we have genotype data. Individuals may have arbitrary ploidy levels (i.e. haploid, diploid, tetraploid, etc.). Different individuals within a dataset can have different ploidy levels. You can add an individual, and specify arbitrary Metadata for it, using the SampleData.add_individual() method; if you do not do so tsinfer will create a default set of haploid individuals, one for each sampled genome.

The tree sequence that you infer from your data will contain sample nodes (i.e. genomes) that are linked to these individuals: the tskit documentation provides more detail on the distinction between individuals and the genomes they contain.


Each individual is composed of one or more sample genomes, depending on their ploidy. If an individual is diploid they have two samples, one each for the maternal and paternal chromosome copies. More generally, a node refers to a maternal or paternal chromosome that is either a sample, or an ancestor of our samples.

When we add an individual with ploidy k using SampleData.add_individual(), k new samples are also added which refer to this new individual. When adding genotype information using the SampleData.add_site() method, the user must ensure that the observed genotypes are in this same order.


A population is some grouping of individuals. Populations principally exist to allow us define metadata for groups of individuals (although technically, population IDs are associated with samples). Populations are added using the SampleData.add_population() method.


Metadata can be associated with populations and individuals in tsinfer, which results in this information being available in the final tree sequence. Metadata allows us to incorporate extra information that is not part of the basic data model; for example, we can record the upstream ID of a given individual and their family relationships with other individuals.

In tsinfer, metadata can be stored by providing a JSON encodable mapping. This information is then stored as JSON, and embedded in the final tree sequence object and can be recovered using the tskit APIs.

Import samples data#

In tsinfer we make several passes through the input sample haplotypes in order to construct ancestors and to find copying paths for samples. To do this efficiently we store the data using the zarr library, which provides very fast access to large arrays of numerical data compressed using cutting-edge compression methods. As a result, we can store the input sample haplotypes and related metadata in a fraction of the size of a compressed VCF and can process it efficiently (although still not as efficently as it is possible to analyse an equivalent tree sequence)

Rather than require the user to understand the internal structure of this file format, we provide a simple Python API to allow the user to efficiently construct it from their own data. An example of how to use this API is given in the Tutorial.

We do not provide an automatic means of importing data from VCF (or any other format) intentionally, as we believe that this would be extremely difficult to do. As there is no universally accepted way of encoding ancestral state information in VCF, in practise the user would most often have to write a new VCF file with ancestral state and metadata information in a specific form that we would require. Thus, it is more efficient to skip this intermediate step and to directly produce a format that is both compact and very efficient to process.

Generate ancestors#

The first step in a tsinfer inference process is to generate a large number of potential ancestors and to store these in an ancestors file. The ancestors file conventionally ends with .ancestors.

The ancestor generation algorithm is described in the Methods section of the original tsinfer paper.


Describe the ancestor generation algorithm in more detail here

Matching ancestors & samples#

After we have generated a set of potential ancestors and stored them in an ancestors file, we then run two matching steps. First we match the ancestors against each other to generate an “ancestors tree sequence”, then we match the samples against this ancestors tree sequence to generate the final result.

In both matching stages, we can set parameters that adjust the behaviour of the matching algorithm, in particular the path_compression setting, and the recombination_rate and mismatch_ratio parameters. The latter two only need to be specified if you wish to allow multiple mutations to occur at a single site (i.e. breaking the infinite sites model of mutation). Note, however, that multiple mutations are useful not only to show true recurrent or back mutations in the evolutionary history of a site, but also to represent errors in sequencing etc. which cause the distribution of variation to fit poorly to the marginal tree at that site. Hence, if there is error in your dataset, you may wish to experiment with these settings to obtain optimal results.

Recombination and mismatch#

The recombination_rate parameter is either a floating point value giving a single rate (\(\rho\)) per unit length of genome, used to calculate the genetic distance between adjacent sites, or an msprime.RateMap object (whose .get_cumulative_mass method provides the genetic distances instead). The genetic distances are then used to derive an array of probabilities of recombination between adjacent sites, (\(r\)) used when assessing the relative likelihood that a mismatch (and hence an extra mutation) may be responsible for some patterns of variation at a site.

