from typing import Tuple
import hail as hl
import hail.expr.aggregators as agg
from hail.expr import expr_call, expr_float64
from hail.genetics.pedigree import Pedigree
from hail.matrixtable import MatrixTable
from hail.table import Table
from hail.typecheck import numeric, typecheck
from hail.utils.java import Env
from .misc import require_biallelic, require_col_key_str
[docs]@typecheck(dataset=MatrixTable, pedigree=Pedigree, complete_trios=bool)
def trio_matrix(dataset, pedigree, complete_trios=False) -> MatrixTable:
"""Builds and returns a matrix where columns correspond to trios and entries contain genotypes for the trio.
.. include:: ../_templates/req_tstring.rst
Examples
--------
Create a trio matrix:
>>> pedigree = hl.Pedigree.read('data/case_control_study.fam')
>>> trio_dataset = hl.trio_matrix(dataset, pedigree, complete_trios=True)
Notes
-----
This method builds a new matrix table with one column per trio. If
`complete_trios` is ``True``, then only trios that satisfy
:meth:`.Trio.is_complete` are included. In this new dataset, the column
identifiers are the sample IDs of the trio probands. The column fields and
entries of the matrix are changed in the following ways:
The new column fields consist of three structs (`proband`, `father`,
`mother`), a Boolean field, and a string field:
- **proband** (:class:`.tstruct`) - Column fields on the proband.
- **father** (:class:`.tstruct`) - Column fields on the father.
- **mother** (:class:`.tstruct`) - Column fields on the mother.
- **id** (:py:data:`.tstr`) - Column key for the proband.
- **is_female** (:py:data:`.tbool`) - Proband is female.
``True`` for female, ``False`` for male, missing if unknown.
- **fam_id** (:py:data:`.tstr`) - Family ID.
The new entry fields are:
- **proband_entry** (:class:`.tstruct`) - Proband entry fields.
- **father_entry** (:class:`.tstruct`) - Father entry fields.
- **mother_entry** (:class:`.tstruct`) - Mother entry fields.
Parameters
----------
pedigree : :class:`.Pedigree`
Returns
-------
:class:`.MatrixTable`
"""
mt = dataset
require_col_key_str(mt, "trio_matrix")
k = mt.col_key.dtype.fields[0]
samples = mt[k].collect()
pedigree = pedigree.filter_to(samples)
trios = pedigree.complete_trios() if complete_trios else pedigree.trios
n_trios = len(trios)
sample_idx = {}
for i, s in enumerate(samples):
sample_idx[s] = i
trios = [
hl.Struct(
id=sample_idx[t.s],
pat_id=None if t.pat_id is None else sample_idx[t.pat_id],
mat_id=None if t.mat_id is None else sample_idx[t.mat_id],
is_female=t.is_female,
fam_id=t.fam_id,
)
for t in trios
]
trios_type = hl.dtype('array<struct{id:int,pat_id:int,mat_id:int,is_female:bool,fam_id:str}>')
trios_sym = Env.get_uid()
entries_sym = Env.get_uid()
cols_sym = Env.get_uid()
mt = mt.annotate_globals(**{trios_sym: hl.literal(trios, trios_type)})
mt = mt._localize_entries(entries_sym, cols_sym)
mt = mt.annotate_globals(**{
cols_sym: hl.map(
lambda i: hl.bind(
lambda t: hl.struct(
id=mt[cols_sym][t.id][k],
proband=mt[cols_sym][t.id],
father=mt[cols_sym][t.pat_id],
mother=mt[cols_sym][t.mat_id],
is_female=t.is_female,
fam_id=t.fam_id,
),
mt[trios_sym][i],
),
hl.range(0, n_trios),
)
})
mt = mt.annotate(**{
entries_sym: hl.map(
lambda i: hl.bind(
lambda t: hl.struct(
proband_entry=mt[entries_sym][t.id],
father_entry=mt[entries_sym][t.pat_id],
mother_entry=mt[entries_sym][t.mat_id],
),
mt[trios_sym][i],
),
hl.range(0, n_trios),
)
})
mt = mt.drop(trios_sym)
return mt._unlocalize_entries(entries_sym, cols_sym, ['id'])
[docs]@typecheck(call=expr_call, pedigree=Pedigree)
def mendel_errors(call, pedigree) -> Tuple[Table, Table, Table, Table]:
r"""Find Mendel errors; count per variant, individual and nuclear family.
