def phase_parents_by_transmission(g, window_size):
"""Phase parent genotypes from a trio or cross, given progeny genotypes
already phased by Mendelian transmission.
Parameters
----------
g : GenotypeArray
Genotype array, with parents as first two columns and progeny as
remaining columns, where progeny genotypes are already phased.
window_size : int
Number of previous heterozygous sites to include when phasing each
parent. A number somewhere between 10 and 100 may be appropriate,
depending on levels of heterozygosity and quality of data.
Returns
-------
g : GenotypeArray
Genotype array with parents phased where possible.
"""
# setup
check_type(g, GenotypeArray)
check_dtype(g.values, 'i1')
check_ploidy(g.ploidy, 2)
if g.is_phased is None:
raise ValueError('genotype array must first have progeny phased by transmission')
check_min_samples(g.n_samples, 3)
# run the phasing
is_phased = g.is_phased.view('u1')
phase_parents_by_transmission_int8(g.values, is_phased, window_size)
# outputs
return g
def phase_by_transmission(g, window_size, copy=True):
"""Phase genotypes in a trio or cross where possible using Mendelian
transmission.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, 2)
Genotype array, with parents as first two columns and progeny as
remaining columns.
window_size : int
Number of previous heterozygous sites to include when phasing each
parent. A number somewhere between 10 and 100 may be appropriate,
depending on levels of heterozygosity and quality of data.
copy : bool, optional
If False, attempt to phase genotypes in-place. Note that this is
only possible if the input array has int8 dtype, otherwise a copy is
always made regardless of this parameter.
Returns
-------
g : GenotypeArray
Genotype array with progeny phased where possible.
"""
# setup
g = np.asarray(g, dtype='i1')
g = GenotypeArray(g, copy=copy)
g._values = memoryview_safe(g.values)
check_ploidy(g.ploidy, 2)
check_min_samples(g.n_samples, 3)
# phase the progeny
is_phased = _opt_phase_progeny_by_transmission(g.values)
g.is_phased = np.asarray(is_phased).view(bool)
# phase the parents
_opt_phase_parents_by_transmission(g.values, is_phased, window_size)
return g
def phase_progeny_by_transmission(g):
"""Phase progeny genotypes from a trio or cross using Mendelian
transmission.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, 2)
Genotype array, with parents as first two columns and progeny as
remaining columns.
Returns
-------
g : ndarray, int8, shape (n_variants, n_samples, 2)
Genotype array with progeny phased where possible.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([
... [[0, 0], [0, 0], [0, 0]],
... [[1, 1], [1, 1], [1, 1]],
... [[0, 0], [1, 1], [0, 1]],
... [[1, 1], [0, 0], [0, 1]],
... [[0, 0], [0, 1], [0, 0]],
... [[0, 0], [0, 1], [0, 1]],
... [[0, 1], [0, 0], [0, 1]],
... [[0, 1], [0, 1], [0, 1]],
... [[0, 1], [1, 2], [0, 1]],
... [[1, 2], [0, 1], [1, 2]],
... [[0, 1], [2, 3], [0, 2]],
... [[2, 3], [0, 1], [1, 3]],
... [[0, 0], [0, 0], [-1, -1]],
... [[0, 0], [0, 0], [1, 1]],
... ], dtype='i1')
>>> g = allel.phase_progeny_by_transmission(g)
>>> print(g.to_str(row_threshold=None))
0/0 0/0 0|0
1/1 1/1 1|1
0/0 1/1 0|1
1/1 0/0 1|0
0/0 0/1 0|0
0/0 0/1 0|1
0/1 0/0 1|0
0/1 0/1 0/1
0/1 1/2 0|1
1/2 0/1 2|1
0/1 2/3 0|2
2/3 0/1 3|1
0/0 0/0 ./.
