def test_read_stored_globals(self):
ds = self.get_vds()
ds = ds.annotate_globals(x=5, baz='foo')
f = new_temp_file(suffix='vds')
ds.write(f)
t = hl.read_table(f + '/globals')
self.assertTrue(ds.globals_table()._same(t))
def test_backward_compatability(self):
import os
all_values_table, all_values_matrix_table = create_all_values_datasets()
table_dir = resource('backward_compatability/1.0.0/table')
matrix_table_dir = resource('backward_compatability/1.0.0/matrix_table')
n = 0
i = 0
f = os.path.join(table_dir, '{}.ht'.format(i))
while os.path.exists(f):
ds = hl.read_table(f)
self.assertTrue(ds._same(all_values_table))
i += 1
f = os.path.join(table_dir, '{}.ht'.format(i))
n += 1
i = 0
f = os.path.join(matrix_table_dir, '{}.hmt'.format(i))
while os.path.exists(f):
ds = hl.read_matrix_table(f)
self.assertTrue(ds._same(all_values_matrix_table))
i += 1
f = os.path.join(matrix_table_dir, '{}.hmt'.format(i))
n += 1
self.assertEqual(n, 8)
def test_codecs_table(self):
from hail.utils.java import scala_object
codecs = scala_object(Env.hail().io, 'CodecSpec').codecSpecs()
rt = self.get_vds().rows()
temp = new_temp_file(suffix='ht')
for codec in codecs:
rt.write(temp, overwrite=True, _codec_spec=codec.toString())
rt2 = hl.read_table(temp)
self.assertTrue(rt._same(rt2))
def test_fix3307_read_mt_wrong(self):
mt = hl.import_vcf(resource('sample2.vcf'))
mt = hl.split_multi_hts(mt)
mt.write('/tmp/foo.mt', overwrite=True)
mt2 = hl.read_matrix_table('/tmp/foo.mt')
t = hl.read_table('/tmp/foo.mt/rows')
self.assertTrue(mt.rows()._same(t))
self.assertTrue(mt2.rows()._same(t))
self.assertTrue(mt._same(mt2))
def test_large_number_of_fields(tmpdir):
mt = hl.utils.range_table(100)
mt = mt.annotate(**{
str(k): k for k in range(1000)
})
f = tmpdir.join("foo.mt")
assert_time(lambda: mt.count(), 5)
assert_time(lambda: mt.write(str(f)), 5)
mt = assert_time(lambda: hl.read_table(str(f)), 5)
assert_time(lambda: mt.count(), 5)
def read_expression(path):
"""Read an :class:`Expression` written with :meth:`.experimental.write_expression`.
Example
-------
>>> hl.experimental.write_expression(hl.array([1, 2]), 'output/test_expression.he')
>>> expression = hl.experimental.read_expression('output/test_expression.he')
>>> hl.eval(expression)
Parameters
----------
path : :obj:`str`
File to read.
Returns
-------
:class:`Expression`
"""
return hl.read_table(path).index_globals().expr
def ld_score_regression(weight_expr,
ld_score_expr,
chi_sq_exprs,
n_samples_exprs,
n_blocks=200,
two_step_threshold=30,
n_reference_panel_variants=None) -> Table:
r"""Estimate SNP-heritability and level of confounding biases from
GWAS summary statistics.
Given a set or multiple sets of genome-wide association study (GWAS)
summary statistics, :func:`.ld_score_regression` estimates the heritability
of a trait or set of traits and the level of confounding biases present in
the underlying studies by regressing chi-squared statistics on LD scores,
leveraging the model:
.. math::
\mathrm{E}[\chi_j^2] = 1 + Na + \frac{Nh_g^2}{M}l_j
* :math:`\mathrm{E}[\chi_j^2]` is the expected chi-squared statistic
for variant :math:`j` resulting from a test of association between
variant :math:`j` and a trait.
* :math:`l_j = \sum_{k} r_{jk}^2` is the LD score of variant
:math:`j`, calculated as the sum of squared correlation coefficients
between variant :math:`j` and nearby variants. See :func:`ld_score`
for further details.
* :math:`a` captures the contribution of confounding biases, such as
cryptic relatedness and uncontrolled population structure, to the
association test statistic.
* :math:`h_g^2` is the SNP-heritability, or the proportion of variation
in the trait explained by the effects of variants included in the
regression model above.
* :math:`M` is the number of variants used to estimate :math:`h_g^2`.
* :math:`N` is the number of samples in the underlying association study.
For more details on the method implemented in this function, see:
* `LD Score regression distinguishes confounding from polygenicity in genome-wide association studies (Bulik-Sullivan et al, 2015) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495769/>`__
Examples
--------
Run the method on a matrix table of summary statistics, where the rows
are variants and the columns are different phenotypes:
>>> mt_gwas = hl.read_matrix_table('data/ld_score_regression.sumstats.mt')
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=mt_gwas['ld_score'],
... ld_score_expr=mt_gwas['ld_score'],
... chi_sq_exprs=mt_gwas['chi_squared'],
... n_samples_exprs=mt_gwas['n'])
Run the method on a table with summary statistics for a single
phenotype:
>>> ht_gwas = hl.read_table('data/ld_score_regression.sumstats.ht')
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=ht_gwas['ld_score'],
... ld_score_expr=ht_gwas['ld_score'],
... chi_sq_exprs=ht_gwas['chi_squared_50_irnt'],
... n_samples_exprs=ht_gwas['n_50_irnt'])
Run the method on a table with summary statistics for multiple
phenotypes:
>>> ht_gwas = hl.read_table('data/ld_score_regression.sumstats.ht')
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=ht_gwas['ld_score'],
... ld_score_expr=ht_gwas['ld_score'],
... chi_sq_exprs=[ht_gwas['chi_squared_50_irnt'],
... ht_gwas['chi_squared_20160']],
... n_samples_exprs=[ht_gwas['n_50_irnt'],
... ht_gwas['n_20160']])
Notes
-----
The ``exprs`` provided as arguments to :func:`.ld_score_regression`
must all be from the same object, either a :class:`Table` or a
:class:`MatrixTable`.
**If the arguments originate from a table:**
* The table must be keyed by fields ``locus`` of type
:class:`.tlocus` and ``alleles``, a :py:data:`.tarray` of
:py:data:`.tstr` elements.
* ``weight_expr``, ``ld_score_expr``, ``chi_sq_exprs``, and
``n_samples_exprs`` are must be row-indexed fields.
* The number of expressions passed to ``n_samples_exprs`` must be
equal to one or the number of expressions passed to
``chi_sq_exprs``. If just one expression is passed to
``n_samples_exprs``, that sample size expression is assumed to
apply to all sets of statistics passed to ``chi_sq_exprs``.
Otherwise, the expressions passed to ``chi_sq_exprs`` and
``n_samples_exprs`` are matched by index.
* The ``phenotype`` field that keys the table returned by
:func:`.ld_score_regression` will have generic :obj:`int` values
``0``, ``1``, etc. corresponding to the ``0th``, ``1st``, etc.
expressions passed to the ``chi_sq_exprs`` argument.
**If the arguments originate from a matrix table:**
* The dimensions of the matrix table must be variants
(rows) by phenotypes (columns).
* The rows of the matrix table must be keyed by fields
``locus`` of type :class:`.tlocus` and ``alleles``,
a :py:data:`.tarray` of :py:data:`.tstr` elements.
* The columns of the matrix table must be keyed by a field
of type :py:data:`.tstr` that uniquely identifies phenotypes
represented in the matrix table. The column key must be a single
expression; compound keys are not accepted.
* ``weight_expr`` and ``ld_score_expr`` must be row-indexed
fields.
* ``chi_sq_exprs`` must be a single entry-indexed field
(not a list of fields).
* ``n_samples_exprs`` must be a single entry-indexed field
(not a list of fields).
* The ``phenotype`` field that keys the table returned by
:func:`.ld_score_regression` will have values corresponding to the
column keys of the input matrix table.
This function returns a :class:`Table` with one row per set of summary
statistics passed to the ``chi_sq_exprs`` argument. The following
row-indexed fields are included in the table:
* **phenotype** (:py:data:`.tstr`) -- The name of the phenotype. The
returned table is keyed by this field. See the notes below for
details on the possible values of this field.
* **mean_chi_sq** (:py:data:`.tfloat64`) -- The mean chi-squared
test statistic for the given phenotype.
* **intercept** (`Struct`) -- Contains fields:
- **estimate** (:py:data:`.tfloat64`) -- A point estimate of the
intercept :math:`1 + Na`.
- **standard_error** (:py:data:`.tfloat64`) -- An estimate of
the standard error of this point estimate.
* **snp_heritability** (`Struct`) -- Contains fields:
- **estimate** (:py:data:`.tfloat64`) -- A point estimate of the
SNP-heritability :math:`h_g^2`.
- **standard_error** (:py:data:`.tfloat64`) -- An estimate of
the standard error of this point estimate.
Warning
-------
:func:`.ld_score_regression` considers only the rows for which both row
fields ``weight_expr`` and ``ld_score_expr`` are defined. Rows with missing
values in either field are removed prior to fitting the LD score
regression model.
Parameters
----------
weight_expr : :class:`.Float64Expression`
Row-indexed expression for the LD scores used to derive
variant weights in the model.
ld_score_expr : :class:`.Float64Expression`
Row-indexed expression for the LD scores used as covariates
in the model.
chi_sq_exprs : :class:`.Float64Expression` or :obj:`list` of
:class:`.Float64Expression`
One or more row-indexed (if table) or entry-indexed
(if matrix table) expressions for chi-squared
statistics resulting from genome-wide association
studies.
n_samples_exprs: :class:`.NumericExpression` or :obj:`list` of
:class:`.NumericExpression`
One or more row-indexed (if table) or entry-indexed
(if matrix table) expressions indicating the number of
samples used in the studies that generated the test
statistics supplied to ``chi_sq_exprs``.
n_blocks : :obj:`int`
The number of blocks used in the jackknife approach to
estimating standard errors.
two_step_threshold : :obj:`int`
Variants with chi-squared statistics greater than this
value are excluded in the first step of the two-step
procedure used to fit the model.
n_reference_panel_variants : :obj:`int`, optional
Number of variants used to estimate the
SNP-heritability :math:`h_g^2`.
Returns
-------
:class:`.Table`
Table keyed by ``phenotype`` with intercept and heritability estimates
for each phenotype passed to the function."""
chi_sq_exprs = wrap_to_list(chi_sq_exprs)
n_samples_exprs = wrap_to_list(n_samples_exprs)
assert ((len(chi_sq_exprs) == len(n_samples_exprs)) or
(len(n_samples_exprs) == 1))
__k = 2 # number of covariates, including intercept
ds = chi_sq_exprs[0]._indices.source
analyze('ld_score_regression/weight_expr',
weight_expr,
ds._row_indices)
analyze('ld_score_regression/ld_score_expr',
ld_score_expr,
ds._row_indices)
# format input dataset
if isinstance(ds, MatrixTable):
if len(chi_sq_exprs) != 1:
raise ValueError("""Only one chi_sq_expr allowed if originating
from a matrix table.""")
if len(n_samples_exprs) != 1:
raise ValueError("""Only one n_samples_expr allowed if
originating from a matrix table.""")
col_key = list(ds.col_key)
if len(col_key) != 1:
raise ValueError("""Matrix table must be keyed by a single
phenotype field.""")
analyze('ld_score_regression/chi_squared_expr',
chi_sq_exprs[0],
ds._entry_indices)
analyze('ld_score_regression/n_samples_expr',
n_samples_exprs[0],
ds._entry_indices)
ds = ds._select_all(row_exprs={'__locus': ds.locus,
'__alleles': ds.alleles,
'__w_initial': weight_expr,
'__w_initial_floor': hl.max(weight_expr,
1.0),
'__x': ld_score_expr,
'__x_floor': hl.max(ld_score_expr,
1.0)},
row_key=['__locus', '__alleles'],
col_exprs={'__y_name': ds[col_key[0]]},
col_key=['__y_name'],
entry_exprs={'__y': chi_sq_exprs[0],
'__n': n_samples_exprs[0]})
ds = ds.annotate_entries(**{'__w': ds.__w_initial})
ds = ds.filter_rows(hl.is_defined(ds.__locus) &
hl.is_defined(ds.__alleles) &
hl.is_defined(ds.__w_initial) &
hl.is_defined(ds.__x))
else:
assert isinstance(ds, Table)
for y in chi_sq_exprs:
analyze('ld_score_regression/chi_squared_expr', y, ds._row_indices)
for n in n_samples_exprs:
analyze('ld_score_regression/n_samples_expr', n, ds._row_indices)
ys = ['__y{:}'.format(i) for i, _ in enumerate(chi_sq_exprs)]
ws = ['__w{:}'.format(i) for i, _ in enumerate(chi_sq_exprs)]
ns = ['__n{:}'.format(i) for i, _ in enumerate(n_samples_exprs)]
ds = ds.select(**dict(**{'__locus': ds.locus,
'__alleles': ds.alleles,
'__w_initial': weight_expr,
'__x': ld_score_expr},
**{y: chi_sq_exprs[i]
for i, y in enumerate(ys)},
**{w: weight_expr for w in ws},
**{n: n_samples_exprs[i]
for i, n in enumerate(ns)}))
ds = ds.key_by(ds.__locus, ds.__alleles)
table_tmp_file = new_temp_file()
ds.write(table_tmp_file)
ds = hl.read_table(table_tmp_file)
hts = [ds.select(**{'__w_initial': ds.__w_initial,
'__w_initial_floor': hl.max(ds.__w_initial,
1.0),
'__x': ds.__x,
'__x_floor': hl.max(ds.__x, 1.0),
'__y_name': i,
'__y': ds[ys[i]],
'__w': ds[ws[i]],
'__n': hl.int(ds[ns[i]])})
for i, y in enumerate(ys)]
mts = [ht.to_matrix_table(row_key=['__locus',
'__alleles'],
col_key=['__y_name'],
row_fields=['__w_initial',
'__w_initial_floor',
'__x',
'__x_floor'])
for ht in hts]
ds = mts[0]
for i in range(1, len(ys)):
ds = ds.union_cols(mts[i])
ds = ds.filter_rows(hl.is_defined(ds.__locus) &
hl.is_defined(ds.__alleles) &
hl.is_defined(ds.__w_initial) &
hl.is_defined(ds.__x))
mt_tmp_file1 = new_temp_file()
ds.write(mt_tmp_file1)
mt = hl.read_matrix_table(mt_tmp_file1)
if not n_reference_panel_variants:
M = mt.count_rows()
else:
M = n_reference_panel_variants
# block variants for each phenotype
n_phenotypes = mt.count_cols()
mt = mt.annotate_entries(__in_step1=(hl.is_defined(mt.__y) &
(mt.__y < two_step_threshold)),
__in_step2=hl.is_defined(mt.__y))
mt = mt.annotate_cols(__col_idx=hl.int(hl.scan.count()),
__m_step1=hl.agg.count_where(mt.__in_step1),
__m_step2=hl.agg.count_where(mt.__in_step2))
col_keys = list(mt.col_key)
ht = mt.localize_entries(entries_array_field_name='__entries',
columns_array_field_name='__cols')
ht = ht.annotate(__entries=hl.rbind(
hl.scan.array_agg(
lambda entry: hl.scan.count_where(entry.__in_step1),
ht.__entries),
lambda step1_indices: hl.map(
lambda i: hl.rbind(
hl.int(hl.or_else(step1_indices[i], 0)),
ht.__cols[i].__m_step1,
ht.__entries[i],
lambda step1_idx, m_step1, entry: hl.rbind(
hl.map(
lambda j: hl.int(hl.floor(j * (m_step1 / n_blocks))),
hl.range(0, n_blocks + 1)),
lambda step1_separators: hl.rbind(
hl.set(step1_separators).contains(step1_idx),
hl.sum(
hl.map(
lambda s1: step1_idx >= s1,
step1_separators)) - 1,
lambda is_separator, step1_block: entry.annotate(
__step1_block=step1_block,
__step2_block=hl.cond(~entry.__in_step1 & is_separator,
step1_block - 1,
step1_block))))),
hl.range(0, hl.len(ht.__entries)))))
mt = ht._unlocalize_entries('__entries', '__cols', col_keys)