.. include:: ../_templates/req_tstring.rst
.. include:: ../_templates/req_tvariant.rst
.. include:: ../_templates/req_biallelic.rst
Examples
--------
Find all violations of Mendelian inheritance in each (dad, mom, kid) trio in
a pedigree and return four tables (all errors, errors by family, errors by
individual, errors by variant):
>>> ped = hl.Pedigree.read('data/trios.fam')
>>> all_errors, per_fam, per_sample, per_variant = hl.mendel_errors(dataset['GT'], ped)
Export all mendel errors to a text file:
>>> all_errors.export('output/all_mendel_errors.tsv')
Annotate columns with the number of Mendel errors:
>>> annotated_samples = dataset.annotate_cols(mendel=per_sample[dataset.s])
Annotate rows with the number of Mendel errors:
>>> annotated_variants = dataset.annotate_rows(mendel=per_variant[dataset.locus, dataset.alleles])
Notes
-----
The example above returns four tables, which contain Mendelian violations
grouped in various ways. These tables are modeled after the `PLINK mendel
formats <https://www.cog-genomics.org/plink2/formats#mendel>`_, resembling
the ``.mendel``, ``.fmendel``, ``.imendel``, and ``.lmendel`` formats,
respectively.
**First table:** all Mendel errors. This table contains one row per Mendel
error, keyed by the variant and proband id.
- `locus` (:class:`.tlocus`) -- Variant locus, key field.
- `alleles` (:class:`.tarray` of :py:data:`.tstr`) -- Variant alleles, key field.
- (column key of `dataset`) (:py:data:`.tstr`) -- Proband ID, key field.
- `fam_id` (:py:data:`.tstr`) -- Family ID.
- `mendel_code` (:py:data:`.tint32`) -- Mendel error code, see below.
**Second table:** errors per nuclear family. This table contains one row
per nuclear family, keyed by the parents.
- `pat_id` (:py:data:`.tstr`) -- Paternal ID. (key field)
- `mat_id` (:py:data:`.tstr`) -- Maternal ID. (key field)
- `fam_id` (:py:data:`.tstr`) -- Family ID.
- `children` (:py:data:`.tint32`) -- Number of children in this nuclear family.
- `errors` (:py:data:`.tint64`) -- Number of Mendel errors in this nuclear family.
- `snp_errors` (:py:data:`.tint64`) -- Number of Mendel errors at SNPs in this
nuclear family.
**Third table:** errors per individual. This table contains one row per
individual. Each error is counted toward the proband, father, and mother
according to the `Implicated` in the table below.
- (column key of `dataset`) (:py:data:`.tstr`) -- Sample ID (key field).
- `fam_id` (:py:data:`.tstr`) -- Family ID.
- `errors` (:py:data:`.tint64`) -- Number of Mendel errors involving this
individual.
- `snp_errors` (:py:data:`.tint64`) -- Number of Mendel errors involving this
individual at SNPs.
**Fourth table:** errors per variant.
- `locus` (:class:`.tlocus`) -- Variant locus, key field.
- `alleles` (:class:`.tarray` of :py:data:`.tstr`) -- Variant alleles, key field.
- `errors` (:py:data:`.tint64`) -- Number of Mendel errors in this variant.
This method only considers complete trios (two parents and proband with
defined sex). The code of each Mendel error is determined by the table
below, extending the
`Plink classification <https://www.cog-genomics.org/plink2/basic_stats#mendel>`__.
In the table, the copy state of a locus with respect to a trio is defined
as follows, where PAR is the `pseudoautosomal region
<https://en.wikipedia.org/wiki/Pseudoautosomal_region>`__ (PAR) of X and Y
defined by the reference genome and the autosome is defined by
:meth:`~.LocusExpression.in_autosome`.
- Auto -- in autosome or in PAR or female child
- HemiX -- in non-PAR of X and male child
- HemiY -- in non-PAR of Y and male child
`Any` refers to the set \{ HomRef, Het, HomVar, NoCall \} and `~`
denotes complement in this set.
+------+---------+---------+--------+----------------------------+
| Code | Dad | Mom | Kid | Copy State | Implicated |
+======+=========+=========+========+============+===============+
| 1 | HomVar | HomVar | Het | Auto | Dad, Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 2 | HomRef | HomRef | Het | Auto | Dad, Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 3 | HomRef | ~HomRef | HomVar | Auto | Dad, Kid |
+------+---------+---------+--------+------------+---------------+