0/0 0/0 1/1
>>> g.is_phased
array([[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, False],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, False],
[False, False, False]])
"""
# setup
g = GenotypeArray(g, dtype='i1', copy=True)
check_ploidy(g.ploidy, 2)
check_min_samples(g.n_samples, 3)
# run the phasing
# N.B., a copy has already been made, so no need to make memoryview safe
is_phased = _opt_phase_progeny_by_transmission(g.values)
g.is_phased = np.asarray(is_phased).view(bool)
# outputs
return g
def mendel_errors(parent_genotypes, progeny_genotypes):
"""Locate genotype calls not consistent with Mendelian transmission of
alleles.
Parameters
----------
parent_genotypes : array_like, int, shape (n_variants, 2, 2)
Genotype calls for the two parents.
progeny_genotypes : array_like, int, shape (n_variants, n_progeny, 2)
Genotype calls for the progeny.
Returns
-------
me : ndarray, int, shape (n_variants, n_progeny)
Count of Mendel errors for each progeny genotype call.
Examples
--------
The following are all consistent with Mendelian transmission. Note that a
value of 0 is returned for missing calls::
>>> import allel
>>> import numpy as np
>>> genotypes = np.array([
... # aa x aa -> aa
... [[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1], [-1, -1]],
... [[1, 1], [1, 1], [1, 1], [-1, -1], [-1, -1], [-1, -1]],
... [[2, 2], [2, 2], [2, 2], [-1, -1], [-1, -1], [-1, -1]],
... # aa x ab -> aa or ab
... [[0, 0], [0, 1], [0, 0], [0, 1], [-1, -1], [-1, -1]],
... [[0, 0], [0, 2], [0, 0], [0, 2], [-1, -1], [-1, -1]],
... [[1, 1], [0, 1], [1, 1], [0, 1], [-1, -1], [-1, -1]],
... # aa x bb -> ab
... [[0, 0], [1, 1], [0, 1], [-1, -1], [-1, -1], [-1, -1]],
... [[0, 0], [2, 2], [0, 2], [-1, -1], [-1, -1], [-1, -1]],
... [[1, 1], [2, 2], [1, 2], [-1, -1], [-1, -1], [-1, -1]],
... # aa x bc -> ab or ac
... [[0, 0], [1, 2], [0, 1], [0, 2], [-1, -1], [-1, -1]],
... [[1, 1], [0, 2], [0, 1], [1, 2], [-1, -1], [-1, -1]],
... # ab x ab -> aa or ab or bb
... [[0, 1], [0, 1], [0, 0], [0, 1], [1, 1], [-1, -1]],
... [[1, 2], [1, 2], [1, 1], [1, 2], [2, 2], [-1, -1]],
... [[0, 2], [0, 2], [0, 0], [0, 2], [2, 2], [-1, -1]],
... # ab x bc -> ab or ac or bb or bc
... [[0, 1], [1, 2], [0, 1], [0, 2], [1, 1], [1, 2]],
... [[0, 1], [0, 2], [0, 0], [0, 1], [0, 1], [1, 2]],
... # ab x cd -> ac or ad or bc or bd
... [[0, 1], [2, 3], [0, 2], [0, 3], [1, 2], [1, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
The following are cases of 'non-parental' inheritance where one or two
alleles are found in the progeny that are not present in either parent.