mt_tmp_file2 = new_temp_file()
mt.write(mt_tmp_file2)
mt = hl.read_matrix_table(mt_tmp_file2)
# initial coefficient estimates
mt = mt.annotate_cols(__initial_betas=[
1.0, (hl.agg.mean(mt.__y) - 1.0) / hl.agg.mean(mt.__x)])
mt = mt.annotate_cols(__step1_betas=mt.__initial_betas,
__step2_betas=mt.__initial_betas)
# step 1 iteratively reweighted least squares
for i in range(3):
mt = mt.annotate_entries(__w=hl.cond(
mt.__in_step1,
1.0/(mt.__w_initial_floor * 2.0 * (mt.__step1_betas[0] +
mt.__step1_betas[1] *
mt.__x_floor)**2),
0.0))
mt = mt.annotate_cols(__step1_betas=hl.agg.filter(
mt.__in_step1,
hl.agg.linreg(y=mt.__y,
x=[1.0, mt.__x],
weight=mt.__w).beta))
mt = mt.annotate_cols(__step1_h2=hl.max(hl.min(
mt.__step1_betas[1] * M / hl.agg.mean(mt.__n), 1.0), 0.0))
mt = mt.annotate_cols(__step1_betas=[
mt.__step1_betas[0],
mt.__step1_h2 * hl.agg.mean(mt.__n) / M])
# step 1 block jackknife
mt = mt.annotate_cols(__step1_block_betas=[
hl.agg.filter((mt.__step1_block != i) & mt.__in_step1,
hl.agg.linreg(y=mt.__y,
x=[1.0, mt.__x],
weight=mt.__w).beta)
for i in range(n_blocks)])
mt = mt.annotate_cols(__step1_block_betas_bias_corrected=hl.map(
lambda x: n_blocks * mt.__step1_betas - (n_blocks - 1) * x,
mt.__step1_block_betas))
mt = mt.annotate_cols(
__step1_jackknife_mean=hl.map(
lambda i: hl.mean(
hl.map(lambda x: x[i],
mt.__step1_block_betas_bias_corrected)),
hl.range(0, __k)),
__step1_jackknife_variance=hl.map(
lambda i: (hl.sum(
hl.map(lambda x: x[i]**2,
mt.__step1_block_betas_bias_corrected)) -
hl.sum(
hl.map(lambda x: x[i],
mt.__step1_block_betas_bias_corrected))**2 /
n_blocks) /
(n_blocks - 1) / n_blocks,
hl.range(0, __k)))
# step 2 iteratively reweighted least squares
for i in range(3):
mt = mt.annotate_entries(__w=hl.cond(
mt.__in_step2,
1.0/(mt.__w_initial_floor *
2.0 * (mt.__step2_betas[0] +
mt.__step2_betas[1] *
mt.__x_floor)**2),
0.0))
mt = mt.annotate_cols(__step2_betas=[
mt.__step1_betas[0],
hl.agg.filter(mt.__in_step2,
hl.agg.linreg(y=mt.__y - mt.__step1_betas[0],
x=[mt.__x],
weight=mt.__w).beta[0])])
mt = mt.annotate_cols(__step2_h2=hl.max(hl.min(
mt.__step2_betas[1] * M/hl.agg.mean(mt.__n), 1.0), 0.0))
mt = mt.annotate_cols(__step2_betas=[
mt.__step1_betas[0],
mt.__step2_h2 * hl.agg.mean(mt.__n)/M])
# step 2 block jackknife
mt = mt.annotate_cols(__step2_block_betas=[
hl.agg.filter((mt.__step2_block != i) & mt.__in_step2,
hl.agg.linreg(y=mt.__y - mt.__step1_betas[0],
x=[mt.__x],
weight=mt.__w).beta[0])
for i in range(n_blocks)])
mt = mt.annotate_cols(__step2_block_betas_bias_corrected=hl.map(
lambda x: n_blocks * mt.__step2_betas[1] - (n_blocks - 1) * x,
mt.__step2_block_betas))
mt = mt.annotate_cols(
__step2_jackknife_mean=hl.mean(
mt.__step2_block_betas_bias_corrected),
__step2_jackknife_variance=(
hl.sum(mt.__step2_block_betas_bias_corrected**2) -
hl.sum(mt.__step2_block_betas_bias_corrected)**2 /
n_blocks) / (n_blocks - 1) / n_blocks)
# combine step 1 and step 2 block jackknifes
mt = mt.annotate_entries(
__step2_initial_w=1.0/(mt.__w_initial_floor *
2.0 * (mt.__initial_betas[0] +
mt.__initial_betas[1] *
mt.__x_floor)**2))
mt = mt.annotate_cols(
__final_betas=[
mt.__step1_betas[0],
mt.__step2_betas[1]],
__c=(hl.agg.sum(mt.__step2_initial_w * mt.__x) /
hl.agg.sum(mt.__step2_initial_w * mt.__x**2)))
mt = mt.annotate_cols(__final_block_betas=hl.map(
lambda i: (mt.__step2_block_betas[i] - mt.__c *
(mt.__step1_block_betas[i][0] - mt.__final_betas[0])),
hl.range(0, n_blocks)))
mt = mt.annotate_cols(
__final_block_betas_bias_corrected=(n_blocks * mt.__final_betas[1] -
(n_blocks - 1) *
mt.__final_block_betas))
mt = mt.annotate_cols(
__final_jackknife_mean=[
mt.__step1_jackknife_mean[0],
hl.mean(mt.__final_block_betas_bias_corrected)],
__final_jackknife_variance=[
mt.__step1_jackknife_variance[0],
(hl.sum(mt.__final_block_betas_bias_corrected**2) -
hl.sum(mt.__final_block_betas_bias_corrected)**2 /
n_blocks) / (n_blocks - 1) / n_blocks])
# convert coefficient to heritability estimate
mt = mt.annotate_cols(
phenotype=mt.__y_name,
mean_chi_sq=hl.agg.mean(mt.__y),
intercept=hl.struct(
estimate=mt.__final_betas[0],
standard_error=hl.sqrt(mt.__final_jackknife_variance[0])),
snp_heritability=hl.struct(
estimate=(M/hl.agg.mean(mt.__n)) * mt.__final_betas[1],
standard_error=hl.sqrt((M/hl.agg.mean(mt.__n))**2 *
mt.__final_jackknife_variance[1])))
# format and return results
ht = mt.cols()
ht = ht.key_by(ht.phenotype)
ht = ht.select(ht.mean_chi_sq,
ht.intercept,
ht.snp_heritability)
ht_tmp_file = new_temp_file()
ht.write(ht_tmp_file)
ht = hl.read_table(ht_tmp_file)
return ht
def maximal_independent_set(i,
j,
keep=True,
tie_breaker=None,
keyed=True) -> Table:
"""Return a table containing the vertices in a near
`maximal independent set <https://en.wikipedia.org/wiki/Maximal_independent_set>`_
of an undirected graph whose edges are given by a two-column table.
Examples
--------
Run PC-relate and compute pairs of closely related individuals:
>>> pc_rel = hl.pc_relate(dataset.GT, 0.001, k=2, statistics='kin')
>>> pairs = pc_rel.filter(pc_rel['kin'] > 0.125)
Starting from the above pairs, prune individuals from a dataset until no
close relationships remain:
>>> related_samples_to_remove = hl.maximal_independent_set(pairs.i, pairs.j, False)
>>> result = dataset.filter_cols(
... hl.is_defined(related_samples_to_remove[dataset.col_key]), keep=False)
Starting from the above pairs, prune individuals from a dataset until no
close relationships remain, preferring to keep cases over controls:
>>> samples = dataset.cols()
>>> pairs_with_case = pairs.key_by(
... i=hl.struct(id=pairs.i, is_case=samples[pairs.i].is_case),
... j=hl.struct(id=pairs.j, is_case=samples[pairs.j].is_case))
>>> def tie_breaker(l, r):
... return hl.cond(l.is_case & ~r.is_case, -1,
... hl.cond(~l.is_case & r.is_case, 1, 0))
>>> related_samples_to_remove = hl.maximal_independent_set(
... pairs_with_case.i, pairs_with_case.j, False, tie_breaker)
>>> result = dataset.filter_cols(hl.is_defined(
... related_samples_to_remove.key_by(
... s = related_samples_to_remove.node.id.s)[dataset.col_key]), keep=False)
Notes
-----
The vertex set of the graph is implicitly all the values realized by `i`
and `j` on the rows of this table. Each row of the table corresponds to an
undirected edge between the vertices given by evaluating `i` and `j` on
that row. An undirected edge may appear multiple times in the table and
will not affect the output. Vertices with self-edges are removed as they
are not independent of themselves.
The expressions for `i` and `j` must have the same type.
The value of `keep` determines whether the vertices returned are those
in the maximal independent set, or those in the complement of this set.
This is useful if you need to filter a table without removing vertices that
don't appear in the graph at all.
This method implements a greedy algorithm which iteratively removes a
vertex of highest degree until the graph contains no edges. The greedy
algorithm always returns an independent set, but the set may not always
be perfectly maximal.
`tie_breaker` is a Python function taking two arguments---say `l` and
`r`---each of which is an :class:`Expression` of the same type as `i` and
`j`. `tie_breaker` returns a :class:`NumericExpression`, which defines an
ordering on nodes. A pair of nodes can be ordered in one of three ways, and
`tie_breaker` must encode the relationship as follows:
- if ``l < r`` then ``tie_breaker`` evaluates to some negative integer
- if ``l == r`` then ``tie_breaker`` evaluates to 0
- if ``l > r`` then ``tie_breaker`` evaluates to some positive integer
For example, the usual ordering on the integers is defined by: ``l - r``.
The `tie_breaker` function must satisfy the following property:
``tie_breaker(l, r) == -tie_breaker(r, l)``.
When multiple nodes have the same degree, this algorithm will order the
nodes according to ``tie_breaker`` and remove the *largest* node.
Parameters
----------
i : :class:`.Expression`
Expression to compute one endpoint of an edge.
j : :class:`.Expression`
Expression to compute another endpoint of an edge.
keep : :obj:`bool`
If ``True``, return vertices in set. If ``False``, return vertices removed.
tie_breaker : function
Function used to order nodes with equal degree.
keyed : :obj:`bool`
If ``True``, key the resulting table by the `node` field, this requires
a sort.
Returns
-------
:class:`.Table`
Table with the set of independent vertices. The table schema is one row
field `node` which has the same type as input expressions `i` and `j`.
"""
if i.dtype != j.dtype:
raise ValueError(
"'maximal_independent_set' expects arguments `i` and `j` to have same type. "
"Found {} and {}.".format(i.dtype, j.dtype))
source = i._indices.source
if not isinstance(source, Table):
raise ValueError(
"'maximal_independent_set' expects an expression of 'Table'. Found {}"
.format("expression of '{}'".format(source.__class__)
if source is not None else 'scalar expression'))
if i._indices.source != j._indices.source:
raise ValueError(
"'maximal_independent_set' expects arguments `i` and `j` to be expressions of the same Table. "
"Found\n{}\n{}".format(i, j))
node_t = i.dtype
if tie_breaker:
wrapped_node_t = ttuple(node_t)
l = construct_variable('l', wrapped_node_t)
r = construct_variable('r', wrapped_node_t)
tie_breaker_expr = hl.int64(tie_breaker(l[0], r[0]))
t, _ = source._process_joins(i, j, tie_breaker_expr)
tie_breaker_str = str(tie_breaker_expr._ir)
else:
t, _ = source._process_joins(i, j)
tie_breaker_str = None
edges = t.select(__i=i, __j=j).key_by().select('__i', '__j')
edges_path = new_temp_file()
edges.write(edges_path)
edges = hl.read_table(edges_path)
mis_nodes = Env.hail().utils.Graph.maximalIndependentSet(
edges._jt.collect(), node_t._parsable_string(),
joption(tie_breaker_str))
nodes = edges.select(node=[edges.__i, edges.__j])
nodes = nodes.explode(nodes.node)
# avoid serializing `mis_nodes` from java to python and back to java
nodes = Table._from_java(
nodes._jt.annotateGlobal(mis_nodes,
hl.tset(node_t)._parsable_string(),
'mis_nodes'))
nodes = nodes.filter(nodes.mis_nodes.contains(nodes.node), keep)
nodes = nodes.select_globals()
if keyed:
return nodes.key_by('node')
return nodes
def prepare_variant_results(table_urls):
annotations = None
analysis_groups = []
for annotations_table_url, results_table_url in table_urls:
group_annotations = hl.import_table(
annotations_table_url,
force=True,
key="v",
missing="NA",
types={
"v": hl.tstr,
"in_analysis": hl.tbool,
"gene_id": hl.tstr,
"gene_name": hl.tstr,
"transcript_id": hl.tstr,
"hgvsc": hl.tstr,
"hgvsp": hl.tstr,
"csq_analysis": hl.tstr,
"csq_worst": hl.tstr,
"mpc": hl.tfloat,
"polyphen": hl.tstr,
},
)
group_results = hl.import_table(
results_table_url,
force=True,
key="v",
missing="NA",
types={
"v": hl.tstr,
"analysis_group": hl.tstr,
"ac_case": hl.tint,
"an_case": hl.tstr,
"af_case": hl.tstr,
"ac_ctrl": hl.tint,
"an_ctrl": hl.tstr,
"af_ctrl": hl.tstr,
},
)
groups_in_table = group_results.aggregate(
hl.agg.collect_as_set(group_results.analysis_group))
assert len(groups_in_table) == 1, groups_in_table
group_name = groups_in_table.pop()
analysis_groups.append(group_name)
group_results = group_results.annotate(
an_case=hl.int(group_results.an_case),
af_case=hl.float(group_results.af_case),
an_ctrl=hl.int(group_results.an_ctrl),
af_ctrl=hl.float(group_results.af_ctrl),
in_analysis=group_annotations[group_results.v].in_analysis,
)
group_results.drop("analysis_group").write(f"temp_{group_name}.ht")
group_annotations = group_annotations.drop("in_analysis")
if annotations is None:
annotations = group_annotations
else:
annotations = annotations.union(group_annotations)
annotations = annotations.distinct()
annotations = annotations.annotate(
filters="PASS",
csq_analysis=hl.sorted(annotations.csq_analysis.split(","),
lambda c: consequence_term_rank(c))[0],
csq_worst=hl.sorted(annotations.csq_worst.split(","),
lambda c: consequence_term_rank(c))[0],
canonical_transcript_id=annotations.transcript_id,
hgvsc_canonical=annotations.hgvsc,
hgvsp_canonical=annotations.hgvsp,
)
annotations = annotations.annotate(
locus=hl.locus(
annotations.v.split(":")[0], hl.int(annotations.v.split(":")[1])),
alleles=annotations.v.split(":")[2:4],
)
annotations = annotations.annotate(
variant_id=variant_id(annotations.locus, annotations.alleles),
chrom=annotations.locus.contig,
pos=annotations.locus.position,
xpos=x_position(annotations.locus),
alt=annotations.alleles[1],
ref=annotations.alleles[0],
)
annotations = annotations.drop("locus", "alleles")
annotations = annotations.annotate(groups=hl.struct())
for group_name in analysis_groups:
results = hl.read_table(f"temp_{group_name}.ht")
annotations = annotations.annotate(groups=annotations.groups.annotate(
**{group_name: results[annotations.key]}))
annotations = annotations.key_by().drop("v")
return annotations
def tx_annotate_mt(mt,
gtex,
tx_annotation_type,
tissues_to_filter=v7_tissues_to_drop,
gene_maximums_ht_path=gtex_v7_gene_maximums_ht_path,
filter_to_csqs=all_coding_csqs,
filter_to_genes=None,
gene_column_in_mt=None,
filter_to_homs=False,
out_tx_annotation_tsv=None,
out_tx_annotation_ht=None):
"""
Annotate variants in the input MatrixTable with transcript-based expression values accross GTEx. Returns Table.
:param MatrixTable mt: Input variant file
:param MatrixTable gtex: Input GTEx summary MatrixTable, must have transcript_id column to key by
:param str tx_annotation_type: One of ["expression", "proportion"]. Select proportion if you'd like the
tx_annotation values to be normalized by max expression of the gene
:param None or list filter_to_csqs: Default None. If you'd like to filter the mt before annotating
(decreases time) feed in a list or set of consequence terms.
:param str gene_column_in_mt: Must be set if filter_to_genes != None.
Column in matrix table that contains gene information within vep.transcript_consequences.
often ["gene_id", "gene_symbol"]
:param None or list filter_to_csqs: Default None. If you'd like to filter the mt before annotating
(decreases time) feed in a list or set of consequence terms.
Example = ["stop_gained","splice_donor_variant", "splice_acceptor_variant","frameshift_variant"]
:param None or str out_tx_annotation_tsv: Default None.
If you'd like to write out the results table as a tsv, provide a tsv path
:param None or str out_tx_annotation_ht: Default None.