| 4 | ~HomRef | HomRef | HomVar | Auto | Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 5 | HomRef | HomRef | HomVar | Auto | Kid |
+------+---------+---------+--------+------------+---------------+
| 6 | HomVar | ~HomVar | HomRef | Auto | Dad, Kid |
+------+---------+---------+--------+------------+---------------+
| 7 | ~HomVar | HomVar | HomRef | Auto | Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 8 | HomVar | HomVar | HomRef | Auto | Kid |
+------+---------+---------+--------+------------+---------------+
| 9 | Any | HomVar | HomRef | HemiX | Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 10 | Any | HomRef | HomVar | HemiX | Mom, Kid |
+------+---------+---------+--------+------------+---------------+
| 11 | HomVar | Any | HomRef | HemiY | Dad, Kid |
+------+---------+---------+--------+------------+---------------+
| 12 | HomRef | Any | HomVar | HemiY | Dad, Kid |
+------+---------+---------+--------+------------+---------------+
See Also
--------
:func:`.mendel_error_code`
Parameters
----------
dataset : :class:`.MatrixTable`
pedigree : :class:`.Pedigree`
Returns
-------
(:class:`.Table`, :class:`.Table`, :class:`.Table`, :class:`.Table`)
"""
source = call._indices.source
if not isinstance(source, MatrixTable):
raise ValueError(
"'mendel_errors': expected 'call' to be an expression of 'MatrixTable', found {}".format(
"expression of '{}'".format(source.__class__) if source is not None else 'scalar expression'
)
)
source = source.select_entries(__GT=call)
dataset = require_biallelic(source, 'mendel_errors')
tm = trio_matrix(dataset, pedigree, complete_trios=True)
tm = tm.select_entries(
mendel_code=hl.mendel_error_code(
tm.locus, tm.is_female, tm.father_entry['__GT'], tm.mother_entry['__GT'], tm.proband_entry['__GT']
)
)
ck_name = next(iter(source.col_key))
tm = tm.filter_entries(hl.is_defined(tm.mendel_code))
tm = tm.rename({'id': ck_name})
entries = tm.entries()
table1 = entries.select('fam_id', 'mendel_code')
t2 = tm.annotate_cols(errors=hl.agg.count(), snp_errors=hl.agg.count_where(hl.is_snp(tm.alleles[0], tm.alleles[1])))
table2 = t2.key_cols_by().cols()
table2 = table2.select(
pat_id=table2.father[ck_name],
mat_id=table2.mother[ck_name],
fam_id=table2.fam_id,
errors=table2.errors,
snp_errors=table2.snp_errors,
)
table2 = table2.group_by('pat_id', 'mat_id').aggregate(
fam_id=hl.agg.take(table2.fam_id, 1)[0],
children=hl.int32(hl.agg.count()),
errors=hl.agg.sum(table2.errors),
snp_errors=hl.agg.sum(table2.snp_errors),
)
table2 = table2.annotate(
errors=hl.or_else(table2.errors, hl.int64(0)), snp_errors=hl.or_else(table2.snp_errors, hl.int64(0))
)
# in implicated, idx 0 is dad, idx 1 is mom, idx 2 is child
implicated = hl.literal(
[
[0, 0, 0], # dummy
[1, 1, 1],
[1, 1, 1],
[1, 0, 1],
[0, 1, 1],
[0, 0, 1],
[1, 0, 1],
[0, 1, 1],
[0, 0, 1],
[0, 1, 1],
[0, 1, 1],
[1, 0, 1],
[1, 0, 1],
],
dtype=hl.tarray(hl.tarray(hl.tint64)),
)
table3 = (
tm.annotate_cols(
all_errors=hl.or_else(hl.agg.array_sum(implicated[tm.mendel_code]), [0, 0, 0]),
snp_errors=hl.or_else(
hl.agg.filter(hl.is_snp(tm.alleles[0], tm.alleles[1]), hl.agg.array_sum(implicated[tm.mendel_code])),
[0, 0, 0],
),
)
.key_cols_by()
.cols()
)
table3 = table3.select(
xs=[
hl.struct(**{
ck_name: table3.father[ck_name],
'fam_id': table3.fam_id,
'errors': table3.all_errors[0],
'snp_errors': table3.snp_errors[0],
}),
hl.struct(**{
ck_name: table3.mother[ck_name],
'fam_id': table3.fam_id,
'errors': table3.all_errors[1],
'snp_errors': table3.snp_errors[1],
}),
hl.struct(**{
ck_name: table3.proband[ck_name],
'fam_id': table3.fam_id,
'errors': table3.all_errors[2],
'snp_errors': table3.snp_errors[2],
}),
]
)
table3 = table3.explode('xs')
table3 = table3.select(**table3.xs)
table3 = (
table3.group_by(ck_name, 'fam_id')
.aggregate(errors=hl.agg.sum(table3.errors), snp_errors=hl.agg.sum(table3.snp_errors))
.key_by(ck_name)
)
table4 = tm.select_rows(errors=hl.agg.count_where(hl.is_defined(tm.mendel_code))).rows()
return table1, table2, table3, table4
[docs]@typecheck(dataset=MatrixTable, pedigree=Pedigree)
def transmission_disequilibrium_test(dataset, pedigree) -> Table:
r"""Performs the transmission disequilibrium test on trios.
