The relatedness of two individuals characterizes their biological relationship. For example, two individuals might be siblings or parent-and-child. All notions of relatedness implemented in Hail are rooted in the idea of alleles “inherited identically by descent”. Two alleles in two distinct individuals are inherited identically by descent if both alleles were inherited by the same “recent,” common ancestor. The term “recent” distinguishes alleles shared IBD from family members from alleles shared IBD from “distant” ancestors. Distant ancestors are thought of contributing to population structure rather than relatedness.

Relatedness is usually quantified by two quantities: kinship coefficient (\(\phi\) or PI_HAT) and probability-of-identity-by-descent-zero (\(\pi_0\) or Z0). The kinship coefficient is the probability that any two alleles selected randomly from the same locus are identical by descent. Twice the kinship coefficient is the coefficient of relationship which is the percent of genetic material shared identically by descent. Probability-of-identity-by-descent-zero is the probability that none of the alleles at a randomly chosen locus were inherited identically by descent.

Hail provides three methods for the inference of relatedness: PLINK-style identity by descent 1, KING 2, and PC-Relate 3.

  • identity_by_descent() is appropriate for datasets containing one homogeneous population.

  • king() is appropriate for datasets containing multiple homogeneous populations and no admixture. It is also used to prune close relatives before using pc_relate().

  • pc_relate() is appropriate for datasets containing multiple homogeneous populations and admixture.

identity_by_descent(dataset[, maf, bounded, …])

Compute matrix of identity-by-descent estimates.

king(call_expr, *[, block_size])

Compute relatedness estimates between individuals using a KING variant.

pc_relate(call_expr, min_individual_maf, *)

Compute relatedness estimates between individuals using a variant of the PC-Relate method.

hail.methods.identity_by_descent(dataset, maf=None, bounded=True, min=None, max=None)[source]

Compute matrix of identity-by-descent estimates.


Requires the column key to be one field of type tstr


Requires the dataset to have a compound row key:


Requires the dataset to contain no multiallelic variants. Use split_multi() or split_multi_hts() to split multiallelic sites, or MatrixTable.filter_rows() to remove them.


To calculate a full IBD matrix, using minor allele frequencies computed from the dataset itself:

>>> hl.identity_by_descent(dataset)

To calculate an IBD matrix containing only pairs of samples with PI_HAT in \([0.2, 0.9]\), using minor allele frequencies stored in the row field panel_maf:

>>> hl.identity_by_descent(dataset, maf=dataset['panel_maf'], min=0.2, max=0.9)


The dataset must have a column field named s which is a StringExpression and which uniquely identifies a column.

The implementation is based on the IBD algorithm described in the PLINK paper.

identity_by_descent() requires the dataset to be biallelic and does not perform LD pruning. Linkage disequilibrium may bias the result so consider filtering variants first.

The resulting Table entries have the type: { i: String, j: String, ibd: { Z0: Double, Z1: Double, Z2: Double, PI_HAT: Double }, ibs0: Long, ibs1: Long, ibs2: Long }. The key list is: *i: String, j: String*.

Conceptually, the output is a symmetric, sample-by-sample matrix. The output table has the following form

i               j       ibd.Z0  ibd.Z1  ibd.Z2  ibd.PI_HAT ibs0 ibs1    ibs2
sample1 sample2 1.0000  0.0000  0.0000  0.0000 ...
sample1 sample3 1.0000  0.0000  0.0000  0.0000 ...
sample1 sample4 0.6807  0.0000  0.3193  0.3193 ...
sample1 sample5 0.1966  0.0000  0.8034  0.8034 ...
  • dataset (MatrixTable) – Variant-keyed and sample-keyed MatrixTable containing genotype information.

  • maf (Float64Expression, optional) – Row-indexed expression for the minor allele frequency.

  • bounded (bool) – Forces the estimations for Z0`, Z1, Z2, and PI_HAT to take on biologically meaningful values (in the range [0,1]).

  • min (float or None) – Sample pairs with a PI_HAT below this value will not be included in the output. Must be in \([0,1]\).

  • max (float or None) – Sample pairs with a PI_HAT above this value will not be included in the output. Must be in \([0,1]\).



hail.methods.king(call_expr, *, block_size=None)[source]

Compute relatedness estimates between individuals using a KING variant.


Requires the dataset to contain only diploid genotype calls.


Estimate the kinship coefficient for every pair of samples.

>>> kinship = hl.king(dataset.GT)


The following presentation summarizes the methods section of Manichaikul, et. al., but adopts a more consistent notation for matrices.