If you'd like to write out the results table as a Hail 0.2 table, provide a .ht path
:param bool filter_to_homs: Default False
If True, filter to variants with at least one homozygote in dataset
:return: Table with columns: variant, worst_csq, ensg, LOFTEE LOF, LOFTEE LOF Flag, transcript-aware expression
by GTEx Tissue
:rtype: Table with variants annotated with transcript-aware tissue expression
"""
#check_inputs(**locals())
gtex_table = gtex.key_by("transcript_id")
#mt = process_consequences(mt, penalize_flags=False)
mt_exploded = mt.distinct_by_row()
mt_exploded = mt_exploded.annotate_rows(vep=mt_exploded.vep.annotate(
transcript_consequences=mt_exploded.vep.transcript_consequences.map(
add_most_severe_consequence_to_consequence)))
# Explode the mt for the transcript consequences to be able to key by transcript ID
mt_exploded = mt_exploded.explode_rows(
mt_exploded.vep.transcript_consequences)
mt_kt = mt_exploded.rows()
# Currently testing removal of protein coding transcripts
mt_kt = mt_kt.filter(
mt_kt.vep.transcript_consequences.biotype == "protein_coding")
if filter_to_genes:
print("Filtering to genes of interest")
mt_kt = filter_table_to_gene_list(mt_kt, filter_to_genes,
gene_column_in_mt)
if filter_to_csqs:
print("Filtering to csqs in %s" % (",".join(filter_to_csqs)))
mt_kt = filter_table_to_csqs(mt_kt, filter_to_csqs)
if filter_to_homs:
print(
"Filtering to variants with at least 1 homozygote sample in dataset"
)
#mt_kt = mt_kt.filter(mt_kt.info.Hom[mt_kt.a_index - 1] > 0)
idx = mt_kt.globals.freq_index_dict['gnomad']
mt_kt = mt_kt.filter(mt_kt.freq[idx].homozygote_count >= 1)
# Annotate mt with the gtex values (ie. join them)
mt_kt = mt_kt.annotate(
tx_data=gtex_table[mt_kt.vep.transcript_consequences.transcript_id])
# Group by gene, worst_csq and variant, and do a pairwise-sum
grouped_table = (mt_kt.group_by(
csq=mt_kt.vep.transcript_consequences.most_severe_consequence,
ensg=mt_kt.vep.transcript_consequences.gene_id,
symbol=mt_kt.vep.transcript_consequences.gene_symbol,
locus=mt_kt.locus,
alleles=mt_kt.alleles,
lof=mt_kt.vep.transcript_consequences.lof,
lof_flag=mt_kt.vep.transcript_consequences.lof_flags).aggregate(
tx_annotation=hl.agg.array_sum(mt_kt.tx_data.agg_expression)))
# Expand the columns from the arrays and add tissues as headers
#tissue_ids = gtex.tissue.collect()
# Since gtex no longer has .tissue just a new way to do this, i probably want to save it as a global at some point
tissue_ids = sorted([y.tissue for y in gtex.values.take(1)[0]])
d = {tiss: i for i, tiss in enumerate(tissue_ids)}
tx_annotation_table = grouped_table.annotate(
**{
tissue_id.replace("-", "_").replace(" ", "_").replace("(", "_").
replace(")", "_"): grouped_table.tx_annotation[d[tissue_id]]
for tissue_id in tissue_ids
})
tx_annotation_table = tx_annotation_table.drop(
tx_annotation_table.tx_annotation)
# First of all do you want proportions or expression?
if tx_annotation_type == "proportion":
print("Returning expression proportion")
gene_maximums_ht = hl.read_table(gene_maximums_ht_path)
tx_annotation_table = get_expression_proportion(
tx_annotation_table, tissues_to_filter, gene_maximums_ht)
#You can write the output that is exploded by variants-ensg-csq-symbol-LOFTEE-LOFTEEflag
# and has a value for each tissue as column, either as a TSV or a KT
if out_tx_annotation_tsv:
print("Writing tsv file to %s" % out_tx_annotation_tsv)
tx_annotation_table.export(out_tx_annotation_tsv)
if out_tx_annotation_ht:
print("Writing Table to %s" % out_tx_annotation_ht)
tx_annotation_table.write(out_tx_annotation_ht)
tx_annotation_table = tx_annotation_table.key_by(
tx_annotation_table.locus, tx_annotation_table.alleles)
tx_annotation_table = tx_annotation_table.collect_by_key('tx_annotation')
mt = mt.annotate_rows(**tx_annotation_table[mt.locus, mt.alleles])
return mt
def prepare_mitochondrial_variants(path, mnvs_path=None):
ds = hl.read_table(path)
haplogroups = hl.eval(ds.globals.hap_order)
ds = ds.annotate(hl_hist=ds.hl_hist.annotate(
bin_edges=ds.hl_hist.bin_edges.map(
lambda n: hl.float(hl.format("%.2f", n)))))
filter_names = hl.dict({
"artifact_prone_site": "Artifact-prone site",
"indel_stack": "Indel stack",
"npg": "No passing genotype"
})
ds = ds.select(
# ID
variant_id=variant_id(ds.locus, ds.alleles),
reference_genome=ds.locus.dtype.reference_genome.name,
chrom=normalized_contig(ds.locus.contig),
pos=ds.locus.position,
ref=ds.alleles[0],
alt=ds.alleles[1],
rsid=ds.rsid,
# Quality
filters=ds.filters.map(lambda f: filter_names.get(f, f)),
qual=ds.qual,
genotype_quality_metrics=[
hl.struct(name="Depth", alt=ds.dp_hist_alt, all=ds.dp_hist_all)
],
genotype_quality_filters=[
hl.struct(
name="Base Quality",
filtered=hl.struct(bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.base_qual_hist),
),
hl.struct(
name="Contamination",
filtered=hl.struct(bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.contamination_hist),
),
hl.struct(
name="Heteroplasmy below 10%",
filtered=hl.struct(
bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.heteroplasmy_below_10_percent_hist),
),
hl.struct(name="Position",
filtered=hl.struct(bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.position_hist)),
hl.struct(
name="Strand Bias",
filtered=hl.struct(bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.strand_bias_hist),
),
hl.struct(
name="Weak Evidence",
filtered=hl.struct(bin_edges=ds.hl_hist.bin_edges,
bin_freq=ds.weak_evidence_hist),
),
],
site_quality_metrics=[
hl.struct(name="Mean Depth", value=nullify_nan(ds.dp_mean)),
hl.struct(name="Mean MQ", value=nullify_nan(ds.mq_mean)),
hl.struct(name="Mean TLOD", value=nullify_nan(ds.tlod_mean)),
],
# Frequency
an=ds.AN,
ac_hom=ds.AC_hom,
ac_het=ds.AC_het,
excluded_ac=ds.excluded_AC,
# Heteroplasmy
common_low_heteroplasmy=ds.common_low_heteroplasmy,
heteroplasmy_distribution=ds.hl_hist,
max_heteroplasmy=ds.max_hl,
# Haplogroups
hapmax_af_hom=ds.hapmax_AF_hom,
hapmax_af_het=ds.hapmax_AF_het,
faf_hapmax_hom=ds.faf_hapmax_hom,
haplogroup_defining=ds.hap_defining_variant,
haplogroups=[
hl.struct(
id=haplogroup,
an=ds.hap_AN[i],
ac_het=ds.hap_AC_het[i],
ac_hom=ds.hap_AC_hom[i],
faf_hom=ds.hap_faf_hom[i],
heteroplasmy_distribution=ds.hap_hl_hist[i],
) for i, haplogroup in enumerate(haplogroups)
],
# Other
age_distribution=hl.struct(het=ds.age_hist_het, hom=ds.age_hist_hom),
flags=hl.set([
hl.or_missing(ds.common_low_heteroplasmy,
"common_low_heteroplasmy")
]).filter(hl.is_defined),
mitotip_score=ds.mitotip_score,
mitotip_trna_prediction=ds.mitotip_trna_prediction,
pon_ml_probability_of_pathogenicity=ds.
pon_ml_probability_of_pathogenicity,
pon_mt_trna_prediction=ds.pon_mt_trna_prediction,
variant_collapsed=ds.variant_collapsed,
vep=ds.vep,
)
if mnvs_path:
mnvs = hl.import_table(mnvs_path,
types={
"pos": hl.tint,
"ref": hl.tstr,
"alt": hl.tstr,
"AC_hom_MNV": hl.tint
})
mnvs = mnvs.key_by(
locus=hl.locus("chrM",
mnvs.pos,
reference_genome=ds.locus.dtype.reference_genome),
alleles=[mnvs.ref, mnvs.alt],
)
ds = ds.annotate(ac_hom_mnv=hl.or_else(mnvs[ds.key].AC_hom_MNV, 0))
ds = ds.annotate(
flags=hl.if_else(ds.ac_hom_mnv > 0, ds.flags.add("mnv"), ds.flags))
return ds
# path for Julia's sample metadata file
jul_metadata_path = (
'gs://hgdp_tgp/output/gnomad_v3.1_sample_qc_metadata_hgdp_tgp_subset.ht')
# path for variant qc info
var_metadata_path = 'gs://gcp-public-data--gnomad/release/3.1.1/ht/genomes/gnomad.genomes.v3.1.1.sites.ht'
# path for Konrad's densified matrix table
dense_mt_path = 'gs://hgdp_tgp/output/tgp_hgdp.mt'
# reading in Alicia's sample metadata file (Note: this file uses the 'v3.1::' prefix as done in gnomAD)
sample_meta = hl.import_table(sample_metadata_path, impute=True)
# reading in Julia's sample metadata file
jul_meta = hl.read_table(jul_metadata_path)
# reading in variant qc information
var_meta = hl.read_table(var_metadata_path)
# reading in densified matrix table
dense_mt = hl.read_matrix_table(dense_mt_path)
# These bits below were written by Tim Poterba to help troubleshoot unflattening a ht with nested structure
# dict to hold struct names as well as nested field names
d = {}
# Getting just the row field names
row = sample_meta.row_value
# returns a dict with the struct names as keys and their inner field names as values
def compatible_checkpoint(obj, path):
obj.write(path, overwrite=True)
return hl.read_table(path)
f'Invalid sex argument "{sex}" - must be one of {{"both_sexes", "female", "male"}}.'
)
if contig not in set(['autosomes', 'chrX', 'chrXY']):
raise ValueError(
f'Invalid contig argument "{contig}" - must be one of {{"autosomes", "chrX", "chrXY"}}.'
)
try:
dilution = sys.argv[4]
except:
dilution = False
else:
dilution = True
ht_phenotypes = hl.read_table(
f'gs://ukb31063-mega-gwas/biomarkers/pipelines/ukb31063.biomarkers_gwas.{sex}.pipeline_{pipeline}.ht'
)
ht_covariates = hl.read_table(
f'gs://ukb31063/hail/ukb31063.neale_gwas_covariates.{sex}.ht')
ht_variants = hl.read_table(
'gs://ukb31063/hail/ukb31063.neale_gwas_variants.ht')
if dilution:
ht = hl.read_table(f'gs://ukb31063/hail/ukb31063.biomarkers_gwas.{sex}.ht')
ht = ht.select('estimated_sample_dilution_factor_raw')
ht_covariates = ht_covariates.annotate(estimated_sample_dilution_factor=ht[
ht_covariates.s]['estimated_sample_dilution_factor_raw'])
if contig == 'autosomes':
contig_expr = 'chr{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22}'
else:
def load_dataset(name,
version,
reference_genome,
config_file='gs://hail-datasets/datasets.json'):
"""Load a genetic dataset from Hail's repository.
Example
-------
>>> # Load 1000 Genomes MatrixTable with GRCh38 coordinates
>>> mt_1kg = hl.experimental.load_dataset(name='1000_genomes', # doctest: +SKIP
... version='phase3',
... reference_genome='GRCh38')
Parameters
----------
name : :obj:`str`
Name of the dataset to load.
version : :obj:`str`
Version of the named dataset to load
(see available versions in documentation).
reference_genome : `GRCh37` or `GRCh38`
Reference genome build.
Returns
-------
:class:`.Table` or :class:`.MatrixTable`"""
with hl.hadoop_open(config_file, 'r') as f:
datasets = json.load(f)
names = set([dataset['name'] for dataset in datasets])
if name not in names:
raise ValueError(
'{} is not a dataset available in the repository.'.format(
repr(name)))
versions = set([
dataset['version'] for dataset in datasets if dataset['name'] == name
])
if version not in versions:
raise ValueError("""Version {0} not available for dataset {1}.
Available versions: {{{2}}}.""".format(
repr(version), repr(name), repr('","'.join(versions))))
reference_genomes = set([
dataset['reference_genome'] for dataset in datasets
if dataset['name'] == name
])
if reference_genome not in reference_genomes:
raise ValueError(
"""Reference genome build {0} not available for dataset {1}.
Available reference genome builds: {{'{2}'}}.""".
format(repr(reference_genome), repr(name), '\',\''.join(
(reference_genomes))))
path = [
dataset['path'] for dataset in datasets if all([
dataset['name'] == name, dataset['version'] == version,
dataset['reference_genome'] == reference_genome
])
][0].strip('/')
if path.endswith('.ht'):
dataset = hl.read_table(path)
else:
if not path.endswith('.mt'):
raise ValueError(
'Invalid path {}: can only load datasets with .ht or .mt extensions.'