.. include:: ../_templates/req_tstring.rst
.. include:: ../_templates/req_tvariant.rst
.. include:: ../_templates/req_biallelic.rst
Examples
--------
Compute TDT association statistics and show the first two results:
>>> pedigree = hl.Pedigree.read('data/tdt_trios.fam')
>>> tdt_table = hl.transmission_disequilibrium_test(tdt_dataset, pedigree)
>>> tdt_table.show(2) # doctest: +SKIP_OUTPUT_CHECK
+---------------+------------+-------+-------+----------+----------+
| locus | alleles | t | u | chi_sq | p_value |
+---------------+------------+-------+-------+----------+----------+
| locus<GRCh37> | array<str> | int64 | int64 | float64 | float64 |
+---------------+------------+-------+-------+----------+----------+
| 1:246714629 | ["C","A"] | 0 | 4 | 4.00e+00 | 4.55e-02 |
| 2:167262169 | ["T","C"] | NA | NA | NA | NA |
+---------------+------------+-------+-------+----------+----------+
Export variants with p-values below 0.001:
>>> tdt_table = tdt_table.filter(tdt_table.p_value < 0.001)
>>> tdt_table.export(f"output/tdt_results.tsv")
Notes
-----
The
`transmission disequilibrium test <https://en.wikipedia.org/wiki/Transmission_disequilibrium_test#The_case_of_trios:_one_affected_child_per_family>`__
compares the number of times the alternate allele is transmitted (t) versus
not transmitted (u) from a heterozgyous parent to an affected child. The null
hypothesis holds that each case is equally likely. The TDT statistic is given by
.. math::
(t - u)^2 \over (t + u)
and asymptotically follows a chi-squared distribution with one degree of
freedom under the null hypothesis.
:func:`transmission_disequilibrium_test` only considers complete trios (two
parents and a proband with defined sex) and only returns results for the
autosome, as defined by :meth:`~.LocusExpression.in_autosome`, and
chromosome X. Transmissions and non-transmissions are counted only for the
configurations of genotypes and copy state in the table below, in order to
filter out Mendel errors and configurations where transmission is
guaranteed. The copy state of a locus with respect to a trio is defined as
follows:
- Auto -- in autosome or in PAR of X or female child
- HemiX -- in non-PAR of X and male child
Here PAR is the `pseudoautosomal region
<https://en.wikipedia.org/wiki/Pseudoautosomal_region>`__
of X and Y defined by :class:`.ReferenceGenome`, which many variant callers
map to chromosome X.
+--------+--------+--------+------------+---+---+
| Kid | Dad | Mom | Copy State | t | u |
+========+========+========+============+===+===+
| HomRef | Het | Het | Auto | 0 | 2 |
+--------+--------+--------+------------+---+---+
| HomRef | HomRef | Het | Auto | 0 | 1 |
+--------+--------+--------+------------+---+---+
| HomRef | Het | HomRef | Auto | 0 | 1 |
+--------+--------+--------+------------+---+---+
| Het | Het | Het | Auto | 1 | 1 |
+--------+--------+--------+------------+---+---+
| Het | HomRef | Het | Auto | 1 | 0 |
+--------+--------+--------+------------+---+---+
| Het | Het | HomRef | Auto | 1 | 0 |
+--------+--------+--------+------------+---+---+
| Het | HomVar | Het | Auto | 0 | 1 |
+--------+--------+--------+------------+---+---+
| Het | Het | HomVar | Auto | 0 | 1 |
+--------+--------+--------+------------+---+---+
| HomVar | Het | Het | Auto | 2 | 0 |
+--------+--------+--------+------------+---+---+
| HomVar | Het | HomVar | Auto | 1 | 0 |
+--------+--------+--------+------------+---+---+
| HomVar | HomVar | Het | Auto | 1 | 0 |
+--------+--------+--------+------------+---+---+
| HomRef | HomRef | Het | HemiX | 0 | 1 |
+--------+--------+--------+------------+---+---+
| HomRef | HomVar | Het | HemiX | 0 | 1 |
+--------+--------+--------+------------+---+---+
| HomVar | HomRef | Het | HemiX | 1 | 0 |
+--------+--------+--------+------------+---+---+
| HomVar | HomVar | Het | HemiX | 1 | 0 |
+--------+--------+--------+------------+---+---+
:func:`.transmission_disequilibrium_test` produces a table with the following columns:
- `locus` (:class:`.tlocus`) -- Locus.
- `alleles` (:class:`.tarray` of :py:data:`.tstr`) -- Alleles.
- `t` (:py:data:`.tint32`) -- Number of transmitted alternate alleles.
- `u` (:py:data:`.tint32`) -- Number of untransmitted alternate alleles.
- `chi_sq` (:py:data:`.tfloat64`) -- TDT statistic.
- `p_value` (:py:data:`.tfloat64`) -- p-value.
Parameters
----------
dataset : :class:`.MatrixTable`
Dataset.
pedigree : :class:`~hail.genetics.Pedigree`
Sample pedigree.
Returns
-------
:class:`.Table`
Table of TDT results.