  • \(i\) and \(j\) be two individuals in the dataset

  • \(N^{Aa}_{i}\) be the number of heterozygote genotypes for individual \(i\).

  • \(N^{Aa,Aa}_{i,j}\) be the number of variants at which a pair of individuals both have heterozygote genotypes.

  • \(N^{AA,aa}_{i,j}\) be the number of variants at which a pair of individuals have opposing homozygote genotypes.

  • \(S_{i,j}\) be the set of single-nucleotide variants for which both individuals \(i\) and \(j\) have a non-missing genotype.

  • \(X_{i,s}\) be the genotype score matrix. Each entry corresponds to the genotype of individual \(i\) at variant \(s\). Homozygous-reference genotypes are represented as 0, heterozygous genotypes are represented as 1, and homozygous-alternate genotypes are represented as 2. \(X_{i,s}\) is calculated by invoking n_alt_alleles() on the call_expr.

The three counts above, \(N^{Aa}\), \(N^{Aa,Aa}\), and \(N^{AA,aa}\), exclude variants where one or both individuals have missing genotypes.

In terms of the symbols above, we can define \(d\), the genetic distance between two samples. We can interpret \(d\) as an unnormalized measurement of the genetic material not shared identically-by-descent:

\[d_{i,j} = \sum_{s \in S_{i,j}}\left(X_{i,s} - X_{j,s}\right)^2\]

In the supplement to Manichaikul, et. al, the authors show how to re-express the genetic distance above in terms of the three counts of hetero- and homozygosity by considering the nine possible configurations of a pair of genotypes:

\((X_{i,s} - X_{j,s})^2\)
















which leads to this expression for genetic distance:

\[d_{i,j} = 4 N^{AA,aa}_{i,j} + N^{Aa}_{i} + N^{Aa}_{j} - 2 N^{Aa,Aa}_{i,j}\]

The first term, \(4 N^{AA,aa}_{i,j}\), accounts for all pairs of genotypes with opposing homozygous genotypes. The second and third terms account for the four cases of one heteroyzgous genotype and one non-heterozygous genotype. Unfortunately, the second and third term also contribute to the case of a pair of heteroyzgous genotypes. We offset this with the fourth and final term.

The genetic distance, \(d_{i,j}\), ranges between zero and four times the number of variants in the dataset. In the supplement to Manichaikul, et. al, the authors demonstrate that the kinship coefficient, \(\phi_{i,j}\), between two individuals from the same population is related to the expected genetic distance at any one variant by way of the allele frequency:

\[\mathop{\mathbb{E}}_{i,j} (X_{i,s} - X_{j,s})^2 = 4 p_s (1 - p_s) (1 - 2\phi_{i,j})\]

This identity reveals that the quotient of the expected genetic distance and the four-trial binomial variance in the allele frequency represents, roughly, the “fraction of genetic material not shared identically-by-descent”:

\[1 - 2 \phi_{i,j} = \frac{4 N^{AA,aa}_{i,j} + N^{Aa}_{i} + N^{Aa}_{j} - 2 N^{Aa,Aa}_{i,j}} {\sum_{s \in S_{i,j}} 4 p_s (1 - p_s)}\]

Note that the “coefficient of relationship”, (by definition: the fraction of genetic material shared identically-by-descent) is equal to twice the kinship coefficient: \(\phi_{i,j} = 2 r_{i,j}\).

Manichaikul, et. al, assuming one homogeneous population, demonstrate in Section 2.3 that the sum of the variance of the allele frequencies,

\[\sum_{s \in S_{i, j}} 2 p_s (1 - p_s)\]

is, in expectation, proportional to the count of heterozygous genotypes of either individual:


For individuals from distinct populations, the authors propose replacing the count of heteroyzgous genotypes with the average of the two individuals:

\[\frac{N^{Aa}_{i} + N^{Aa}_{j}}{2}\]

Using the aforementioned equality, we define a normalized genetic distance, \(\widetilde{d_{i,j}}\), for a pair of individuals from distinct populations:

\[\begin{aligned} \widetilde{d_{i,j}} &= \frac{4 N^{AA,aa}_{i,j} + N^{Aa}_{i} + N^{Aa}_{j} - 2 N^{Aa,Aa}_{i,j}} {N^{Aa}_{i} + N^{Aa}_{j}} \\ &= 1 + \frac{4 N^{AA,aa}_{i,j} - 2 N^{Aa,Aa}_{i,j}} {N^{Aa}_{i} + N^{Aa}_{j}} \end{aligned}\]