.format(repr(path)))
dataset = hl.read_matrix_table(path)
return dataset
# need to create spark cluster first before intiialising hail
sc = pyspark.SparkContext()
# Define the hail persistent storage directory
tmp_dir = "hdfs://spark-master:9820/"
temp_dir = "file:///home/ubuntu/data/tmp"
plot_dir = "/home/ubuntu/data/tmp"
hl.init(sc=sc, tmp_dir=tmp_dir, default_reference="GRCh38")
# s3 credentials required for user to access the datasets in farm flexible compute s3 environment
# you may use your own here from your .s3fg file in your home directory
hadoop_config = sc._jsc.hadoopConfiguration()
hadoop_config.set("fs.s3a.access.key", credentials["mer"]["access_key"])
hadoop_config.set("fs.s3a.secret.key", credentials["mer"]["secret_key"])
n_partitions = 500
omni = f'{temp_dir}/ddd-elgh-ukbb/training_sets/1000G_omni2.5.hg38.ht'
omni_ht = hl.read_table(omni)
mills = f'{temp_dir}/ddd-elgh-ukbb/training_sets/Mills_and_1000G_gold_standard.indels.hg38.ht'
mills_ht = hl.read_table(mills)
thousand_genomes = f'{temp_dir}/ddd-elgh-ukbb/training_sets/1000G_phase1.snps.high_confidence.hg38.ht'
thousand_genomes_ht = hl.read_table(thousand_genomes)
hapmap = f'{temp_dir}/ddd-elgh-ukbb/training_sets/hapmap_3.3.hg38.ht'
hapmap_ht = hl.read_table(hapmap)
# ANNOTATION TABLES:
truth_data_ht = hl.read_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/truthset_table.ht')
trio_stats_table = hl.read_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/Sanger_cohorts_trios_stats.ht')
#inbreeding_ht = hl.read_table(f'{temp_dir}/ddd-elgh-ukbb/variant_qc/Sanger_cohorts_inbreeding.ht')
allele_data_ht = hl.read_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/Sanger_cohorts_allele_data.ht')
allele_counts_ht = hl.read_table(
def get_overlapping_phenos(apcdr_ukb, gwas_phenos, gwas_biomarkers,
pheno_table, overwrite):
# get which phenotypes exist in apcdr data
pheno_gwas = hl.import_table(apcdr_ukb)
pheno_gwas = {
row['pheno_code']: row['ukb_code']
for row in pheno_gwas.collect()
}
incl_whr = False
if 'WHR' in pheno_gwas:
del pheno_gwas['WHR']
incl_whr = True
if 'EA' in pheno_gwas:
educ = hl.import_table(
'gs://ukb-diverse-pops/Phenotypes/Everyone/PHESANT_final_output/January_2020_plus_pharma_and_updated_codings/phesant_output_multi_ancestry_combined_both_sexes_no_sex_specific_educ_att_years.tsv',
missing='',
impute=True,
min_partitions=100,
key='userId',
types={
'userId': hl.tstr,
'EDUC_ATT_CAT_ORD': hl.tint
})
# read ukb data
ht_phenotypes = hl.import_table(gwas_phenos,
force_bgz=True,
missing='',
impute=True,
min_partitions=100,
types={'s': hl.tstr},
key='s')
phenotype_cols = set(ht_phenotypes.row)
irnt = []
raw_phenos = []
biomarker = []
for pheno in pheno_gwas.values():
if pheno + '_irnt' in phenotype_cols:
irnt.append(pheno)
elif pheno in phenotype_cols:
raw_phenos.append(pheno)
elif pheno + '_0' in phenotype_cols:
raw_phenos.append(pheno + '_0')
else:
biomarker.append(pheno)
print(pheno_gwas.values())
if incl_whr:
irnt.append('whr')
ht_phenotypes = ht_phenotypes.annotate(whr=ht_phenotypes['48_raw'] /
ht_phenotypes['49_raw'])
ht_phenotypes = irnt_funct(ht_phenotypes.whr, 'whr_irnt').key_by('s')
if 'EA' in pheno_gwas:
ht_phenotypes = ht_phenotypes.annotate(
ea=educ[ht_phenotypes.key].EDUC_ATT_CAT_ORD)
raw_phenos.append('ea')
print('adding EA')
# now select phenotypes that are in apcdr data
ht_phenos = ht_phenotypes.select(*[x + '_irnt' for x in irnt] + raw_phenos)
ht_phenos.show()
# filter biomarkers to codes
biomarkers = [
'cholesterol_irnt', 'hdl_cholesterol_irnt', 'ldl_irnt',
'triglycerides_irnt', 'albumin_irnt', 'alkaline_phosphatase_irnt',
'alanine_aminotransferase_irnt', 'aspartate_aminotransferase_irnt',
'direct_bilirubin_irnt', 'gamma_glutamyltransferase_irnt',
'glycated_haemoglobin_irnt'
]
if gwas_biomarkers:
ht_biomarkers = hl.read_table(gwas_biomarkers).select(*biomarkers)
# now join biomarkers with phenotypes
ht_all_phenos = ht_phenos.join(ht_biomarkers)
ht_all_phenos.write(pheno_table, overwrite=args.overwrite)
else:
ht_phenos.write(pheno_table, overwrite=args.overwrite)
def main(args):
# get phenotypes that overlap with APCDR dataset
if args.write_phenos:
get_overlapping_phenos(args.pheno_ukb_codes, args.gwas_phenos,
args.gwas_biomarkers, args.pheno_table,
args.overwrite)
ht_phenos = hl.read_table(args.pheno_table)
ht_covariates = hl.read_table(args.gwas_covariates)
ht_variants = hl.read_table(args.gwas_variants)
ht_samples = hl.import_table(args.gwas_samples,
types={'s': hl.tstr},
key='s')
contig = 'autosomes'
contig_expr = 'chr{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22}'
# import ukb bgen files
mt = hl.import_bgen(
path=
f'gs://fc-7d5088b4-7673-45b5-95c2-17ae00a04183/imputed/ukb_imp_{contig_expr}_v3.bgen',
sample_file=f'gs://ukb31063/ukb31063.{contig}.sample',
entry_fields=['dosage'],
variants=ht_variants)
# add phenotype and covariate info
mt = mt.annotate_cols(phenotypes=ht_phenos[mt.s],
covariates=ht_covariates[mt.s])
# filter keeping samples in gwas, i.e. exclude holdout samples
# mt = mt.filter_cols(ht_samples[mt.s].in_gwas == 'TRUE')
disc_or_target = 'target'
if disc_or_target == 'target':
mt = mt.filter_cols(ht_samples[mt.s].in_gwas == 'FALSE') # target
else:
mt = mt.filter_cols(ht_samples[mt.s].in_gwas == 'TRUE') # discovery
# mt.write(args.mt, overwrite=args.overwrite)
#
# mt = hl.read_matrix_table(args.mt)
phenotypes = list(mt['phenotypes'].keys())
pheno1 = phenotypes[0:10]
pheno2 = phenotypes[10:20]
pheno3 = phenotypes[20:30]
pheno4 = phenotypes[30:len(phenotypes)]
pheno_leftover = ['whr_irnt', 'glycated_haemoglobin_irnt']
# run_grouped_regressions(mt, args.holdout_ss_output, pheno1, 'pheno1')
# run_grouped_regressions(mt, args.holdout_ss_output, pheno2, 'pheno2')
# run_grouped_regressions(mt, args.holdout_ss_output, pheno3, 'pheno3')
# run_grouped_regressions(mt, args.holdout_ss_output, pheno4, 'pheno4')
# run_grouped_regressions(mt, args.holdout_ss_output, pheno_leftover, 'pheno_leftover')
if disc_or_target == 'target':
run_grouped_regressions(mt, args.holdout_ss_output, phenotypes,
'target_holdout2')
else:
run_grouped_regressions(mt, args.holdout_ss_output, phenotypes,
'discovery')
import argparse
import hail as hl
p = argparse.ArgumentParser()
p.add_argument("--input-url", required=True)
p.add_argument("--genes-url", required=True)
p.add_argument("--output-url", required=True)
args = p.parse_args()
hl.init(log="/tmp/hail.log")
ds = hl.read_table(args.input_url)
ds = ds.annotate(analysis_group="meta")
genes = hl.read_table(args.genes_url)
genes = genes.key_by("gene_id")
ds = ds.annotate(chrom=genes[ds.gene_id].chrom, pos=genes[ds.gene_id].start)
ds.write(args.output_url)
import hail as hl
ht_samples = hl.read_table(
'gs://hail-datasets-hail-data/1000_Genomes_phase3_samples.ht')
ht_relationships = hl.read_table(
'gs://hail-datasets-hail-data/1000_Genomes_phase3_sample_relationships.ht')
mt = hl.import_vcf(
'gs://hail-datasets-raw-data/1000_Genomes/1000_Genomes_phase3_chrY_GRCh37.vcf.bgz',
reference_genome='GRCh37')
mt = mt.annotate_cols(**ht_samples[mt.s])
mt = mt.annotate_cols(**ht_relationships[mt.s])
mt_split = hl.split_multi(mt)
mt_split = mt_split.select_entries(
GT=hl.downcode(mt_split.GT, mt_split.a_index))
mt_split = mt_split.annotate_rows(info=hl.struct(
DP=mt_split.info.DP,
END=mt_split.info.END,
SVTYPE=mt_split.info.SVTYPE,
AA=mt_split.info.AA,
AC=mt_split.info.AC[mt_split.a_index - 1],
AF=mt_split.info.AF[mt_split.a_index - 1],
NS=mt_split.info.NS,
AN=mt_split.info.AN,
EAS_AF=mt_split.info.EAS_AF[mt_split.a_index - 1],
EUR_AF=mt_split.info.EUR_AF[mt_split.a_index - 1],
AFR_AF=mt_split.info.AFR_AF[mt_split.a_index - 1],
AMR_AF=mt_split.info.AMR_AF[mt_split.a_index - 1],
SAS_AF=mt_split.info.SAS_AF[mt_split.a_index - 1],
def main():
print("main")
run_hash = "91ba5f38"
ht=hl.read_table(f'{lustre_dir}/variant_qc/models/{run_hash}_score_binning.ht')
mt = hl.read_matrix_table(
f'{lustre_dir}/MegaWESSanger_cohorts_sampleQC_filtered.mt')
table_cohort = hl.import_table(
f"{lustre_dir}/sanger_cohorts_corrected_ukbb_july_2020.tsv", delimiter="\t").key_by('s')
mt = mt.annotate_cols(cohort=table_cohort[mt.s].cohort)
df = pd.read_csv(
f"{lustre_dir}/sanger_cohorts_corrected_ukbb_july_2020.tsv", sep="\t")
cohorts_array = df.cohort.unique()
mt = mt.annotate_rows(
MAF_cohorts=hl.agg.group_by(mt.cohort,
hl.min(hl.agg.call_stats(mt.GT, mt.alleles).AF))
)
mt = mt.annotate_rows(
AN_cohorts=hl.agg.group_by(mt.cohort,
hl.min(hl.agg.call_stats(mt.GT, mt.alleles).AN))
)
mt = mt.annotate_rows(
AC_cohorts=hl.agg.group_by(mt.cohort,
hl.min(hl.agg.call_stats(mt.GT, mt.alleles).AC))
)
mt = mt.annotate_rows(
missingness_cohorts=hl.agg.group_by(mt.cohort, hl.min(
(hl.agg.count_where(hl.is_missing(mt['GT']))) / mt.count_rows()*2))
)
mt = mt.annotate_rows(
info=mt.info.annotate(cohort_names=mt.MAF_cohorts.keys())
)
mt = mt.annotate_rows(
info=mt.info.annotate(MAF_cohorts_values=mt.MAF_cohorts.values())
)
mt = mt.annotate_rows(
info=mt.info.annotate(AN_cohorts_values=mt.AN_cohorts.values())
)
mt = mt.annotate_rows(
info=mt.info.annotate(AC_cohorts=mt.AC_cohorts.values())
)
mt = mt.annotate_rows(
info=mt.info.annotate(
missingness_cohorts_values=mt.missingness_cohorts.values())
)
mt = mt.annotate_rows(
Variant_Type=hl.cond((hl.is_snp(mt.alleles[0], mt.alleles[1])), "SNP",
hl.cond(
hl.is_insertion(
mt.alleles[0], mt.alleles[1]),
"INDEL",
hl.cond(hl.is_deletion(mt.alleles[0],
mt.alleles[1]), "INDEL",
"Other"))))
mt = mt.annotate_rows(
info=mt.info.annotate(
rf_probability=ht[mt.row_key].rf_probability['TP'])
)
mt = mt.annotate_rows(
info=mt.info.annotate(score=ht[mt.row_key].score)
)
mt = mt.annotate_rows(
info=mt.info.annotate(bin=ht[mt.row_key].bin)
)
filter_column_annotation = (
hl.case()
.when(((mt.Variant_Type == "SNP") & (mt.info.bin <= SNV_PASS_BIN)), "PASS")
.when(((mt.Variant_Type == "INDEL") & (mt.info.bin <= INDEL_PASS_BIN)), "PASS")
.default(".") # not pass for rest
)
# mt_annotated = mt.annotate_rows(mt.filters=filter_column_annotation)
mt1 = mt.annotate_rows(
filtercol=((filter_column_annotation))
)
mt_fail = mt1.filter_rows(mt1.filtercol == ".")
print(mt_fail.count())
mt2 = mt1.annotate_rows(filters=mt1.filters.add(mt1.filtercol))
mt_fail2 = mt2.filter_rows(mt2.filters.contains("."))
mt_pass = mt2.filter_rows(mt2.filters.contains("PASS"))
print(f'Failed:{mt_fail2.count()}')
print(f'Passed:{mt_pass.count()}')
mt2 = mt2.checkpoint(
f'{lustre_dir}/variant_qc/megaWES_final_after_RF_{run_hash}.mt', overwrite=True)
#Remove gt and entries and samples
mt1 = mt2.select_entries()
mt_fin = mt2.filter_cols(mt2['s'] == 'sample')
chroms=[*range(1,23),"X","Y"]
chromosomes=["chr"+ str(chr) for chr in chroms]
for chromosome in chromosomes:
print(chromosome)
mt=mt_fin.filter_rows(mt_fin.locus.contig==chromosome)
mt.write(f'{lustre_dir}/final_matrixtables_VCFs/{chromosome}_after_RF_{run_hash}_NOSAMPLES_GT.mt',overwrite=True)
hl.export_vcf(
mt, f'{lustre_dir}/final_matrixtables_VCFs/VCFs/{chromosome}_after_RF_{run_hash}_LOCI_only',parallel='separate_header')
def compute_from_full_mt(chr20: bool, overwrite: bool):
mt = get_gnomad_data('exomes', adj=True, release_samples=True)
freq_ht = hl.read_table(annotations_ht_path('exomes', 'frequencies'))
vep_ht = hl.read_table(annotations_ht_path('exomes', 'vep'))
rf_ht = hl.read_table(annotations_ht_path('exomes', 'rf'))
if chr20:
mt, freq_ht, vep_ht, rf_ht = filter_to_chr20([mt, freq_ht, vep_ht, rf_ht])
vep_ht = vep_ht.annotate(
vep=get_worst_gene_csq_code_expr(vep_ht.vep).values()
)
freq_ht = freq_ht.select(
freq=freq_ht.freq[:10],
popmax=freq_ht.popmax
)
freq_meta = hl.eval(freq_ht.globals.freq_meta)
freq_dict = {f['pop']: i for i, f in enumerate(freq_meta[:10]) if 'pop' in f}
freq_dict['all'] = 0
freq_dict = hl.literal(freq_dict)
mt = mt.annotate_rows(
**freq_ht[mt.row_key],
vep=vep_ht[mt.row_key].vep,
filters=rf_ht[mt.row_key].filters
)
mt = mt.filter_rows(
(mt.freq[0].AF <= MAX_FREQ) &
(hl.len(mt.vep) > 0) &
(hl.len(mt.filters) == 0)
)
mt = mt.filter_entries(mt.GT.is_non_ref())
mt = mt.select_entries(
is_het=mt.GT.is_het()
)
mt = mt.explode_rows(mt.vep)
mt = mt.transmute_rows(**mt.vep)
mt = mt.annotate_cols(
pop=['all', mt.meta.pop]
)
mt = mt.explode_cols(mt.pop)
mt = mt.group_rows_by(
'gene_id'
).aggregate_rows(
gene_symbol=hl.agg.take(mt.gene_symbol, 1)[0]
).aggregate(
counts=hl.agg.filter(
hl.if_else(
mt.pop == 'all',
hl.is_defined(mt.popmax) & (mt.popmax.AF <= MAX_FREQ),
mt.freq[freq_dict[mt.pop]].AF <= MAX_FREQ
),
hl.agg.group_by(
hl.if_else(
mt.pop == 'all',
mt.popmax.AF > 0.001,
mt.freq[freq_dict[mt.pop]].AF > 0.001
),
hl.struct(
hom_csq=hl.agg.filter(~mt.is_het, hl.agg.min(mt.csq)),
het_csq=hl.agg.filter(mt.is_het, hl.agg.min(mt.csq)),
het_het_csq=hl.sorted(
hl.array(
hl.agg.filter(mt.is_het, hl.agg.counter(mt.csq))
),
key=lambda x: x[0]
).scan(
lambda i, j: (j[0], i[1] + j[1]),
(0, 0)
).find(
lambda x: x[1] > 1
)[0]
)
)
)
)
mt = mt.annotate_entries(
counts=hl.struct(
all=hl.struct(
hom_csq=hl.min(mt.counts.get(True).hom_csq, mt.counts.get(False).hom_csq),
het_csq=hl.min(mt.counts.get(True).het_csq, mt.counts.get(False).het_csq),
het_het_csq=hl.min(
mt.counts.get(True).het_het_csq,
mt.counts.get(False).het_het_csq,
hl.or_missing(
hl.is_defined(mt.counts.get(True).het_csq) & hl.is_defined(mt.counts.get(False).het_csq),
hl.max(mt.counts.get(True).het_csq, mt.counts.get(False).het_csq)
)
),
),
af_le_0_001=mt.counts.get(False)
)
)
mt = mt.checkpoint('gs://gnomad-tmp/compound_hets/het_and_hom_per_gene{}.1.mt'.format(
'.chr20' if chr20 else ''
), overwrite=True)
gene_ht = mt.annotate_rows(
row_counts=hl.flatten([
hl.array(
hl.agg.group_by(
mt.pop,
hl.struct(
csq=csq,
af=af,
n_hom=hl.agg.count_where(mt.counts[af].hom_csq == csq_i),
n_het=hl.agg.count_where(mt.counts[af].het_csq == csq_i),
n_het_het=hl.agg.count_where(mt.counts[af].het_het_csq == csq_i)
)
)
).filter(
lambda x: (x[1].n_het > 0) | (x[1].n_hom > 0) | (x[1].n_het_het > 0)
).map(
lambda x: x[1].annotate(
pop=x[0]
)
)
for csq_i, csq in enumerate(CSQ_CODES)
for af in ['all', 'af_le_0_001']
])
).rows()
gene_ht = gene_ht.explode('row_counts')
gene_ht = gene_ht.select(
'gene_symbol',
**gene_ht.row_counts
)
gene_ht.describe()
gene_ht = gene_ht.checkpoint(
'gs://gnomad-lfran/compound_hets/het_and_hom_per_gene{}.ht'.format(