"""
dataset = require_biallelic(dataset, 'transmission_disequilibrium_test')
dataset = dataset.annotate_rows(auto_or_x_par=dataset.locus.in_autosome() | dataset.locus.in_x_par())
dataset = dataset.filter_rows(dataset.auto_or_x_par | dataset.locus.in_x_nonpar())
hom_ref = 0
het = 1
hom_var = 2
auto = 2
hemi_x = 1
# kid, dad, mom, copy, t, u
config_counts = [
(hom_ref, het, het, auto, 0, 2),
(hom_ref, hom_ref, het, auto, 0, 1),
(hom_ref, het, hom_ref, auto, 0, 1),
(het, het, het, auto, 1, 1),
(het, hom_ref, het, auto, 1, 0),
(het, het, hom_ref, auto, 1, 0),
(het, hom_var, het, auto, 0, 1),
(het, het, hom_var, auto, 0, 1),
(hom_var, het, het, auto, 2, 0),
(hom_var, het, hom_var, auto, 1, 0),
(hom_var, hom_var, het, auto, 1, 0),
(hom_ref, hom_ref, het, hemi_x, 0, 1),
(hom_ref, hom_var, het, hemi_x, 0, 1),
(hom_var, hom_ref, het, hemi_x, 1, 0),
(hom_var, hom_var, het, hemi_x, 1, 0),
]
count_map = hl.literal({(c[0], c[1], c[2], c[3]): [c[4], c[5]] for c in config_counts})
tri = trio_matrix(dataset, pedigree, complete_trios=True)
# this filter removes mendel error of het father in x_nonpar. It also avoids
# building and looking up config in common case that neither parent is het
father_is_het = tri.father_entry.GT.is_het()
parent_is_valid_het = (father_is_het & tri.auto_or_x_par) | (tri.mother_entry.GT.is_het() & ~father_is_het)
copy_state = hl.if_else(tri.auto_or_x_par | tri.is_female, 2, 1)
config = (
tri.proband_entry.GT.n_alt_alleles(),
tri.father_entry.GT.n_alt_alleles(),
tri.mother_entry.GT.n_alt_alleles(),
copy_state,
)
tri = tri.annotate_rows(counts=agg.filter(parent_is_valid_het, agg.array_sum(count_map.get(config))))
tab = tri.rows().select('counts')
tab = tab.transmute(t=tab.counts[0], u=tab.counts[1])
tab = tab.annotate(chi_sq=((tab.t - tab.u) ** 2) / (tab.t + tab.u))
tab = tab.annotate(p_value=hl.pchisqtail(tab.chi_sq, 1.0))
return tab.cache()
[docs]@typecheck(
mt=MatrixTable,
pedigree=Pedigree,
pop_frequency_prior=expr_float64,
min_gq=int,
min_p=numeric,
max_parent_ab=numeric,
min_child_ab=numeric,
min_dp_ratio=numeric,
ignore_in_sample_allele_frequency=bool,
)
def de_novo(
mt: MatrixTable,
pedigree: Pedigree,
pop_frequency_prior,
*,
min_gq: int = 20,
min_p: float = 0.05,
max_parent_ab: float = 0.05,
min_child_ab: float = 0.20,
min_dp_ratio: float = 0.10,
ignore_in_sample_allele_frequency: bool = False,
) -> Table:
r"""Call putative *de novo* events from trio data.
.. include:: ../_templates/req_tstring.rst
.. include:: ../_templates/req_tvariant.rst
.. include:: ../_templates/req_biallelic.rst
Examples
--------
Call de novo events:
>>> pedigree = hl.Pedigree.read('data/trios.fam')
>>> priors = hl.import_table('data/gnomadFreq.tsv', impute=True)
>>> priors = priors.transmute(**hl.parse_variant(priors.Variant)).key_by('locus', 'alleles')
>>> de_novo_results = hl.de_novo(dataset, pedigree, pop_frequency_prior=priors[dataset.row_key].AF)
Notes
-----
This method assumes the GATK high-throughput sequencing fields exist:
`GT`, `AD`, `DP`, `GQ`, `PL`.
This method replicates the functionality of `Kaitlin Samocha's de novo
caller <https://github.com/ksamocha/de_novo_scripts>`__. The version
corresponding to git commit ``bde3e40`` is implemented in Hail with her
permission and assistance.
This method produces a :class:`.Table` with the following fields:
- `locus` (``locus``) -- Variant locus.
- `alleles` (``array<str>``) -- Variant alleles.
- `id` (``str``) -- Proband sample ID.
- `prior` (``float64``) -- Site frequency prior. It is the maximum of:
the computed dataset alternate allele frequency, the
`pop_frequency_prior` parameter, and the global prior
``1 / 3e7``. If the `ignore_in_sample_allele_frequency` parameter is ``True``,
then the computed allele frequency is not included in the calculation, and the
prior is the maximum of the `pop_frequency_prior` and ``1 / 3e7``.
- `proband` (``struct``) -- Proband column fields from `mt`.
- `father` (``struct``) -- Father column fields from `mt`.
- `mother` (``struct``) -- Mother column fields from `mt`.
- `proband_entry` (``struct``) -- Proband entry fields from `mt`.
- `father_entry` (``struct``) -- Father entry fields from `mt`.
- `proband_entry` (``struct``) -- Mother entry fields from `mt`.
- `is_female` (``bool``) -- ``True`` if proband is female.
- `p_de_novo` (``float64``) -- Unfiltered posterior probability
that the event is *de novo* rather than a missed heterozygous
event in a parent.