As mentioned before, the complement of the normalized genetic distance is the coefficient of relationship which is also equal to twice the kinship coefficient:

\[2 \phi_{i,j} = r_{i,j} = 1 - \widetilde{d_{i,j}}\]

We now present the KING “within-family” estimator of the kinship coefficient as one-half of the coefficient of relationship:

\[\begin{aligned} \widehat{\phi_{i,j}^{\mathrm{within}}} &= \frac{1}{2} r_{i,j} \\ &= \frac{1}{2} \left( 1 - \widetilde{d_{i,j}} \right) \\ &= \frac{N^{Aa,Aa}_{i,j} - 2 N^{AA,aa}_{i,j}} {N^{Aa}_{i} + N^{Aa}_{j}} \end{aligned}\]

This “within-family” estimator over-estimates the kinship coefficient under certain circumstances detailed in Section 2.3 of Manichaikul, et. al. The authors recommend an alternative estimator when individuals are known to be from different families. The estimator replaces the average count of heteroyzgous genotypes with the minimum count of heterozygous genotypes:

\[\frac{N^{Aa}_{i} + N^{Aa}_{j}}{2} \rightsquigarrow \mathrm{min}(N^{Aa}_{i}, N^{Aa}_{j})\]

This transforms the “within-family” estimator into the “between-family” estimator, defined by Equation 11 of Manichaikul, et. al.:

\[\begin{aligned} \widetilde{d_{i,j}^{\mathrm{between}}} &= \frac{4 N^{AA,aa}_{i,j} + N^{Aa}_{i} + N^{Aa}_{j} - 2 N^{Aa,Aa}_{i,j}} {2 \mathrm{min}(N^{Aa}_{i}, N^{Aa}_{j})} \\ \widehat{\phi_{i,j}^{\mathrm{between}}} &= \frac{1}{2} + \frac{2 N^{Aa,Aa}_{i,j} - 4 N^{AA,aa}_{i,j} - N^{Aa}_{i} - N^{Aa}_{j}} {4 \cdot \mathrm{min}(N^{Aa}_{i}, N^{Aa}_{j})} \end{aligned}\]

This function, king(), only implements the “between-family” estimator, \(\widehat{\phi_{i,j}^{\mathrm{between}}}\).


MatrixTable – A MatrixTable whose rows and columns are keys are taken from call-expr’s column keys. It has one entry field, phi.

hail.methods.pc_relate(call_expr, min_individual_maf, *, k=None, scores_expr=None, min_kinship=None, statistics='all', block_size=None)[source]

Compute relatedness estimates between individuals using a variant of the PC-Relate method.


Requires the dataset to contain only diploid genotype calls.


Estimate kinship, identity-by-descent two, identity-by-descent one, and identity-by-descent zero for every pair of samples, using a minimum minor allele frequency filter of 0.01 and 10 principal components to control for population structure.

>>> rel = hl.pc_relate(dataset.GT, 0.01, k=10)

Only compute the kinship statistic. This is more efficient than computing all statistics.

>>> rel = hl.pc_relate(dataset.GT, 0.01, k=10, statistics='kin')

Compute all statistics, excluding sample-pairs with kinship less than 0.1. This is more efficient than producing the full table and then filtering using Table.filter().

>>> rel = hl.pc_relate(dataset.GT, 0.01, k=10, min_kinship=0.1)

One can also pass in pre-computed principal component scores. To produce the same results as in the previous example:

>>> _, scores_table, _ = hl.hwe_normalized_pca(dataset.GT,
...                                      k=10,
...                                      compute_loadings=False)
>>> rel = hl.pc_relate(dataset.GT,
...                    0.01,
...                    scores_expr=scores_table[dataset.col_key].scores,
...                    min_kinship=0.1)


The traditional estimator for kinship between a pair of individuals \(i\) and \(j\), sharing the set \(S_{ij}\) of single-nucleotide variants, from a population with allele frequencies \(p_s\), is given by:

\[\widehat{\phi_{ij}} \coloneqq \frac{1}{|S_{ij}|} \sum_{s \in S_{ij}} \frac{(g_{is} - 2 p_s) (g_{js} - 2 p_s)} {4 \sum_{s \in S_{ij}} p_s (1 - p_s)}\]

This estimator is true under the model that the sharing of common (relative to the population) alleles is not very informative to relatedness (because they’re common) and the sharing of rare alleles suggests a recent common ancestor from which the allele was inherited by descent.