'.chr20' if chr20 else ''
),
overwrite=overwrite
)
gene_ht.flatten().export('gs://gnomad-lfran/compound_hets/het_and_hom_per_gene{}.tsv.gz'.format(
'.chr20' if chr20 else ''
))
def main(args):
# Set paths for data access based on command line parameters
root = './data'
context_ht_path = f'{root}/context/Homo_sapiens_assembly19.fasta.snps_only.vep_20181129.ht'
processed_genomes_ht_path = f'{root}/model/genomes_processed.ht'
processed_exomes_ht_path = f'{root}/model/exomes_processed.ht'
mutation_rate_ht_path = f'{root}/model/mutation_rate_methylation_bins.ht'
po_coverage_ht_path = f'{root}/model/prop_observed_by_coverage_no_common_pass_filtered_bins.ht'
po_ht_path = f'{root}/{{subdir}}/prop_observed_{{subdir}}.ht'
raw_constraint_ht_path = f'{root}/{{subdir}}/constraint_{{subdir}}.ht'
final_constraint_ht_path = f'{root}/{{subdir}}/constraint_final_{{subdir}}.ht'
possible_variants_ht_path = f'{root}/model/possible_data/possible_transcript_pop_{args.model}.ht'
po_output_path = po_ht_path.format(subdir=args.model)
output_path = raw_constraint_ht_path.format(subdir=args.model)
final_path = final_constraint_ht_path.format(subdir=args.model)
# Sets method for aggregation, will need to be changed for custom analysis
MODEL_KEYS = {
'worst_csq': ['gene'],
'tx_annotation': ['gene', 'expressed'],
'standard': ['gene', 'transcript', 'canonical']
}
if args.test:
ht = load_or_import_po(po_output_path, args.overwrite)
run_tests(ht)
if args.get_proportion_observed:
# Build a model for methylation-dependent mutation rate and apply it to get proportion of variants observed
# Also need to incorporate genomes and v3 if possible
print('Running aggregation of variants by grouping variables')
# Tables of observed mutations in exomes
full_exome_ht = prepare_ht(hl.read_table(processed_exomes_ht_path), args.trimers)
# filter into X, Y and autosomal regions
exome_ht = full_exome_ht.filter(full_exome_ht.locus.in_autosome_or_par())
exome_x_ht = hl.filter_intervals(full_exome_ht, [hl.parse_locus_interval('X')])
exome_x_ht = exome_x_ht.filter(exome_x_ht.locus.in_x_nonpar())
exome_y_ht = hl.filter_intervals(full_exome_ht, [hl.parse_locus_interval('Y')])
exome_y_ht = exome_y_ht.filter(exome_y_ht.locus.in_y_nonpar())
# Modelling results of estimated mutation rates for genes, coverage, methylation level and base context
possible_variants_ht = hl.read_table(possible_variants_ht_path)
# Set chosen groupings to aggregate on
groupings = ['gene','annotation','modifier']
# Apply model; aggregate by chosen groupings & get proportion observed; write to file
po_exome_ht, po_exome_x_ht, po_exome_y_ht = \
[get_proportion_observed(ht, possible_variants_ht, groupings) for ht in (exome_ht, exome_x_ht, exome_y_ht)]
po_exome_ht.write(po_output_path, overwrite=args.overwrite)
po_exome_x_ht.write(po_output_path.replace('.ht', '_x.ht'), overwrite=args.overwrite)
po_exome_y_ht.write(po_output_path.replace('.ht', '_y.ht'), overwrite=args.overwrite)
if args.aggregate:
print('Running aggregation by gene')
# read PO hail tables for autosomes, X and Y chromosomes and join them
print(f'Reading hail table from {po_output_path}')
# Autosomes
ht = load_or_import(po_output_path, args.overwrite)
# X chromosome
ht_x = load_or_import(po_output_path.replace('.ht','_x.ht'), args.overwrite)
# Y chromosome
ht_y = load_or_import(po_output_path.replace('.ht','_y.ht'), args.overwrite)
# Combine into one table
ht = ht.union(ht_x).union(ht_y)
# group by gene/transcript and calculate summary stats
ht = finalize_dataset(ht, keys=MODEL_KEYS[args.model])
# write hail table to output path
ht.write(output_path, args.overwrite)
hl.read_table(output_path).export(output_path.replace('.ht', '.txt.bgz'))
if args.summarise:
print('Finalising summary stats')
# write summary stats to output path
ht = load_or_import(output_path, args.overwrite)
mut_types = ('lof', 'mis', 'syn','mis_pphen','mis_non_pphen')
output_var_types = zip(('obs', 'exp', 'oe', 'oe', 'oe'),
('', '', '', '_lower', '_upper'))
output_vars = product(mut_types,output_var_types)
ht.select(
'gene','transcript','canonical',
*[f'{t}_{m}{ci}' for m, (t, ci) in output_vars],
#*[f'{m}_z' for m in mut_types[:3]],
'pLI', 'pRec', 'pNull',
#gene_issues=ht.constraint_flag
).select_globals().write(final_path, overwrite=args.overwrite)
hl.read_table(final_path).export(final_path.replace('.ht', '.txt.bgz'))
def compute_from_vp_mt(chr20: bool, overwrite: bool):
meta = get_gnomad_meta('exomes')
vp_mt = hl.read_matrix_table(full_mt_path('exomes'))
vp_mt = vp_mt.filter_cols(meta[vp_mt.col_key].release)
ann_ht = hl.read_table(vp_ann_ht_path('exomes'))
phase_ht = hl.read_table(phased_vp_count_ht_path('exomes'))
if chr20:
vp_mt, ann_ht, phase_ht = filter_to_chr20([vp_mt, ann_ht, phase_ht])
vep1_expr = get_worst_gene_csq_code_expr(ann_ht.vep1)
vep2_expr = get_worst_gene_csq_code_expr(ann_ht.vep2)
ann_ht = ann_ht.select(
'snv1',
'snv2',
is_singleton_vp=(ann_ht.freq1['all'].AC < 2) & (ann_ht.freq2['all'].AC < 2),
pop_af=hl.dict(
ann_ht.freq1.key_set().intersection(ann_ht.freq2.key_set())
.map(
lambda pop: hl.tuple([pop, hl.max(ann_ht.freq1[pop].AF, ann_ht.freq2[pop].AF)])
)
),
popmax_af=hl.max(ann_ht.popmax1.AF, ann_ht.popmax2.AF, filter_missing=False),
filtered=(hl.len(ann_ht.filters1) > 0) | (hl.len(ann_ht.filters2) > 0),
vep=vep1_expr.keys().filter(
lambda k: vep2_expr.contains(k)
).map(
lambda k: vep1_expr[k].annotate(
csq=hl.max(vep1_expr[k].csq, vep2_expr[k].csq)
)
)
)
vp_mt = vp_mt.annotate_cols(
pop=meta[vp_mt.col_key].pop
)
vp_mt = vp_mt.annotate_rows(
**ann_ht[vp_mt.row_key],
phase_info=phase_ht[vp_mt.row_key].phase_info
)
vp_mt = vp_mt.filter_rows(
~vp_mt.filtered
)
vp_mt = vp_mt.filter_entries(
vp_mt.GT1.is_het() & vp_mt.GT2.is_het() & vp_mt.adj1 & vp_mt.adj2
)
vp_mt = vp_mt.select_entries(
x=True
)
vp_mt = vp_mt.annotate_cols(
pop=['all', vp_mt.pop]
)
vp_mt = vp_mt.explode_cols('pop')
vp_mt = vp_mt.explode_rows('vep')
vp_mt = vp_mt.transmute_rows(
**vp_mt.vep
)
def get_grouped_phase_agg():
return hl.agg.group_by(
hl.case()
.when(~vp_mt.is_singleton_vp & (vp_mt.phase_info[vp_mt.pop].em.adj.p_chet > CHET_THRESHOLD), 1)
.when(~vp_mt.is_singleton_vp & (vp_mt.phase_info[vp_mt.pop].em.adj.p_chet < SAME_HAP_THRESHOLD), 2)
.default(3)
,
hl.agg.min(vp_mt.csq)
)
vp_mt = vp_mt.group_rows_by(
'gene_id',
'gene_symbol'
).aggregate(
all=hl.agg.filter(
vp_mt.x &
hl.if_else(
vp_mt.pop == 'all',
hl.is_defined(vp_mt.popmax_af) &
(vp_mt.popmax_af <= MAX_FREQ),
vp_mt.pop_af[vp_mt.pop] <= MAX_FREQ
),
get_grouped_phase_agg()
),
af_le_0_001=hl.agg.filter(
hl.if_else(
vp_mt.pop == 'all',
hl.is_defined(vp_mt.popmax_af) &
(vp_mt.popmax_af <= 0.001),
vp_mt.pop_af[vp_mt.pop] <= 0.001
)
& vp_mt.x,
get_grouped_phase_agg()
)
)
vp_mt = vp_mt.checkpoint('gs://gnomad-tmp/compound_hets/chet_per_gene{}.2.mt'.format(
'.chr20' if chr20 else ''
), overwrite=True)
gene_ht = vp_mt.annotate_rows(
row_counts=hl.flatten([
hl.array(
hl.agg.group_by(
vp_mt.pop,
hl.struct(
csq=csq,
af=af,
# TODO: Review this
# These will only kept the worst csq -- now maybe it'd be better to keep either
# - the worst csq for chet or
# - the worst csq for both chet and same_hap
n_worst_chet=hl.agg.count_where(vp_mt[af].get(1) == csq_i),
n_chet=hl.agg.count_where((vp_mt[af].get(1) == csq_i) & (vp_mt[af].get(2, 9) >= csq_i) & (vp_mt[af].get(3, 9) >= csq_i)),
n_same_hap=hl.agg.count_where((vp_mt[af].get(2) == csq_i) & (vp_mt[af].get(1, 9) > csq_i) & (vp_mt[af].get(3, 9) >= csq_i)),
n_unphased=hl.agg.count_where((vp_mt[af].get(3) == csq_i) & (vp_mt[af].get(1, 9) > csq_i) & (vp_mt[af].get(2, 9) > csq_i))
)
)
).filter(
lambda x: (x[1].n_chet > 0) | (x[1].n_same_hap > 0) | (x[1].n_unphased > 0)
).map(
lambda x: x[1].annotate(
pop=x[0]
)
)
for csq_i, csq in enumerate(CSQ_CODES)
for af in ['all', 'af_le_0_001']
])
).rows()
gene_ht = gene_ht.explode('row_counts')
gene_ht = gene_ht.select(
**gene_ht.row_counts
)
gene_ht.describe()
gene_ht = gene_ht.checkpoint(
'gs://gnomad-lfran/compound_hets/chet_per_gene{}.ht'.format(
'.chr20' if chr20 else ''
),
overwrite=overwrite
)
gene_ht.flatten().export(
'gs://gnomad-lfran/compound_hets/chet_per_gene{}.tsv.gz'.format(
'.chr20' if chr20 else ''
)
)
def get_baselevel_expression_for_genes(
mt,
gtex,
gene_list=None,
get_proportions=None,
gene_maximums_ht_path=gtex_v7_gene_maximums_ht_path):
gtex_table = gtex.key_by("transcript_id")
if gene_list:
genes = hl.literal(gene_list)
# Filter context_ht to genes of interest
mt = mt.annotate_rows(in_gene_of_interest=genes.find(
lambda x: mt.vep.transcript_consequences.any(lambda tc: tc.
gene_symbol == x)))
mt = mt.filter_rows(mt.in_gene_of_interest != "NA")
# Need to modify process consequences to ignore splice variants, because these can occur on intronic regions
all_coding_minus_splice = list(
set(all_coding_csqs) - set([
'splice_acceptor_variant', 'splice_donor_variant',
'splice_region_variant'
]))
def add_most_severe_consequence_to_consequence_minus_splice(
tc: hl.expr.StructExpression) -> hl.expr.StructExpression:
"""
Copied from gnomad_hail but slight change
"""
csqs = hl.literal(all_coding_minus_splice)
return tc.annotate(most_severe_consequence=csqs.find(
lambda c: tc.consequence_terms.contains(c)))
# Add worst consequence within transcript consequences
mt = (mt.annotate_rows(vep=mt.vep.annotate(
transcript_consequences=mt.vep.transcript_consequences.map(
add_most_severe_consequence_to_consequence_minus_splice))))
# Explode on transcript consequences
mt = mt.explode_rows(mt.vep.transcript_consequences)
mt_kt = mt.rows()
# Filter to positions in the CDS regions
cds_intervals = hl.import_bed(
"gs://gnomad-public/papers/2019-tx-annotation/data/other_data/gencode.v19.CDS.Hail.021519.bed"
)
mt_kt = mt_kt.annotate(in_cds=hl.is_defined(cds_intervals[mt_kt.locus]))
mt_kt = mt_kt.filter(mt_kt.in_cds)
# Filter to protein coding transcripts only
mt_kt = mt_kt.filter(
mt_kt.vep.transcript_consequences.biotype == "protein_coding")
# Filter to coding variants to only evalute those effects
mt_kt = filter_table_to_csqs(mt_kt, all_coding_minus_splice)
# To avoid double counting transcripts at a given base, key by transcript and position and dedup
mt_kt = mt_kt.key_by(mt_kt.locus,
mt_kt.vep.transcript_consequences.transcript_id)
mt_kt = mt_kt.distinct()
# Annotate mt with the gtex values (ie. join them)
mt_kt = mt_kt.annotate(
tx_data=gtex_table[mt_kt.vep.transcript_consequences.transcript_id])
## Group by gene, symbol and position
ht_sum_of_bases = mt_kt.group_by(
locus=mt_kt.locus,
ensg=mt_kt.vep.transcript_consequences.gene_id,
symbol=mt_kt.vep.transcript_consequences.gene_symbol).aggregate(
sum_per_base=hl.agg.array_sum(mt_kt.tx_data.agg_expression))
tissue_ids = sorted([
y.tissue.replace("-", "_").replace(" ",
"_").replace("(",
"_").replace(")", "_")
for y in gtex.values.take(1)[0]
])
d = {tiss: i for i, tiss in enumerate(tissue_ids)}
ht_sum_of_bases = ht_sum_of_bases.annotate(**{
tissue: ht_sum_of_bases.sum_per_base[d[tissue]]
for tissue in tissue_ids
})
if get_proportions:
gene_maximums_ht = hl.read_table(gene_maximums_ht_path)
ht_sum_of_bases = ht_sum_of_bases.key_by(ht_sum_of_bases.locus)
ht_sum_of_bases = ht_sum_of_bases.annotate(alleles="filler")
ht_sum_of_bases = get_expression_proportion(
tx_table=ht_sum_of_bases,
tissues_to_filter=["sum_per_base"],
gene_maximum_ht=gene_maximums_ht)
ht_sum_of_bases = ht_sum_of_bases.key_by(ht_sum_of_bases.locus)
ht_sum_of_bases = ht_sum_of_bases.drop(ht_sum_of_bases.alleles)
return ht_sum_of_bases
hl.init(sc=sc, tmp_dir=tmp_dir, default_reference="GRCh38")
# s3 credentials required for user to access the datasets in farm flexible compute s3 environment
# you may use your own here from your .s3fg file in your home directory
hadoop_config = sc._jsc.hadoopConfiguration()
hadoop_config.set("fs.s3a.access.key", credentials["mer"]["access_key"])
hadoop_config.set("fs.s3a.secret.key", credentials["mer"]["secret_key"])
bed_to_exclude_pca = hl.import_bed(
f"{temp_dir}/1000g/price_high_ld.bed.txt", reference_genome='GRCh38')
cohorts_pop = hl.import_table(
"s3a://DDD-ELGH-UKBB-exomes/ancestry/sanger_cohort_known_populations_ukbb_elgh_labels.tsv",
delimiter="\t").key_by('s')
pca_scores = hl.read_table(
f"{temp_dir}/ddd-elgh-ukbb/elgh_labels/pop_assignments_test.ht")
# pca_loadings = hl.read_table(f"{temp_dir}/ddd-elgh-ukbb/pca_loadings.ht")
logger.info("assign population pcs")
# population_assignment_table = assign_population_pcs(
# pca_scores, pca_loadings, known_col="known_pop")
pop_ht, pop_clf = assign_population_pcs(pca_scores,
pca_scores.pca_scores,
known_col="known_pop",
n_estimators=100,
prop_train=0.8,
min_prob=0.5)
pop_ht.write(
f"{tmp_dir}/ddd-elgh-ukbb/pop_assignments_test_minprob_0.5.ht",
overwrite=True)
pop_ht.export(
def read_clump_ht(f):
ht = hl.read_table(f)
ht = ht.drop('idx')
return ht
'locus':
hl.tlocus(reference_genome='GRCh38'),
'alleles':
hl.tarray(hl.tstr)
})
ht_final_variants = ht_final_variants.key_by(ht_final_variants.locus,
ht_final_variants.alleles)
ht_final_pruned_variants = hl.import_table(FINAL_PRUNED_VARIANTS,
no_header=True)
ht_final_pruned_variants = ht_final_pruned_variants.annotate(
**hl.parse_variant(ht_final_pruned_variants.f0, reference_genome='GRCh38'))
ht_final_pruned_variants = ht_final_pruned_variants.key_by(
ht_final_pruned_variants.locus, ht_final_pruned_variants.alleles)
sample_annotations = hl.read_table(PHENOTYPES_TABLE)
impute_sex_annotations = hl.read_table(IMPUTESEX_TABLE)
annotation_annotations = hl.read_table(ANNOTATION_TABLE)
mt = hl.read_matrix_table(MT)
mt = mt.drop('a_index', 'qual', 'info', 'filters', 'was_split')
mt = mt.filter_cols(hl.is_defined(ht_final_samples[mt.col_key]))
mt = mt.filter_rows(hl.is_defined(ht_final_variants[mt.row_key]))
mt = mt.annotate_cols(phenotype=sample_annotations[mt.col_key])
mt = mt.annotate_cols(imputesex=impute_sex_annotations[mt.col_key])
mt = mt.annotate_rows(annotation=annotation_annotations[mt.row_key])
mt = hl.variant_qc(mt, name='qc')
def ld_score_regression(weight_expr,
ld_score_expr,
chi_sq_exprs,
n_samples_exprs,
n_blocks=200,
two_step_threshold=30,
n_reference_panel_variants=None) -> Table:
r"""Estimate SNP-heritability and level of confounding biases from genome-wide association study
(GWAS) summary statistics.