- `confidence` (``str``) Validation confidence. One of: ``'HIGH'``,
``'MEDIUM'``, ``'LOW'``.
The key of the table is ``['locus', 'alleles', 'id']``.
The model looks for de novo events in which both parents are homozygous
reference and the proband is a heterozygous. The model makes the simplifying
assumption that when this configuration ``x = (AA, AA, AB)`` of calls
occurs, exactly one of the following is true:
- ``d``: a de novo mutation occurred in the proband and all calls are
accurate.
- ``m``: at least one parental allele is actually heterozygous and
the proband call is accurate.
We can then estimate the posterior probability of a de novo mutation as:
.. math::
\mathrm{P_{\text{de novo}}} = \frac{\mathrm{P}(d \mid x)}{\mathrm{P}(d \mid x) + \mathrm{P}(m \mid x)}
Applying Bayes rule to the numerator and denominator yields
.. math::
\frac{\mathrm{P}(x \mid d)\,\mathrm{P}(d)}{\mathrm{P}(x \mid d)\,\mathrm{P}(d) +
\mathrm{P}(x \mid m)\,\mathrm{P}(m)}
The prior on de novo mutation is estimated from the rate in the literature:
.. math::
\mathrm{P}(d) = \frac{1 \, \text{mutation}}{30{,}000{,}000 \, \text{bases}}
The prior used for at least one alternate allele between the parents
depends on the alternate allele frequency:
.. math::
\mathrm{P}(m) = 1 - (1 - AF)^4
The likelihoods :math:`\mathrm{P}(x \mid d)` and :math:`\mathrm{P}(x \mid m)`
are computed from the PL (genotype likelihood) fields using these
factorizations:
.. math::
\mathrm{P}(x = (AA, AA, AB) \mid d) = \left(
\begin{aligned}
&\mathrm{P}(x_{\mathrm{father}} = AA \mid \mathrm{father} = AA) \\
{} \cdot {} &\mathrm{P}(x_{\mathrm{mother}} = AA \mid \mathrm{mother} = AA) \\
{} \cdot {} &\mathrm{P}(x_{\mathrm{proband}} = AB \mid \mathrm{proband} = AB)
\end{aligned}
\right)
.. math::
\begin{aligned}
\mathrm{P}(x = (AA, AA, AB) \mid m) = &\left(
\begin{aligned}
&\mathrm{P}(x_{\mathrm{father}} = AA \mid \mathrm{father} = AB)
\cdot \mathrm{P}(x_{\mathrm{mother}} = AA \mid \mathrm{mother} = AA) \\
{} + {} &\mathrm{P}(x_{\mathrm{father}} = AA \mid \mathrm{father} = AA)
\cdot \mathrm{P}(x_{\mathrm{mother}} = AA \mid \mathrm{mother} = AB)
\end{aligned}
\right) \\
&{} \cdot \mathrm{P}(x_{\mathrm{proband}} = AB \mid \mathrm{proband} = AB)
\end{aligned}
(Technically, the second factorization assumes there is exactly (rather
than at least) one alternate allele among the parents, which may be
justified on the grounds that it is typically the most likely case by far.)
While this posterior probability is a good metric for grouping putative de
novo mutations by validation likelihood, there exist error modes in
high-throughput sequencing data that are not appropriately accounted for by
the phred-scaled genotype likelihoods. To this end, a number of hard filters
are applied in order to assign validation likelihood.
These filters are different for SNPs and insertions/deletions. In the below
rules, the following variables are used:
- ``DR`` refers to the ratio of the read depth in the proband to the
combined read depth in the parents.
- ``DP`` refers to the read depth (DP field) of the proband.
- ``AB`` refers to the read allele balance of the proband (number of
alternate reads divided by total reads).
- ``AC`` refers to the count of alternate alleles across all individuals
in the dataset at the site.
- ``p`` refers to :math:`\mathrm{P_{\text{de novo}}}`.
- ``min_p`` refers to the `min_p` function parameter.
HIGH-quality SNV:
.. code-block:: text
(p > 0.99) AND (AB > 0.3) AND (AC == 1)
OR
(p > 0.99) AND (AB > 0.3) AND (DR > 0.2)
OR
(p > 0.5) AND (AB > 0.3) AND (AC < 10) AND (DP > 10)
MEDIUM-quality SNV:
.. code-block:: text
(p > 0.5) AND (AB > 0.3)
OR
(AC == 1)
LOW-quality SNV:
.. code-block:: text
(AB > 0.2)
HIGH-quality indel:
.. code-block:: text
(p > 0.99) AND (AB > 0.3) AND (AC == 1)
MEDIUM-quality indel:
.. code-block:: text
(p > 0.5) AND (AB > 0.3) AND (AC < 10)
LOW-quality indel:
.. code-block:: text
(AB > 0.2)
Additionally, de novo candidates are not considered if the proband GQ is
smaller than the `min_gq` parameter, if the proband allele balance is
lower than the `min_child_ab` parameter, if the depth ratio between the
proband and parents is smaller than the `min_depth_ratio` parameter, if
the allele balance in a parent is above the `max_parent_ab` parameter, or
if the posterior probability `p` is smaller than the `min_p` parameter.