Note that the number of errors may be 1 or 2 depending on the number of
non-parental alleles::
>>> genotypes = np.array([
... # aa x aa -> ab or ac or bb or cc
... [[0, 0], [0, 0], [0, 1], [0, 2], [1, 1], [2, 2]],
... [[1, 1], [1, 1], [0, 1], [1, 2], [0, 0], [2, 2]],
... [[2, 2], [2, 2], [0, 2], [1, 2], [0, 0], [1, 1]],
... # aa x ab -> ac or bc or cc
... [[0, 0], [0, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 0], [0, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 1], [0, 1], [1, 2], [0, 2], [2, 2], [2, 2]],
... # aa x bb -> ac or bc or cc
... [[0, 0], [1, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 0], [2, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 1], [2, 2], [0, 1], [0, 2], [0, 0], [0, 0]],
... # ab x ab -> ac or bc or cc
... [[0, 1], [0, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 2], [0, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 2], [1, 2], [0, 1], [0, 2], [0, 0], [0, 0]],
... # ab x bc -> ad or bd or cd or dd
... [[0, 1], [1, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... [[0, 1], [0, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... [[0, 2], [1, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... # ab x cd -> ae or be or ce or de
... [[0, 1], [2, 3], [0, 4], [1, 4], [2, 4], [3, 4]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 1, 2],
[1, 1, 1, 2],
[1, 1, 1, 2],
[1, 1, 1, 1]])
The following are cases of 'hemi-parental' inheritance, where progeny
appear to have inherited two copies of an allele found only once in one of
the parents::
>>> genotypes = np.array([
... # aa x ab -> bb
... [[0, 0], [0, 1], [1, 1], [-1, -1]],
... [[0, 0], [0, 2], [2, 2], [-1, -1]],
... [[1, 1], [0, 1], [0, 0], [-1, -1]],
... # ab x bc -> aa or cc
... [[0, 1], [1, 2], [0, 0], [2, 2]],
... [[0, 1], [0, 2], [1, 1], [2, 2]],
... [[0, 2], [1, 2], [0, 0], [1, 1]],
... # ab x cd -> aa or bb or cc or dd
... [[0, 1], [2, 3], [0, 0], [1, 1]],
... [[0, 1], [2, 3], [2, 2], [3, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1]])
The following are cases of 'uni-parental' inheritance, where progeny
appear to have inherited both alleles from a single parent::
>>> genotypes = np.array([
... # aa x bb -> aa or bb
... [[0, 0], [1, 1], [0, 0], [1, 1]],
... [[0, 0], [2, 2], [0, 0], [2, 2]],
... [[1, 1], [2, 2], [1, 1], [2, 2]],
... # aa x bc -> aa or bc
... [[0, 0], [1, 2], [0, 0], [1, 2]],
... [[1, 1], [0, 2], [1, 1], [0, 2]],
... # ab x cd -> ab or cd
... [[0, 1], [2, 3], [0, 1], [2, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1]])
"""
# setup
parent_genotypes = GenotypeArray(parent_genotypes)
progeny_genotypes = GenotypeArray(progeny_genotypes)
check_ploidy(parent_genotypes.ploidy, 2)
check_ploidy(progeny_genotypes.ploidy, 2)
# transform into per-call allele counts
max_allele = max(parent_genotypes.max(), progeny_genotypes.max())
parent_gc = parent_genotypes.to_allele_counts(max_allele=max_allele, dtype='i1')
progeny_gc = progeny_genotypes.to_allele_counts(max_allele=max_allele, dtype='i1')
# detect nonparental and hemiparental inheritance by comparing allele
# counts between parents and progeny
max_progeny_gc = parent_gc.clip(max=1).sum(axis=1)
max_progeny_gc = max_progeny_gc[:, np.newaxis, :]
me = (progeny_gc - max_progeny_gc).clip(min=0).sum(axis=2)
# detect uniparental inheritance by finding cases where no alleles are
# shared between parents, then comparing progeny allele counts to each
# parent
p1_gc = parent_gc[:, 0, np.newaxis, :]
p2_gc = parent_gc[:, 1, np.newaxis, :]
# find variants where parents don't share any alleles
is_shared_allele = (p1_gc > 0) & (p2_gc > 0)
no_shared_alleles = ~np.any(is_shared_allele, axis=2)
# find calls where progeny genotype is identical to one or the other parent
me[no_shared_alleles &
(np.all(progeny_gc == p1_gc, axis=2) |
np.all(progeny_gc == p2_gc, axis=2))] = 1
# retrofit where either or both parent has a missing call
me[np.any(parent_genotypes.is_missing(), axis=1)] = 0
return me