Given a set or multiple sets of GWAS summary statistics, :func:`.ld_score_regression` estimates the heritability
of a trait or set of traits and the level of confounding biases present in
the underlying studies by regressing chi-squared statistics on LD scores,
leveraging the model:
.. math::
\mathrm{E}[\chi_j^2] = 1 + Na + \frac{Nh_g^2}{M}l_j
* :math:`\mathrm{E}[\chi_j^2]` is the expected chi-squared statistic
for variant :math:`j` resulting from a test of association between
variant :math:`j` and a trait.
* :math:`l_j = \sum_{k} r_{jk}^2` is the LD score of variant
:math:`j`, calculated as the sum of squared correlation coefficients
between variant :math:`j` and nearby variants. See :func:`ld_score`
for further details.
* :math:`a` captures the contribution of confounding biases, such as
cryptic relatedness and uncontrolled population structure, to the
association test statistic.
* :math:`h_g^2` is the SNP-heritability, or the proportion of variation
in the trait explained by the effects of variants included in the
regression model above.
* :math:`M` is the number of variants used to estimate :math:`h_g^2`.
* :math:`N` is the number of samples in the underlying association study.
For more details on the method implemented in this function, see:
* `LD Score regression distinguishes confounding from polygenicity in genome-wide association studies (Bulik-Sullivan et al, 2015) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495769/>`__
Examples
--------
Run the method on a matrix table of summary statistics, where the rows
are variants and the columns are different phenotypes:
>>> mt_gwas = ld_score_all_phenos_sumstats
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=mt_gwas['ld_score'],
... ld_score_expr=mt_gwas['ld_score'],
... chi_sq_exprs=mt_gwas['chi_squared'],
... n_samples_exprs=mt_gwas['n'])
Run the method on a table with summary statistics for a single
phenotype:
>>> ht_gwas = ld_score_one_pheno_sumstats
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=ht_gwas['ld_score'],
... ld_score_expr=ht_gwas['ld_score'],
... chi_sq_exprs=ht_gwas['chi_squared_50_irnt'],
... n_samples_exprs=ht_gwas['n_50_irnt'])
Run the method on a table with summary statistics for multiple
phenotypes:
>>> ht_gwas = ld_score_one_pheno_sumstats
>>> ht_results = hl.experimental.ld_score_regression(
... weight_expr=ht_gwas['ld_score'],
... ld_score_expr=ht_gwas['ld_score'],
... chi_sq_exprs=[ht_gwas['chi_squared_50_irnt'],
... ht_gwas['chi_squared_20160']],
... n_samples_exprs=[ht_gwas['n_50_irnt'],
... ht_gwas['n_20160']])
Notes
-----
The ``exprs`` provided as arguments to :func:`.ld_score_regression`
must all be from the same object, either a :class:`Table` or a
:class:`MatrixTable`.
**If the arguments originate from a table:**
* The table must be keyed by fields ``locus`` of type
:class:`.tlocus` and ``alleles``, a :py:data:`.tarray` of
:py:data:`.tstr` elements.
* ``weight_expr``, ``ld_score_expr``, ``chi_sq_exprs``, and
``n_samples_exprs`` are must be row-indexed fields.
* The number of expressions passed to ``n_samples_exprs`` must be
equal to one or the number of expressions passed to
``chi_sq_exprs``. If just one expression is passed to
``n_samples_exprs``, that sample size expression is assumed to
apply to all sets of statistics passed to ``chi_sq_exprs``.
Otherwise, the expressions passed to ``chi_sq_exprs`` and
``n_samples_exprs`` are matched by index.
* The ``phenotype`` field that keys the table returned by
:func:`.ld_score_regression` will have generic :obj:`int` values
``0``, ``1``, etc. corresponding to the ``0th``, ``1st``, etc.
expressions passed to the ``chi_sq_exprs`` argument.
**If the arguments originate from a matrix table:**
* The dimensions of the matrix table must be variants
(rows) by phenotypes (columns).
* The rows of the matrix table must be keyed by fields
``locus`` of type :class:`.tlocus` and ``alleles``,
a :py:data:`.tarray` of :py:data:`.tstr` elements.
* The columns of the matrix table must be keyed by a field
of type :py:data:`.tstr` that uniquely identifies phenotypes
represented in the matrix table. The column key must be a single
expression; compound keys are not accepted.
* ``weight_expr`` and ``ld_score_expr`` must be row-indexed
fields.
* ``chi_sq_exprs`` must be a single entry-indexed field
(not a list of fields).
* ``n_samples_exprs`` must be a single entry-indexed field
(not a list of fields).
* The ``phenotype`` field that keys the table returned by
:func:`.ld_score_regression` will have values corresponding to the
column keys of the input matrix table.
This function returns a :class:`Table` with one row per set of summary
statistics passed to the ``chi_sq_exprs`` argument. The following
row-indexed fields are included in the table:
* **phenotype** (:py:data:`.tstr`) -- The name of the phenotype. The
returned table is keyed by this field. See the notes below for
details on the possible values of this field.
* **mean_chi_sq** (:py:data:`.tfloat64`) -- The mean chi-squared
test statistic for the given phenotype.
* **intercept** (`Struct`) -- Contains fields:
- **estimate** (:py:data:`.tfloat64`) -- A point estimate of the
intercept :math:`1 + Na`.
- **standard_error** (:py:data:`.tfloat64`) -- An estimate of
the standard error of this point estimate.
* **snp_heritability** (`Struct`) -- Contains fields:
- **estimate** (:py:data:`.tfloat64`) -- A point estimate of the
SNP-heritability :math:`h_g^2`.
- **standard_error** (:py:data:`.tfloat64`) -- An estimate of
the standard error of this point estimate.
Warning
-------
:func:`.ld_score_regression` considers only the rows for which both row
fields ``weight_expr`` and ``ld_score_expr`` are defined. Rows with missing
values in either field are removed prior to fitting the LD score
regression model.
Parameters
----------
weight_expr : :class:`.Float64Expression`
Row-indexed expression for the LD scores used to derive
variant weights in the model.
ld_score_expr : :class:`.Float64Expression`
Row-indexed expression for the LD scores used as covariates
in the model.
chi_sq_exprs : :class:`.Float64Expression` or :obj:`list` of
:class:`.Float64Expression`
One or more row-indexed (if table) or entry-indexed
(if matrix table) expressions for chi-squared
statistics resulting from genome-wide association
studies (GWAS).
n_samples_exprs: :class:`.NumericExpression` or :obj:`list` of
:class:`.NumericExpression`
One or more row-indexed (if table) or entry-indexed
(if matrix table) expressions indicating the number of
samples used in the studies that generated the test
statistics supplied to ``chi_sq_exprs``.
n_blocks : :obj:`int`
The number of blocks used in the jackknife approach to
estimating standard errors.
two_step_threshold : :obj:`int`
Variants with chi-squared statistics greater than this
value are excluded in the first step of the two-step
procedure used to fit the model.
n_reference_panel_variants : :obj:`int`, optional
Number of variants used to estimate the
SNP-heritability :math:`h_g^2`.
Returns
-------
:class:`.Table`
Table keyed by ``phenotype`` with intercept and heritability estimates
for each phenotype passed to the function."""
chi_sq_exprs = wrap_to_list(chi_sq_exprs)
n_samples_exprs = wrap_to_list(n_samples_exprs)
assert ((len(chi_sq_exprs) == len(n_samples_exprs))
or (len(n_samples_exprs) == 1))
__k = 2 # number of covariates, including intercept
ds = chi_sq_exprs[0]._indices.source
analyze('ld_score_regression/weight_expr',
weight_expr,
ds._row_indices)
analyze('ld_score_regression/ld_score_expr',
ld_score_expr,
ds._row_indices)
# format input dataset
if isinstance(ds, MatrixTable):
if len(chi_sq_exprs) != 1:
raise ValueError("""Only one chi_sq_expr allowed if originating
from a matrix table.""")
if len(n_samples_exprs) != 1:
raise ValueError("""Only one n_samples_expr allowed if
originating from a matrix table.""")
col_key = list(ds.col_key)
if len(col_key) != 1:
raise ValueError("""Matrix table must be keyed by a single
phenotype field.""")
analyze('ld_score_regression/chi_squared_expr',
chi_sq_exprs[0],
ds._entry_indices)
analyze('ld_score_regression/n_samples_expr',
n_samples_exprs[0],
ds._entry_indices)
ds = ds._select_all(row_exprs={'__locus': ds.locus,
'__alleles': ds.alleles,
'__w_initial': weight_expr,
'__w_initial_floor': hl.max(weight_expr,
1.0),
'__x': ld_score_expr,
'__x_floor': hl.max(ld_score_expr,
1.0)},
row_key=['__locus', '__alleles'],
col_exprs={'__y_name': ds[col_key[0]]},
col_key=['__y_name'],
entry_exprs={'__y': chi_sq_exprs[0],
'__n': n_samples_exprs[0]})
ds = ds.annotate_entries(**{'__w': ds.__w_initial})
ds = ds.filter_rows(hl.is_defined(ds.__locus)
& hl.is_defined(ds.__alleles)
& hl.is_defined(ds.__w_initial)
& hl.is_defined(ds.__x))
else:
assert isinstance(ds, Table)
for y in chi_sq_exprs:
analyze('ld_score_regression/chi_squared_expr', y, ds._row_indices)
for n in n_samples_exprs:
analyze('ld_score_regression/n_samples_expr', n, ds._row_indices)
ys = ['__y{:}'.format(i) for i, _ in enumerate(chi_sq_exprs)]
ws = ['__w{:}'.format(i) for i, _ in enumerate(chi_sq_exprs)]
ns = ['__n{:}'.format(i) for i, _ in enumerate(n_samples_exprs)]
ds = ds.select(**dict(**{'__locus': ds.locus,
'__alleles': ds.alleles,
'__w_initial': weight_expr,
'__x': ld_score_expr},
**{y: chi_sq_exprs[i]
for i, y in enumerate(ys)},
**{w: weight_expr for w in ws},
**{n: n_samples_exprs[i]
for i, n in enumerate(ns)}))
ds = ds.key_by(ds.__locus, ds.__alleles)
table_tmp_file = new_temp_file()
ds.write(table_tmp_file)
ds = hl.read_table(table_tmp_file)
hts = [ds.select(**{'__w_initial': ds.__w_initial,
'__w_initial_floor': hl.max(ds.__w_initial,
1.0),
'__x': ds.__x,
'__x_floor': hl.max(ds.__x, 1.0),
'__y_name': i,
'__y': ds[ys[i]],
'__w': ds[ws[i]],
'__n': hl.int(ds[ns[i]])})
for i, y in enumerate(ys)]
mts = [ht.to_matrix_table(row_key=['__locus',
'__alleles'],
col_key=['__y_name'],
row_fields=['__w_initial',
'__w_initial_floor',
'__x',
'__x_floor'])
for ht in hts]
ds = mts[0]
for i in range(1, len(ys)):
ds = ds.union_cols(mts[i])
ds = ds.filter_rows(hl.is_defined(ds.__locus)
& hl.is_defined(ds.__alleles)
& hl.is_defined(ds.__w_initial)
& hl.is_defined(ds.__x))
mt_tmp_file1 = new_temp_file()
ds.write(mt_tmp_file1)
mt = hl.read_matrix_table(mt_tmp_file1)
if not n_reference_panel_variants:
M = mt.count_rows()
else:
M = n_reference_panel_variants
mt = mt.annotate_entries(__in_step1=(hl.is_defined(mt.__y)
& (mt.__y < two_step_threshold)),
__in_step2=hl.is_defined(mt.__y))
mt = mt.annotate_cols(__col_idx=hl.int(hl.scan.count()),
__m_step1=hl.agg.count_where(mt.__in_step1),
__m_step2=hl.agg.count_where(mt.__in_step2))
col_keys = list(mt.col_key)
ht = mt.localize_entries(entries_array_field_name='__entries',
columns_array_field_name='__cols')
ht = ht.annotate(__entries=hl.rbind(
hl.scan.array_agg(
lambda entry: hl.scan.count_where(entry.__in_step1),
ht.__entries),
lambda step1_indices: hl.map(
lambda i: hl.rbind(
hl.int(hl.or_else(step1_indices[i], 0)),
ht.__cols[i].__m_step1,
ht.__entries[i],
lambda step1_idx, m_step1, entry: hl.rbind(
hl.map(
lambda j: hl.int(hl.floor(j * (m_step1 / n_blocks))),
hl.range(0, n_blocks + 1)),
lambda step1_separators: hl.rbind(
hl.set(step1_separators).contains(step1_idx),
hl.sum(
hl.map(
lambda s1: step1_idx >= s1,
step1_separators)) - 1,
lambda is_separator, step1_block: entry.annotate(
__step1_block=step1_block,
__step2_block=hl.cond(~entry.__in_step1 & is_separator,
step1_block - 1,
step1_block))))),
hl.range(0, hl.len(ht.__entries)))))
mt = ht._unlocalize_entries('__entries', '__cols', col_keys)
mt_tmp_file2 = new_temp_file()
mt.write(mt_tmp_file2)
mt = hl.read_matrix_table(mt_tmp_file2)
# initial coefficient estimates
mt = mt.annotate_cols(__initial_betas=[
1.0, (hl.agg.mean(mt.__y) - 1.0) / hl.agg.mean(mt.__x)])
mt = mt.annotate_cols(__step1_betas=mt.__initial_betas,
__step2_betas=mt.__initial_betas)
# step 1 iteratively reweighted least squares
for i in range(3):
mt = mt.annotate_entries(__w=hl.cond(
mt.__in_step1,
1.0 / (mt.__w_initial_floor * 2.0 * (mt.__step1_betas[0]
+ mt.__step1_betas[1]
* mt.__x_floor) ** 2),
0.0))
mt = mt.annotate_cols(__step1_betas=hl.agg.filter(
mt.__in_step1,
hl.agg.linreg(y=mt.__y,
x=[1.0, mt.__x],
weight=mt.__w).beta))
mt = mt.annotate_cols(__step1_h2=hl.max(hl.min(
mt.__step1_betas[1] * M / hl.agg.mean(mt.__n), 1.0), 0.0))
mt = mt.annotate_cols(__step1_betas=[
mt.__step1_betas[0],
mt.__step1_h2 * hl.agg.mean(mt.__n) / M])
# step 1 block jackknife
mt = mt.annotate_cols(__step1_block_betas=hl.agg.array_agg(
lambda i: hl.agg.filter((mt.__step1_block != i) & mt.__in_step1,
hl.agg.linreg(y=mt.__y,
x=[1.0, mt.__x],
weight=mt.__w).beta),
hl.range(n_blocks)))
mt = mt.annotate_cols(__step1_block_betas_bias_corrected=hl.map(
lambda x: n_blocks * mt.__step1_betas - (n_blocks - 1) * x,
mt.__step1_block_betas))
mt = mt.annotate_cols(
__step1_jackknife_mean=hl.map(
lambda i: hl.mean(
hl.map(lambda x: x[i],
mt.__step1_block_betas_bias_corrected)),
hl.range(0, __k)),
__step1_jackknife_variance=hl.map(
lambda i: (hl.sum(
hl.map(lambda x: x[i]**2,
mt.__step1_block_betas_bias_corrected))
- hl.sum(
hl.map(lambda x: x[i],
mt.__step1_block_betas_bias_corrected)) ** 2
/ n_blocks)
/ (n_blocks - 1) / n_blocks,
hl.range(0, __k)))
# step 2 iteratively reweighted least squares
for i in range(3):
mt = mt.annotate_entries(__w=hl.cond(
mt.__in_step2,
1.0 / (mt.__w_initial_floor
* 2.0 * (mt.__step2_betas[0] +
+ mt.__step2_betas[1]
* mt.__x_floor) ** 2),
0.0))
mt = mt.annotate_cols(__step2_betas=[
mt.__step1_betas[0],
hl.agg.filter(mt.__in_step2,
hl.agg.linreg(y=mt.__y - mt.__step1_betas[0],
x=[mt.__x],
weight=mt.__w).beta[0])])
mt = mt.annotate_cols(__step2_h2=hl.max(hl.min(
mt.__step2_betas[1] * M / hl.agg.mean(mt.__n), 1.0), 0.0))
mt = mt.annotate_cols(__step2_betas=[
mt.__step1_betas[0],
mt.__step2_h2 * hl.agg.mean(mt.__n) / M])
# step 2 block jackknife
mt = mt.annotate_cols(__step2_block_betas=hl.agg.array_agg(
lambda i: hl.agg.filter((mt.__step2_block != i) & mt.__in_step2,
hl.agg.linreg(y=mt.__y - mt.__step1_betas[0],
x=[mt.__x],
weight=mt.__w).beta[0]),
hl.range(n_blocks)))
mt = mt.annotate_cols(__step2_block_betas_bias_corrected=hl.map(
lambda x: n_blocks * mt.__step2_betas[1] - (n_blocks - 1) * x,
mt.__step2_block_betas))
mt = mt.annotate_cols(
__step2_jackknife_mean=hl.mean(
mt.__step2_block_betas_bias_corrected),
__step2_jackknife_variance=(
hl.sum(mt.__step2_block_betas_bias_corrected ** 2)
- hl.sum(mt.__step2_block_betas_bias_corrected) ** 2
/ n_blocks) / (n_blocks - 1) / n_blocks)
# combine step 1 and step 2 block jackknifes
mt = mt.annotate_entries(
__step2_initial_w=1.0 / (mt.__w_initial_floor
* 2.0 * (mt.__initial_betas[0] +
+ mt.__initial_betas[1]
* mt.__x_floor) ** 2))
mt = mt.annotate_cols(
__final_betas=[
mt.__step1_betas[0],
mt.__step2_betas[1]],
__c=(hl.agg.sum(mt.__step2_initial_w * mt.__x)
/ hl.agg.sum(mt.__step2_initial_w * mt.__x**2)))
mt = mt.annotate_cols(__final_block_betas=hl.map(
lambda i: (mt.__step2_block_betas[i] - mt.__c
* (mt.__step1_block_betas[i][0] - mt.__final_betas[0])),
hl.range(0, n_blocks)))
mt = mt.annotate_cols(
__final_block_betas_bias_corrected=(n_blocks * mt.__final_betas[1]
- (n_blocks - 1)
* mt.__final_block_betas))
mt = mt.annotate_cols(
__final_jackknife_mean=[
mt.__step1_jackknife_mean[0],
hl.mean(mt.__final_block_betas_bias_corrected)],
__final_jackknife_variance=[
mt.__step1_jackknife_variance[0],
(hl.sum(mt.__final_block_betas_bias_corrected ** 2)
- hl.sum(mt.__final_block_betas_bias_corrected) ** 2
/ n_blocks) / (n_blocks - 1) / n_blocks])
# convert coefficient to heritability estimate
mt = mt.annotate_cols(
phenotype=mt.__y_name,
mean_chi_sq=hl.agg.mean(mt.__y),
intercept=hl.struct(
estimate=mt.__final_betas[0],
standard_error=hl.sqrt(mt.__final_jackknife_variance[0])),
snp_heritability=hl.struct(
estimate=(M / hl.agg.mean(mt.__n)) * mt.__final_betas[1],
standard_error=hl.sqrt((M / hl.agg.mean(mt.__n)) ** 2
* mt.__final_jackknife_variance[1])))
# format and return results
ht = mt.cols()
ht = ht.key_by(ht.phenotype)
ht = ht.select(ht.mean_chi_sq,
ht.intercept,
ht.snp_heritability)
ht_tmp_file = new_temp_file()
ht.write(ht_tmp_file)
ht = hl.read_table(ht_tmp_file)
return ht
p.add_argument("--index-type", help="Elasticsearch index type", required=True)
p.add_argument("--num-shards",
help="Number of elasticsearch shards",
default=1,
type=int)
p.add_argument("--es-block-size",
help="Elasticsearch block size to use when exporting",
default=200,
type=int)
args = p.parse_args()
hl.init(log="/tmp/hail.log")
print("\n=== Importing Hail table ===")
ds = hl.read_table(args.ht_url)
print("\n=== Exporting to Elasticsearch ===")
es = ElasticsearchClient(args.host, args.port)
es.export_table_to_elasticsearch(
ds,
index_name=args.index_name,
index_type_name=args.index_type,
block_size=args.es_block_size,
num_shards=args.num_shards,
delete_index_before_exporting=True,
export_globals_to_index_meta=True,
verbose=True,
)
def maximal_independent_set(i, j, keep=True, tie_breaker=None, keyed=True) -> Table:
"""Return a table containing the vertices in a near
`maximal independent set <https://en.wikipedia.org/wiki/Maximal_independent_set>`_
of an undirected graph whose edges are given by a two-column table.