Parameters
----------
mt : :class:`.MatrixTable`
High-throughput sequencing dataset.
pedigree : :class:`.Pedigree`
Sample pedigree.
pop_frequency_prior : :class:`.Float64Expression`
Expression for population alternate allele frequency prior.
min_gq
Minimum proband GQ to be considered for *de novo* calling.
min_p
Minimum posterior probability to be considered for *de novo* calling.
max_parent_ab
Maximum parent allele balance.
min_child_ab
Minimum proband allele balance/
min_dp_ratio
Minimum ratio between proband read depth and parental read depth.
ignore_in_sample_allele_frequency
Ignore in-sample allele frequency in computing site prior. Experimental.
Returns
-------
:class:`.Table`
"""
DE_NOVO_PRIOR = 1 / 30000000
MIN_POP_PRIOR = 100 / 30000000
required_entry_fields = {'GT', 'AD', 'DP', 'GQ', 'PL'}
missing_fields = required_entry_fields - set(mt.entry)
if missing_fields:
raise ValueError(
f"'de_novo': expected 'MatrixTable' to have at least {required_entry_fields}, " f"missing {missing_fields}"
)
pop_frequency_prior = (
hl.case()
.when((pop_frequency_prior >= 0) & (pop_frequency_prior <= 1), pop_frequency_prior)
.or_error(hl.str("de_novo: expect 0 <= pop_frequency_prior <= 1, found " + hl.str(pop_frequency_prior)))
)
if ignore_in_sample_allele_frequency:
# this mode is used when families larger than a single trio are observed, in which
# an allele might be de novo in a parent and transmitted to a child in the dataset.
# The original model does not handle this case correctly, and so this experimental
# mode can be used to treat each trio as if it were the only one in the dataset.
mt = mt.annotate_rows(
__prior=pop_frequency_prior,
__alt_alleles=hl.int64(1),
__site_freq=hl.max(pop_frequency_prior, MIN_POP_PRIOR),
)
else:
n_alt_alleles = hl.agg.sum(mt.GT.n_alt_alleles())
total_alleles = 2 * hl.agg.sum(hl.is_defined(mt.GT))
# subtract 1 from __alt_alleles to correct for the observed genotype
mt = mt.annotate_rows(
__prior=pop_frequency_prior,
__alt_alleles=n_alt_alleles,
__site_freq=hl.max((n_alt_alleles - 1) / total_alleles, pop_frequency_prior, MIN_POP_PRIOR),
)
mt = require_biallelic(mt, 'de_novo')
tm = trio_matrix(mt, pedigree, complete_trios=True)
autosomal = tm.locus.in_autosome_or_par() | (tm.locus.in_x_nonpar() & tm.is_female)
hemi_x = tm.locus.in_x_nonpar() & ~tm.is_female
hemi_y = tm.locus.in_y_nonpar() & ~tm.is_female
hemi_mt = tm.locus.in_mito() & tm.is_female
is_snp = hl.is_snp(tm.alleles[0], tm.alleles[1])
n_alt_alleles = tm.__alt_alleles
prior = tm.__site_freq
het_hom_hom = tm.proband_entry.GT.is_het() & tm.father_entry.GT.is_hom_ref() & tm.mother_entry.GT.is_hom_ref()
kid_ad_fail = tm.proband_entry.AD[1] / hl.sum(tm.proband_entry.AD) < min_child_ab
failure = hl.missing(hl.tstruct(p_de_novo=hl.tfloat64, confidence=hl.tstr))
kid = tm.proband_entry
dad = tm.father_entry
mom = tm.mother_entry
kid_linear_pl = 10 ** (-kid.PL / 10)
kid_pp = hl.bind(lambda x: x / hl.sum(x), kid_linear_pl)
dad_linear_pl = 10 ** (-dad.PL / 10)
dad_pp = hl.bind(lambda x: x / hl.sum(x), dad_linear_pl)
mom_linear_pl = 10 ** (-mom.PL / 10)
mom_pp = hl.bind(lambda x: x / hl.sum(x), mom_linear_pl)
kid_ad_ratio = kid.AD[1] / hl.sum(kid.AD)
dp_ratio = kid.DP / (dad.DP + mom.DP)
def call_auto(kid_pp, dad_pp, mom_pp, kid_ad_ratio):
p_data_given_dn = dad_pp[0] * mom_pp[0] * kid_pp[1] * DE_NOVO_PRIOR
p_het_in_parent = 1 - (1 - prior) ** 4
p_data_given_missed_het = (dad_pp[1] * mom_pp[0] + dad_pp[0] * mom_pp[1]) * kid_pp[1] * p_het_in_parent
p_de_novo = p_data_given_dn / (p_data_given_dn + p_data_given_missed_het)