The mismatch_ratio parameter is only relevant if a recombination rate has been provided. It is used to adjust the balance of recombination to multiple mutations at a site. More specifically, a single probability of mismatch is used for all sites, which is calculated from the median genetic distance between inference sites, such that for conventional (small) distances, the probability of a mismatch is approximately equal to the probability of recombination multiplied by the mismatch_ratio. In other words, a mismatch ratio of 2 makes a recurrent mutation two times more likely than a recombination event to explain why an otherwise closely matching ancestral haplotype does not match at a particular site. Setting a high mismatch_ratio therefore results in tree sequences with more recurrent mutations and fewer recombinations (and edges). Setting a low value results in tree sequences with more recombination events and edges, and fewer mutations. In the limit, as the mismatch_ratio tends to zero, only one mutation will be inferred per variable site. This is the default behaviour if no recombination_rate is given or if there is only one inference site. Alternatively, if recombination_rate is set, mismatch_ratio defaults to 1, which has been shown to give reasonable results in simulated inference of human-like data with error. As a rough guide, such simulations recommend mismatch ratios between 1e-3 and 1e3.

Path compression#

The path_compression setting is used to further minimise recombination events by looking for shared recombination breakpoints. A shared breakpoint exists if a set of children share a breakpoint in the same position, and they also have identical parents to the left of the breakpoint and identical parents to the right of the breakpoint. Rather than supposing that these children experienced multiple identical recombination events in parallel, we can reduce the number of ancestral recombination events by postulating a “synthetic ancestor” with this breakpoint, existing at a slightly older point in time, from whom all the children are descended at this genomic position. We call the algorithm used to implement this addition to the ancestral copying paths, “path compression”.

Match ancestors#

Matching ancestors is dependent on the time allocated to each ancestor; an ancestor can only copy from any older ancestor. For each ancestor, we find the most likely path through older ancestors: that is the path that maximises the product of the probabilities of recombination and mismatch over all sites.


Schematic of the ancestors copying process.

The copying path for each ancestor then describes its ancestry at every point in the sequence: from a genealogical perspective, we know its parent node. This information is encoded precisely as an edge in a tree sequence. Thus, we refer to the output of this step as the “ancestors tree sequence”, which is conventionally stored in a file ending with .ancestors.trees.

Match samples#

The final phase of a tsinfer inference consists of a number steps:

  1. The first (and usually most time-consuming) is to find copying paths for our sample haplotypes through the ancestors. Each copying path corresponds to a set of tree sequence edges in precisely the same way as for ancestors, and the path compression algorithm can be equally applied here.

  2. As we only use a subset of the available sites for inference (excluding by default any sites that are fixed, singletons, or where the ancestral state is unknown) we then place mutations on the inferred trees in order to represent the information at these sites. This is done using tskit.Tree.map_mutations().

  3. Post-process the final tree sequence to a. modify the oldest ancestors in the tree sequence, in particular, by taking the single “grand MRCA” containing entirely ancestral states that tsinfer uses to provide a baseline ancestor for the matching algorithm, and splitting it into separate ancestral genomic fragments. b. remove topology at positions before the first site and one genomic unit after the last site, to avoid extrapolating genealogical patterns into regions for which we have no data. c. remove ancestral paths that do not lead to any of the samples by simplifying the final output. When simplifying, we keep non-branching (“unary”) nodes, as they indicate ancestors which we have actively inferred, and for technical reasons keeping unary ancestors can also lead to better compression. Note that this means that not every internal node in the inferred tree sequence will correspond to a coalescent event.


1. Describe path compression here and above in the ancestors
2. Describe the structure of the output tree sequences; how the
   nodes are mapped, what the time values mean, etc.