When multiple ancestry groups are mixed in a sample, this model breaks down. Alleles that are rare in all but one ancestry group are treated as very informative to relatedness. However, these alleles are simply markers of the ancestry group. The PC-Relate method corrects for this situation and the related situation of admixed individuals.

PC-Relate slightly modifies the usual estimator for relatedness: occurrences of population allele frequency are replaced with an “individual-specific allele frequency”. This modification allows the method to correctly weight an allele according to an individual’s unique ancestry profile.

The “individual-specific allele frequency” at a given genetic locus is modeled by PC-Relate as a linear function of a sample’s first k principal component coordinates. As such, the efficacy of this method rests on two assumptions:

  • an individual’s first k principal component coordinates fully describe their allele-frequency-relevant ancestry, and

  • the relationship between ancestry (as described by principal component coordinates) and population allele frequency is linear

The estimators for kinship, and identity-by-descent zero, one, and two follow. Let:

  • \(S_{ij}\) be the set of genetic loci at which both individuals \(i\) and \(j\) have a defined genotype

  • \(g_{is} \in {0, 1, 2}\) be the number of alternate alleles that individual \(i\) has at genetic locus \(s\)

  • \(\widehat{\mu_{is}} \in [0, 1]\) be the individual-specific allele frequency for individual \(i\) at genetic locus \(s\)

  • \({\widehat{\sigma^2_{is}}} \coloneqq \widehat{\mu_{is}} (1 - \widehat{\mu_{is}})\), the binomial variance of \(\widehat{\mu_{is}}\)

  • \(\widehat{\sigma_{is}} \coloneqq \sqrt{\widehat{\sigma^2_{is}}}\), the binomial standard deviation of \(\widehat{\mu_{is}}\)

  • \(\text{IBS}^{(0)}_{ij} \coloneqq \sum_{s \in S_{ij}} \mathbb{1}_{||g_{is} - g_{js} = 2||}\), the number of genetic loci at which individuals \(i\) and \(j\) share no alleles

  • \(\widehat{f_i} \coloneqq 2 \widehat{\phi_{ii}} - 1\), the inbreeding coefficient for individual \(i\)

  • \(g^D_{is}\) be a dominance encoding of the genotype matrix, and \(X_{is}\) be a normalized dominance-coded genotype matrix

\[g^D_{is} \coloneqq \begin{cases} \widehat{\mu_{is}} & g_{is} = 0 \\ 0 & g_{is} = 1 \\ 1 - \widehat{\mu_{is}} & g_{is} = 2 \end{cases} \qquad X_{is} \coloneqq g^D_{is} - \widehat{\sigma^2_{is}} (1 - \widehat{f_i})\]

The estimator for kinship is given by:

\[\widehat{\phi_{ij}} \coloneqq \frac{\sum_{s \in S_{ij}}(g - 2 \mu)_{is} (g - 2 \mu)_{js}} {4 * \sum_{s \in S_{ij}} \widehat{\sigma_{is}} \widehat{\sigma_{js}}}\]

The estimator for identity-by-descent two is given by:

\[\widehat{k^{(2)}_{ij}} \coloneqq \frac{\sum_{s \in S_{ij}}X_{is} X_{js}}{\sum_{s \in S_{ij}} \widehat{\sigma^2_{is}} \widehat{\sigma^2_{js}}}\]

The estimator for identity-by-descent zero is given by:

\[\widehat{k^{(0)}_{ij}} \coloneqq \begin{cases} \frac{\text{IBS}^{(0)}_{ij}} {\sum_{s \in S_{ij}} \widehat{\mu_{is}}^2(1 - \widehat{\mu_{js}})^2 + (1 - \widehat{\mu_{is}})^2\widehat{\mu_{js}}^2} & \widehat{\phi_{ij}} > 2^{-5/2} \\ 1 - 4 \widehat{\phi_{ij}} + k^{(2)}_{ij} & \widehat{\phi_{ij}} \le 2^{-5/2} \end{cases}\]

The estimator for identity-by-descent one is given by:

\[\widehat{k^{(1)}_{ij}} \coloneqq 1 - \widehat{k^{(2)}_{ij}} - \widehat{k^{(0)}_{ij}}\]

Note that, even if present, phase information is ignored by this method.