def solve(p_de_novo):
return (
hl.case()
.when(kid.GQ < min_gq, failure)
.when((kid.DP / (dad.DP + mom.DP) < min_dp_ratio) | ~(kid_ad_ratio >= min_child_ab), failure)
.when((hl.sum(mom.AD) == 0) | (hl.sum(dad.AD) == 0), failure)
.when(
(mom.AD[1] / hl.sum(mom.AD) > max_parent_ab) | (dad.AD[1] / hl.sum(dad.AD) > max_parent_ab), failure
)
.when(p_de_novo < min_p, failure)
.when(
~is_snp,
hl.case()
.when(
(p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (n_alt_alleles == 1),
hl.struct(p_de_novo=p_de_novo, confidence='HIGH'),
)
.when(
(p_de_novo > 0.5) & (kid_ad_ratio > 0.3) & (n_alt_alleles <= 5),
hl.struct(p_de_novo=p_de_novo, confidence='MEDIUM'),
)
.when(kid_ad_ratio > 0.2, hl.struct(p_de_novo=p_de_novo, confidence='LOW'))
.or_missing(),
)
.default(
hl.case()
.when(
((p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (dp_ratio > 0.2))
| ((p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (n_alt_alleles == 1))
| ((p_de_novo > 0.5) & (kid_ad_ratio > 0.3) & (n_alt_alleles < 10) & (kid.DP > 10)),
hl.struct(p_de_novo=p_de_novo, confidence='HIGH'),
)
.when(
(p_de_novo > 0.5) & ((kid_ad_ratio > 0.3) | (n_alt_alleles == 1)),
hl.struct(p_de_novo=p_de_novo, confidence='MEDIUM'),
)
.when(kid_ad_ratio > 0.2, hl.struct(p_de_novo=p_de_novo, confidence='LOW'))
.or_missing()
)
)
return hl.bind(solve, p_de_novo)
def call_hemi(kid_pp, parent, parent_pp, kid_ad_ratio):
p_data_given_dn = parent_pp[0] * kid_pp[1] * DE_NOVO_PRIOR
p_het_in_parent = 1 - (1 - prior) ** 4
p_data_given_missed_het = (parent_pp[1] + parent_pp[2]) * kid_pp[2] * p_het_in_parent
p_de_novo = p_data_given_dn / (p_data_given_dn + p_data_given_missed_het)
def solve(p_de_novo):
return (
hl.case()
.when(kid.GQ < min_gq, failure)
.when((kid.DP / (parent.DP) < min_dp_ratio) | (kid_ad_ratio < min_child_ab), failure)
.when((hl.sum(parent.AD) == 0), failure)
.when(parent.AD[1] / hl.sum(parent.AD) > max_parent_ab, failure)
.when(p_de_novo < min_p, failure)
.when(
~is_snp,
hl.case()
.when(
(p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (n_alt_alleles == 1),
hl.struct(p_de_novo=p_de_novo, confidence='HIGH'),
)
.when(
(p_de_novo > 0.5) & (kid_ad_ratio > 0.3) & (n_alt_alleles <= 5),
hl.struct(p_de_novo=p_de_novo, confidence='MEDIUM'),
)
.when(kid_ad_ratio > 0.3, hl.struct(p_de_novo=p_de_novo, confidence='LOW'))
.or_missing(),
)
.default(
hl.case()
.when(
((p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (dp_ratio > 0.2))
| ((p_de_novo > 0.99) & (kid_ad_ratio > 0.3) & (n_alt_alleles == 1))
| ((p_de_novo > 0.5) & (kid_ad_ratio > 0.3) & (n_alt_alleles < 10) & (kid.DP > 10)),
hl.struct(p_de_novo=p_de_novo, confidence='HIGH'),
)
.when(
(p_de_novo > 0.5) & ((kid_ad_ratio > 0.3) | (n_alt_alleles == 1)),
hl.struct(p_de_novo=p_de_novo, confidence='MEDIUM'),
)
.when(kid_ad_ratio > 0.2, hl.struct(p_de_novo=p_de_novo, confidence='LOW'))
.or_missing()
)
)
return hl.bind(solve, p_de_novo)
de_novo_call = (
hl.case()
.when(~het_hom_hom | kid_ad_fail, failure)
.when(autosomal, hl.bind(call_auto, kid_pp, dad_pp, mom_pp, kid_ad_ratio))
.when(hemi_x | hemi_mt, hl.bind(call_hemi, kid_pp, mom, mom_pp, kid_ad_ratio))
.when(hemi_y, hl.bind(call_hemi, kid_pp, dad, dad_pp, kid_ad_ratio))
.or_missing()
)
tm = tm.annotate_entries(__call=de_novo_call)
tm = tm.filter_entries(hl.is_defined(tm.__call))
entries = tm.entries()
return entries.select(
'__site_freq',
'proband',
'father',
'mother',
'proband_entry',
'father_entry',
'mother_entry',
'is_female',
**entries.__call,
).rename({'__site_freq': 'prior'})