Examples
--------
Run PC-relate and compute pairs of closely related individuals:
>>> pc_rel = hl.pc_relate(dataset.GT, 0.001, k=2, statistics='kin')
>>> pairs = pc_rel.filter(pc_rel['kin'] > 0.125)
Starting from the above pairs, prune individuals from a dataset until no
close relationships remain:
>>> related_samples_to_remove = hl.maximal_independent_set(pairs.i, pairs.j, False)
>>> result = dataset.filter_cols(
... hl.is_defined(related_samples_to_remove[dataset.col_key]), keep=False)
Starting from the above pairs, prune individuals from a dataset until no
close relationships remain, preferring to keep cases over controls:
>>> samples = dataset.cols()
>>> pairs_with_case = pairs.key_by(
... i=hl.struct(id=pairs.i, is_case=samples[pairs.i].is_case),
... j=hl.struct(id=pairs.j, is_case=samples[pairs.j].is_case))
>>> def tie_breaker(l, r):
... return hl.cond(l.is_case & ~r.is_case, -1,
... hl.cond(~l.is_case & r.is_case, 1, 0))
>>> related_samples_to_remove = hl.maximal_independent_set(
... pairs_with_case.i, pairs_with_case.j, False, tie_breaker)
>>> result = dataset.filter_cols(hl.is_defined(
... related_samples_to_remove.key_by(
... s = related_samples_to_remove.node.id.s)[dataset.col_key]), keep=False)
Notes
-----
The vertex set of the graph is implicitly all the values realized by `i`
and `j` on the rows of this table. Each row of the table corresponds to an
undirected edge between the vertices given by evaluating `i` and `j` on
that row. An undirected edge may appear multiple times in the table and
will not affect the output. Vertices with self-edges are removed as they
are not independent of themselves.
The expressions for `i` and `j` must have the same type.
The value of `keep` determines whether the vertices returned are those
in the maximal independent set, or those in the complement of this set.
This is useful if you need to filter a table without removing vertices that
don't appear in the graph at all.
This method implements a greedy algorithm which iteratively removes a
vertex of highest degree until the graph contains no edges. The greedy
algorithm always returns an independent set, but the set may not always
be perfectly maximal.
`tie_breaker` is a Python function taking two arguments---say `l` and
`r`---each of which is an :class:`Expression` of the same type as `i` and
`j`. `tie_breaker` returns a :class:`NumericExpression`, which defines an
ordering on nodes. A pair of nodes can be ordered in one of three ways, and
`tie_breaker` must encode the relationship as follows:
- if ``l < r`` then ``tie_breaker`` evaluates to some negative integer
- if ``l == r`` then ``tie_breaker`` evaluates to 0
- if ``l > r`` then ``tie_breaker`` evaluates to some positive integer
For example, the usual ordering on the integers is defined by: ``l - r``.
The `tie_breaker` function must satisfy the following property:
``tie_breaker(l, r) == -tie_breaker(r, l)``.
When multiple nodes have the same degree, this algorithm will order the
nodes according to ``tie_breaker`` and remove the *largest* node.
Parameters
----------
i : :class:`.Expression`
Expression to compute one endpoint of an edge.
j : :class:`.Expression`
Expression to compute another endpoint of an edge.
keep : :obj:`bool`
If ``True``, return vertices in set. If ``False``, return vertices removed.
tie_breaker : function
Function used to order nodes with equal degree.
keyed : :obj:`bool`
If ``True``, key the resulting table by the `node` field, this requires
a sort.
Returns
-------
:class:`.Table`
Table with the set of independent vertices. The table schema is one row
field `node` which has the same type as input expressions `i` and `j`.
"""
if i.dtype != j.dtype:
raise ValueError("'maximal_independent_set' expects arguments `i` and `j` to have same type. "
"Found {} and {}.".format(i.dtype, j.dtype))
source = i._indices.source
if not isinstance(source, Table):
raise ValueError("'maximal_independent_set' expects an expression of 'Table'. Found {}".format(
"expression of '{}'".format(
source.__class__) if source is not None else 'scalar expression'))
if i._indices.source != j._indices.source:
raise ValueError(
"'maximal_independent_set' expects arguments `i` and `j` to be expressions of the same Table. "
"Found\n{}\n{}".format(i, j))
node_t = i.dtype
if tie_breaker:
wrapped_node_t = ttuple(node_t)
l = construct_variable('l', wrapped_node_t)
r = construct_variable('r', wrapped_node_t)
tie_breaker_expr = hl.int64(tie_breaker(l[0], r[0]))
t, _ = source._process_joins(i, j, tie_breaker_expr)
tie_breaker_str = str(tie_breaker_expr._ir)
else:
t, _ = source._process_joins(i, j)
tie_breaker_str = None
edges = t.select(__i=i, __j=j).key_by().select('__i', '__j')
edges_path = new_temp_file()
edges.write(edges_path)
edges = hl.read_table(edges_path)
mis_nodes = construct_expr(JavaIR(Env.hail().utils.Graph.pyMaximalIndependentSet(
Env.spark_backend('maximal_independent_set')._to_java_ir(edges.collect(_localize=False)._ir),
node_t._parsable_string(),
joption(tie_breaker_str))),
hl.tset(node_t))
nodes = edges.select(node = [edges.__i, edges.__j])
nodes = nodes.explode(nodes.node)
nodes = nodes.annotate_globals(mis_nodes=mis_nodes)
nodes = nodes.filter(nodes.mis_nodes.contains(nodes.node), keep)
nodes = nodes.select_globals()
if keyed:
return nodes.key_by('node')
return nodes
def main(args):
########################################################################
### initialize
phenos = ['crc', 't2d', 'glaucoma', 'afib', 'ra']
renamed = {
's': 's',
'CRC': 'crc',
'T2D': 't2d',
'Glaucoma': 'glaucoma',
'AFib': 'afib',
'RA': 'ra'
}
phenotype = 'ALL5cc'
sumstats_text_file = args.dirname + args.basename + 'ALL5cc.clumped'
prs_loci_table_location = args.dirname + 'keytables/ukb-' + phenotype + '-pt-sumstats-locus-allele-keyed.kt'
contig_row_dict_location = args.dirname + 'contig_row_dict-' + phenotype
contigs = {'0{}'.format(x): str(x) for x in range(1, 10)}
bgen_files = 'gs://fc-7d5088b4-7673-45b5-95c2-17ae00a04183/imputed/ukb_imp_chr{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22}_v3.bgen'
start = time.time()
# large block size because we read very little data (due to filtering & ignoring genotypes)
# hl.init(branching_factor=10, min_block_size=2000)
hl.init()
################################################################################
### set up the sumstats table (chr, bp for union SNPs)
if (args.generate_prs_loci_table):
t = hl.import_table(sumstats_text_file, delimiter='\s+', impute=True)
t = t.select(locus=hl.locus(hl.str(t.CHR), t.BP))
t = t.key_by('locus')
t.write(prs_loci_table_location, overwrite=True)
ss = hl.read_table(prs_loci_table_location)
################################################################################
### Get true phenotypes from UKBB
if args.pheno_table:
phenotypes = hl.import_table(
'gs://mkanai/disparities/ukb31063.phecode_5diseases.both_sexes.tsv.bgz',
key='s',
impute=True,
types={'s': hl.tstr})
phenotypes = phenotypes.rename(renamed)
covariates = hl.import_table(
'gs://phenotype_31063/ukb31063.gwas_covariates.both_sexes.tsv',
key='s',
impute=True,
types={'s': hl.tstr})
samples = covariates.annotate(**phenotypes[covariates.s])
# Write pheno/covar/sample info table
for pheno in phenos:
gwas_holdout = hl.import_table(
'gs://mkanai/disparities/ukbb/pheno_31063_holdout_gwas_' +
pheno + '.info.txt.gz',
delimiter='\s+').key_by('s')
samples = samples.annotate(
**{
pheno + '_holdout':
gwas_holdout[samples.s].gwas_holdout == 'holdout'
})
samples.write(
'gs://mkanai/disparities/pheno_31063_holdout_gwas_cc_phenos.ht',
args.overwrite)
if args.ss_tables:
# Write ss info
for pheno in phenos:
print(pheno)
ss = hl.import_table(args.dirname + args.basename + pheno +
'.*.bgz',
delimiter='\s+',
impute=True,
types={
'beta': hl.tfloat,
'pval': hl.tfloat,
'pos': hl.tint,
'nCompleteSamples': hl.tint,
'AC': hl.tfloat,
'ytx': hl.tfloat,
'se': hl.tfloat,
'tstat': hl.tfloat
})
ss = ss.key_by(locus=hl.locus(hl.str(ss.chr), hl.int(
ss.pos))).repartition(200)
ss.write(args.dirname + args.basename + pheno + '.ht', True)
################################################################################
### Run the PRS using phenotype-specific clump variants
if args.write_bgen:
mt_all = hl.import_bgen(
bgen_files,
entry_fields=['dosage'],
sample_file='gs://phenotype_31063/ukb31063.autosomes.sample',
variants=ss.locus)
samples = hl.read_table(
'gs://mkanai/disparities/pheno_31063_holdout_gwas_cc_phenos.ht')
mt_all = mt_all.annotate_cols(**samples[
mt_all.s]) # ok that phenos keyed on userId not s?
mt_all.repartition(5000, shuffle=False).write(
args.dirname + args.basename + 'ALL5cc.mt', args.overwrite)
mt_all = hl.read_matrix_table(args.dirname + args.basename + 'ALL5cc.mt')
for pheno in phenos: #[6:len(phenos)]:
print(pheno)
ss = hl.read_table(args.dirname + args.basename + pheno + '.ht')
"""
To add:
- Filter only to samples in holdout GWAS
- Filter to rows in phenotype-specific clump file
- Build PRS for 10 p-value thresholds
- Also fix nt1/nt2 to A1 and A2 (check) from sumstats.
"""
# filter to only samples held out from GWAS
mt = mt_all.filter_cols(mt_all[pheno + '_holdout'])
mt = mt.annotate_rows(ss=ss[mt.locus])
mt = annotate_beta(mt, mt.ss)
# p_max = {'s1': 5e-8, 's2': 1e-6, 's3': 1e-4, 's4': 1e-3, 's5': 1e-2, 's6': .05, 's7': .1, 's8': .2, 's9': .5, 's10': 1}
p_max = {'s1': 5e-8, 's2': 1e-6, 's3': 1e-4, 's4': 1e-3, 's5': 1e-2}
pheno_clump = specific_clumps(args.dirname + args.basename + pheno +
'.clumped')
mt = mt.filter_rows(hl.is_defined(pheno_clump[mt.locus]))
print(mt.count())
annot_expr = {
k: hl.agg.sum(mt.beta * mt.dosage * hl.int(mt.ss.pval < v))
for k, v in p_max.items()
}
mt = mt.annotate_cols(**annot_expr)
mt.cols().write(args.dirname + 'UKB_' + pheno + '_PRS.ht',
stage_locally=True,
overwrite=True)
ht = hl.read_table(args.dirname + 'UKB_' + pheno + '_PRS.ht')
ht_out = ht.drop(*[x for x in list(ht.row) if 'holdout' in x],
*[x for x in phenos if pheno not in x])
output_location = args.dirname + 'UKB_' + pheno + '_PRS.txt.bgz'
ht_out.export(output_location)
end = time.time()
print("Success! Job was completed in %s" %
time.strftime("%H:%M:%S", time.gmtime(end - start)))
def test_write_stage_locally(self):
t = hl.utils.range_table(5)
f = new_temp_file(suffix='ht')
t.write(f, stage_locally=True)
t2 = hl.read_table(f)
self.assertTrue(t._same(t2))
def annotate_transcript_consequences(variants_path,
transcripts_path,
mane_transcripts_path=None):
ds = hl.read_table(variants_path)
most_severe_consequence = ds.vep.most_severe_consequence
transcript_consequences = ds.vep.transcript_consequences
# Drop irrelevant consequences
transcript_consequences = transcript_consequences.map(
lambda c: c.annotate(consequence_terms=c.consequence_terms.filter(
lambda t: ~OMIT_CONSEQUENCE_TERMS.contains(t)))).filter(
lambda c: c.consequence_terms.size() > 0)
# Add/transmute derived fields
transcript_consequences = transcript_consequences.map(
lambda c: c.annotate(major_consequence=hl.sorted(
c.consequence_terms, key=consequence_term_rank)[0])
).map(lambda c: c.annotate(
domains=c.domains.map(lambda domain: domain.db + ":" + domain.name),
hgvsc=c.hgvsc.split(":")[-1],
hgvsp=hgvsp_from_consequence_amino_acids(c),
is_canonical=hl.bool(c.canonical),
))
transcript_consequences = transcript_consequences.map(lambda c: c.select(
"biotype",
"consequence_terms",
"domains",
"gene_id",
"gene_symbol",
"hgvsc",
"hgvsp",
"is_canonical",
"lof_filter",
"lof_flags",
"lof",
"major_consequence",
"polyphen_prediction",
"sift_prediction",
"transcript_id",
))
transcripts = hl.read_table(transcripts_path)
transcript_info = hl.dict([
(row.transcript_id, row.transcript_info)
for row in transcripts.select(transcript_info=hl.struct(
transcript_version=transcripts.transcript_version,
gene_version=transcripts.gene.gene_version,
)).collect()
])
transcript_consequences = transcript_consequences.map(
lambda csq: csq.annotate(**transcript_info.get(csq.transcript_id)))
if mane_transcripts_path:
mane_transcripts = hl.read_table(mane_transcripts_path)
mane_transcripts = hl.dict([(row.gene_id, row.drop("gene_id"))
for row in mane_transcripts.collect()])
transcript_consequences = transcript_consequences.map(
lambda csq: csq.annotate(**hl.rbind(
mane_transcripts.get(csq.gene_id),
lambda mane_transcript: (hl.case().when(
(mane_transcript.ensembl_id == csq.transcript_id)
& (mane_transcript.ensembl_version == csq.
