Source code for hail.methods.family_methods

from typing import *
import hail as hl
import hail.expr.aggregators as agg
from hail.genetics.pedigree import Pedigree
from hail.matrixtable import MatrixTable
from hail.expr import expr_call, expr_float64
from hail.table import Table
from hail.typecheck import *
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:`~hail.genetics.Locus.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:`~hail.genetics.Locus.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:`tdt` 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.cond(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) 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) -> 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``. - `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. 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}") mt = mt.annotate_rows(__prior=pop_frequency_prior, __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(__site_freq=hl.max((mt.__alt_alleles - 1) / mt.__total_alleles, mt.__prior, MIN_POP_PRIOR)) mt = require_biallelic(mt, 'de_novo') # FIXME check that __site_freq is between 0 and 1 when possible in expr 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.null(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'}))