The PC-Relate method is described in “Model-free Estimation of Recent Genetic Relatedness”. Conomos MP, Reiner AP, Weir BS, Thornton TA. in American Journal of Human Genetics. 2016 Jan 7. The reference implementation is available in the GENESIS Bioconductor package .

pc_relate() differs from the reference implementation in a few ways:

  • if k is supplied, samples scores are computed via PCA on all samples, not a specified subset of genetically unrelated samples. The latter can be achieved by filtering samples, computing PCA variant loadings, and using these loadings to compute and pass in scores for all samples.

  • the estimators do not perform small sample correction

  • the algorithm does not provide an option to use population-wide allele frequency estimates

  • the algorithm does not provide an option to not use “overall standardization” (see R pcrelate documentation)

Under the PC-Relate model, kinship, \(\phi_{ij}\), ranges from 0 to 0.5, and is precisely half of the fraction-of-genetic-material-shared. Listed below are the statistics for a few pairings:

  • Monozygotic twins share all their genetic material so their kinship statistic is 0.5 in expection.

  • Parent-child and sibling pairs both have kinship 0.25 in expectation and are separated by the identity-by-descent-zero, \(k^{(2)}_{ij}\), statistic which is zero for parent-child pairs and 0.25 for sibling pairs.

  • Avuncular pairs and grand-parent/-child pairs both have kinship 0.125 in expectation and both have identity-by-descent-zero 0.5 in expectation

  • “Third degree relatives” are those pairs sharing \(2^{-3} = 12.5 %\) of their genetic material, the results of PCRelate are often too noisy to reliably distinguish these pairs from higher-degree-relative-pairs or unrelated pairs.

Note that \(g_{is}\) is the number of alternate alleles. Hence, for multi-allelic variants, a value of 2 may indicate two distinct alternative alleles rather than a homozygous variant genotype. To enforce the latter, either filter or split multi-allelic variants first.

The resulting table has the first 3, 4, 5, or 6 fields below, depending on the statistics parameter:

  • i (col_key.dtype) – First sample. (key field)

  • j (col_key.dtype) – Second sample. (key field)

  • kin (tfloat64) – Kinship estimate, \(\widehat{\phi_{ij}}\).

  • ibd2 (tfloat64) – IBD2 estimate, \(\widehat{k^{(2)}_{ij}}\).

  • ibd0 (tfloat64) – IBD0 estimate, \(\widehat{k^{(0)}_{ij}}\).

  • ibd1 (tfloat64) – IBD1 estimate, \(\widehat{k^{(1)}_{ij}}\).

Here col_key refers to the column key of the source matrix table, and col_key.dtype is a struct containing the column key fields.

There is one row for each pair of distinct samples (columns), where i corresponds to the column of smaller column index. In particular, if the same column key value exists for \(n\) columns, then the resulting table will have \(\binom{n-1}{2}\) rows with both key fields equal to that column key value. This may result in unexpected behavior in downstream processing.

  • call_expr (CallExpression) – Entry-indexed call expression.

  • min_individual_maf (float) – The minimum individual-specific minor allele frequency. If either individual-specific minor allele frequency for a pair of individuals is below this threshold, then the variant will not be used to estimate relatedness for the pair.

  • k (int, optional) – If set, k principal component scores are computed and used. Exactly one of k and scores_expr must be specified.

  • scores_expr (ArrayNumericExpression, optional) – Column-indexed expression of principal component scores, with the same source as call_expr. All array values must have the same positive length, corresponding to the number of principal components, and all scores must be non-missing. Exactly one of k and scores_expr must be specified.

  • min_kinship (float, optional) – If set, pairs of samples with kinship lower than min_kinship are excluded from the results.

  • statistics (str) – Set of statistics to compute. If 'kin', only estimate the kinship statistic. If 'kin2', estimate the above and IBD2. If 'kin20', estimate the above and IBD0. If 'all', estimate the above and IBD1.

  • block_size (int, optional) – Block size of block matrices used in the algorithm. Default given by BlockMatrix.default_block_size().


Table – A Table mapping pairs of samples to their pair-wise statistics.


Purcell, Shaun et al. “PLINK: a tool set for whole-genome association and population-based linkage analyses.” American journal of human genetics vol. 81,3 (2007): 559-75. doi:10.1086/519795.


Manichaikul, Ani et al. “Robust relationship inference in genome-wide association studies.” Bioinformatics (Oxford, England) vol. 26,22 (2010): 2867-73. doi:10.1093/bioinformatics/btq559.


Conomos, Matthew P et al. “Model-free Estimation of Recent Genetic Relatedness.” American journal of human genetics vol. 98,1 (2016): 127-48. doi:10.1016/j.ajhg.2015.11.022.