transcript_version),
hl.struct(
is_mane_select=True,
is_mane_select_version=True,
refseq_id=mane_transcript.refseq_id,
refseq_version=mane_transcript.refseq_version,
),
).when(
mane_transcript.ensembl_id == csq.transcript_id,
hl.struct(
is_mane_select=True,
is_mane_select_version=False,
refseq_id=hl.null(hl.tstr),
refseq_version=hl.null(hl.tstr),
),
).default(
hl.struct(
is_mane_select=False,
is_mane_select_version=False,
refseq_id=hl.null(hl.tstr),
refseq_version=hl.null(hl.tstr),
))),
)))
transcript_consequences = hl.sorted(
transcript_consequences,
lambda c: (
hl.if_else(
c.biotype == "protein_coding", 0, 1, missing_false=True),
hl.if_else(c.major_consequence == most_severe_consequence,
0,
1,
missing_false=True),
hl.if_else(c.is_mane_select, 0, 1, missing_false=True),
hl.if_else(c.is_canonical, 0, 1, missing_false=True),
),
)
else:
transcript_consequences = hl.sorted(
transcript_consequences,
lambda c: (
hl.if_else(
c.biotype == "protein_coding", 0, 1, missing_false=True),
hl.if_else(c.major_consequence == most_severe_consequence,
0,
1,
missing_false=True),
hl.if_else(c.is_canonical, 0, 1, missing_false=True),
),
)
ds = ds.annotate(
transcript_consequences=transcript_consequences).drop("vep")
return ds
def test_write_stage_locally(self):
t = hl.utils.range_table(5)
f = new_temp_file(suffix='ht')
t.write(f, stage_locally=True)
t2 = hl.read_table(f)
self.assertTrue(t._same(t2))
def main(args):
########################################################################
### initialize
which_beta = 'beta' + args.which_beta
if args.method == 'metal':
end_dir = 'metal/'
clumps = args.dirname + end_dir + 'BBJ_UKBB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_beta_' + args.which_beta + '_' + args.iter + '.metal.clumped'
ss_filename = args.dirname + end_dir + 'BBJ_UKBB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_beta_' + args.which_beta + '_' + args.iter + '.tsv'
out_base = args.dirname + end_dir + which_beta + '_draw_' + args.iter + '_spike_' + args.which_beta + '_metal_PRS'
elif args.method == 'mama':
ld = args.ld + '/'
analysis = args.analysis + '/'
end_dir = 'mama/ld_true/'
clumps = args.dirname + end_dir + ld + analysis + 'draw_' + args.iter + '_spike_' + args.which_beta + '_mama_2.clumped'
ss_filename = args.dirname + end_dir + ld + analysis + 'draw_' + args.iter + '_spike_' + args.which_beta + '_mama_2.txt'
# this ss_filename has different headers
out_base = args.dirname + end_dir + ld + analysis + 'draw_' + args.iter + '_spike_' + args.which_beta + '_mama_2_PRS'
else:
end_dir = 'ukbb_only/'
clumps = args.dirname + end_dir + 'UKB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_' + args.iter + '_beta' + args.which_beta + '.clumped'
ss_filename = args.dirname + end_dir + 'UKB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_' + args.iter + '.tsv.gz'
out_base = args.dirname + end_dir + 'UKB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_' + args.iter + '_beta' + args.which_beta + '_gwas_PRS'
clump_table_location = args.dirname + 'keytables/ukb-' + args.basename + '-pt-sumstats-locus-allele-keyed.kt'
contigs = {'0{}'.format(x): str(x) for x in range(1, 10)}
bgen_files = 'gs://fc-7d5088b4-7673-45b5-95c2-17ae00a04183/imputed/ukb_imp_chr22_v3.bgen'
start = time.time()
# large block size because we read very little data (due to filtering & ignoring genotypes)
hl.init(branching_factor=10, min_block_size=2000)
# set min_block_size only in import_bgen
################################################################################
### set up the sumstats table (chr, bp for union SNPs)
if args.read_clumps:
clump_file = hl.import_table(clumps, delimiter='\s+', impute=True)
clump_file = clump_file.select(
locus=hl.locus(hl.str(clump_file.CHR), clump_file.BP))
clump_file = clump_file.key_by('locus')
clump_file.write(clump_table_location, overwrite=True)
clump_file = hl.read_table(clump_table_location)
################################################################################
### Write ss info, process so sumstats are uniform across MAMA, METAL, and gwas
if args.ss_tables:
#ss = hl.import_table(args.dirname + args.basename + '.tsv.gz',
ss = hl.import_table(
ss_filename,
#'BBJ_UKBB_hm3.chr22.cm.beta.true_PRS.gwas_sumstat_' + args.which_beta + 'beta_01_9.tsv'
# # for mama case
delimiter='\s+',
impute=True,
types={'BP': hl.tint})
if args.method != 'mama' and args.method != 'metal':
ss = ss.rename({
'chr': 'CHR',
'pos': 'BP',
'rsid': 'SNP',
'ref': 'A1',
'alt': 'A2',
'maf': 'FRQ',
'p_value_beta_' + args.which_beta: 'MAMA_PVAL',
'standard_error_beta_' + args.which_beta: 'MAMA_SE',
'beta_beta_' + args.which_beta: 'MAMA_BETA'
})
ss = ss.key_by(
locus=hl.locus(hl.str(ss.CHR), hl.int(ss.BP))).repartition(200)
ss = ss.annotate(A1=ss.A1.upper(), A2=ss.A2.upper())
ss.write(args.dirname + args.basename + '_sep.ht', True)
ss = hl.read_table(args.dirname + args.basename + '_sep.ht')
################################################################################
### Run the PRS using phenotype-specific clump variants
if args.write_bgen:
mt_all = hl.import_bgen(
bgen_files, ['dosage'],
sample_file='gs://phenotype_31063/ukb31063.autosomes.sample',
variants=clump_file.locus)
samples = hl.import_table(args.dirname +
'ukb_not_in_simulation_rand5000.inds',
types={
's': hl.tstr
}).key_by('s')
mt = mt_all.filter_cols(hl.is_defined(samples[mt_all.s]))
mt.repartition(5000, shuffle=False).write(
args.dirname + args.basename + '.mt', True)
mt = hl.read_matrix_table(args.dirname + args.basename + '.mt')
true_ss = hl.read_table(args.dirname +
'BBJ_UKB_hm3.chr22.cm.beta.true_PRS.ht')
"""
To add:
- Also fix nt1/nt2 to A1 and A2 (check) from sumstats.
"""
# filter to only samples held out from GWAS
mt = mt.annotate_rows(ss=ss[mt.locus])
mt = annotate_beta(mt, mt.ss)
p_max = {
's1': 5e-8,
's2': 1e-6,
's3': 1e-4,
's4': 1e-3,
's5': 1e-2,
's6': .05,
's7': .1,
's8': .2,
's9': .5,
's10': 1
}
pheno_clump = specific_clumps(clumps)
mt = mt.filter_rows(pheno_clump.get(mt.locus, False))
print(mt.count())
# divide by sd's of frequencies to get standardized betas back to allelic scale for MAMA betas (only, not METAL)
# sqrt(2pq)
if args.betas_are_standardized:
annot_expr = {
k: hl.agg.sum(mt.beta / hl.sqrt(2 * hl.float(mt.ss.FRQ) *
(1 - hl.float(mt.ss.FRQ))) *
mt.dosage * hl.int(mt.ss.MAMA_PVAL < v))
for k, v in p_max.items()
}
else:
annot_expr = {
k: hl.agg.sum(mt.beta * mt.dosage * hl.int(mt.ss.MAMA_PVAL < v))
for k, v in p_max.items()
}
mt = mt.annotate_cols(**annot_expr, **true_ss[mt.s])
mt.key_cols_by().cols().write(out_base + '.ht',
stage_locally=True,
overwrite=True)
ht = hl.read_table(out_base + '.ht')
output_location = out_base + '.txt.bgz'
ht.export(output_location)
end = time.time()
print("Success! Job was completed in %s" %
time.strftime("%H:%M:%S", time.gmtime(end - start)))
# s3 credentials required for user to access the datasets in farm flexible compute s3 environment
# you may use your own here from your .s3fg file in your home directory
hadoop_config = sc._jsc.hadoopConfiguration()
hadoop_config.set("fs.s3a.access.key", credentials["mer"]["access_key"])
hadoop_config.set("fs.s3a.secret.key", credentials["mer"]["secret_key"])
bed_to_exclude_pca = hl.import_bed(
f"{temp_dir}/1000g/price_high_ld.bed.txt", reference_genome='GRCh38')
cohorts_pop = hl.import_table(
"s3a://DDD-ELGH-UKBB-exomes/ancestry/sanger_cohort_known_populations_ukbb_elgh_labels_updated.tsv", delimiter="\t").key_by('s')
# Read mt
mt = hl.read_matrix_table(
f"{temp_dir}/ddd-elgh-ukbb/new_labels/chr1_chr20_ldpruned_updated.mt")
# pca_scores_pop
pca_scores_pop = hl.read_table(
f"{temp_dir}/ddd-elgh-ukbb/new_labels/pop_assignments_updated_august2020.ht")
''' # pca_scores_superpop
pca_scores_superpop = hl.read_table(
f"{temp_dir}/ddd-elgh-ukbb/new_labels/pop_assignments_updated_august2020_superpops.ht")
# annotate mt with pop and superpop
mt = mt.annotate_cols(assigned_pop=pca_scores_pop[mt.s].pop)
mt = mt.annotate_cols(assigned_superpop=pca_scores_superpop[mt.s].pop)
# do sample_qc
# calculate and annotate with metric heterozygosity
mt_with_sampleqc = hl.sample_qc(mt, name='sample_qc')
mt_with_sampleqc = mt_with_sampleqc.annotate_cols(sample_qc=mt_with_sampleqc.sample_qc.annotate(
heterozygosity_rate=mt_with_sampleqc.sample_qc.n_het/mt_with_sampleqc.sample_qc.n_called))
ht_mfi['chrom'],
hl.str(ht_mfi['position']), ht_mfi['allele1_ref'], ht_mfi['allele2_alt']
]),
delimiter=':'))
# prep to merge with GWAS variant list
ht_mfi = ht_mfi.key_by('variant')
ht_mfi = ht_mfi.annotate(maf=hl.float(ht_mfi.maf), info=hl.float(ht_mfi.info))
ht_mfi = ht_mfi.select('varid', 'rsid', 'maf', 'info')
#######
# load GWAS variant list
#######
# get GWAS variant list
ht_sites = hl.read_table('gs://ukb31063/ukb31063.neale_gwas_variants.ht')
ht_sites = ht_sites.annotate(
variant=hl.variant_str(ht_sites.locus, ht_sites.alleles))
ht_sites = ht_sites.key_by('variant')
########
# merge and save
########
# get final merged file with maf/info of the gwas variants
ht = ht_mfi.join(ht_sites, how='inner')
ht = ht.select('locus', 'alleles', 'varid', 'rsid', 'maf', 'info')
print(ht.count())
# save both ht and tsv
ht.write('gs://ukb31063/ukb31063.neale_gwas_variants.imputed_v3.mfi.ht',
def load_dataset(name,
version,
reference_genome,
config_file='gs://hail-datasets/datasets.json'):
"""Load a genetic dataset from Hail's repository.
Example
-------
>>> # Load 1000 Genomes MatrixTable with GRCh38 coordinates
>>> mt_1kg = hl.experimental.load_dataset(name='1000_genomes', # doctest: +SKIP
... version='phase3',
... reference_genome='GRCh38')
Parameters
----------
name : :obj:`str`
Name of the dataset to load.
version : :obj:`str`
Version of the named dataset to load
(see available versions in documentation).
reference_genome : `GRCh37` or `GRCh38`
Reference genome build.
Returns
-------
:class:`.Table` or :class:`.MatrixTable`"""
with hl.hadoop_open(config_file, 'r') as f:
datasets = json.load(f)
names = set([dataset['name'] for dataset in datasets])
if name not in names:
raise ValueError('{} is not a dataset available in the repository.'.format(repr(name)))
versions = set([dataset['version'] for dataset in datasets if dataset['name']==name])
if version not in versions:
raise ValueError("""Version {0} not available for dataset {1}.
Available versions: {{{2}}}.""".format(repr(version),
repr(name),
repr('","'.join(versions))))
reference_genomes = set([dataset['reference_genome'] for dataset in datasets if dataset['name']==name])
if reference_genome not in reference_genomes:
raise ValueError("""Reference genome build {0} not available for dataset {1}.
Available reference genome builds: {{'{2}'}}.""".format(repr(reference_genome),
repr(name),
'\',\''.join((reference_genomes))))
path = [dataset['path'] for dataset in datasets if all([dataset['name']==name,
dataset['version']==version,
dataset['reference_genome']==reference_genome])][0].strip('/')
if path.endswith('.ht'):
dataset = hl.read_table(path)
else:
if not path.endswith('.mt'):
raise ValueError('Invalid path {}: can only load datasets with .ht or .mt extensions.'.format(repr(path)))
dataset = hl.read_matrix_table(path)
return dataset
def query():
"""Query script entry point."""
hl.init(default_reference='GRCh38')
# save relatedness estimates for pc_relate global populations
ht = hl.read_table(PC_RELATE_ESTIMATE_GLOBAL)
related_samples = ht.filter(ht.kin > 0.1)
pc_relate_global = pd.DataFrame({
'i_s': related_samples.i.s.collect(),
'j_s': related_samples.j.s.collect(),
'kin': related_samples.kin.collect(),
})
filename = output_path(f'pc_relate_global_matrix.csv', 'analysis')
pc_relate_global.to_csv(filename, index=False)
# get maximal independent set
pairs = ht.filter(ht['kin'] >= 0.125)
related_samples_to_remove = hl.maximal_independent_set(
pairs.i, pairs.j, False)
related_samples = pd.DataFrame(
{'removed_individual': related_samples_to_remove.node.s.collect()})
filename = output_path(f'pc_relate_global_maximal_independent_set.csv',
'analysis')
related_samples.to_csv(filename, index=False)
# save relatedness estimates for pc_relate NFE samples
ht = hl.read_table(PC_RELATE_ESTIMATE_NFE)
related_samples = ht.filter(ht.kin > 0.1)
pc_relate_nfe = pd.DataFrame({
'i_s': related_samples.i.s.collect(),
'j_s': related_samples.j.s.collect(),
'kin': related_samples.kin.collect(),
})
filename = output_path(f'pc_relate_nfe_matrix.csv', 'analysis')
pc_relate_nfe.to_csv(filename, index=False)
# get maximal independent set
pairs = ht.filter(ht['kin'] >= 0.125)
related_samples_to_remove = hl.maximal_independent_set(
pairs.i, pairs.j, False)
related_samples = pd.DataFrame(
{'removed_individual': related_samples_to_remove.node.s.collect()})
filename = output_path(f'pc_relate_nfe_maximal_independent_set.csv',
'analysis')
related_samples.to_csv(filename, index=False)
# save relatedness estimates for KING NFE samples
mt = hl.read_matrix_table(KING_ESTIMATE_NFE)
ht = mt.entries()
# remove entries where samples are identical
related_samples = ht.filter(ht.s_1 != ht.s)
related_samples = ht.filter(ht.phi > 0.1)
king_nfe = pd.DataFrame({
'i_s': related_samples.s_1.collect(),
'j_s': related_samples.s.collect(),
'kin': related_samples.phi.collect(),
})
filename = output_path(f'king_nfe_matrix_90k.csv', 'analysis')
king_nfe.to_csv(filename, index=False)
# save KING NFE maximal independent set
second_degree_related_samples = ht.filter(
(ht.s_1 != ht.s) & (ht.phi > 0.125), keep=True)
struct = hl.struct(i=second_degree_related_samples.s_1,
j=second_degree_related_samples.s)
struct = struct.annotate(phi=second_degree_related_samples.phi)
related_samples_to_remove = hl.maximal_independent_set(
struct.i,
struct.j,
False # pylint: disable=E1101
)
related_samples = pd.DataFrame(
{'related_individual': related_samples_to_remove.node.collect()})
filename = output_path(
f'king_90k_related_samples_maximal_independent_set.csv', 'analysis')
related_samples.to_csv(filename, index=False)
