def test_linear_mixed_regression_low_rank(self):
x_table = hl.import_table(resource('fastlmmCov.txt'), no_header=True, impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'), no_header=True, impute=True, delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
h2_hail = 0.10001626
beta_hail = [0.0073201542, 0.039969148, -0.036727875, 0.29852363, -0.049212500]
pval_hail = [0.90685162, 0.54839177, 0.55001054, 9.85247263e-07, 0.42796507]
mt_chr1 = mt.filter_rows((mt.locus.contig == '1') & (mt.locus.position < 200))
model, _ = hl.linear_mixed_model(y=mt_chr1.y, x=[1, mt_chr1.x], z_t=mt_chr1.GT.n_alt_alleles(), p_path=p_path)
model.fit()
self.assertTrue(model.low_rank)
self.assertAlmostEqual(model.h_sq, h2_hail)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3') & (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(stats=hl.agg.stats(mt_chr3.GT.n_alt_alleles()))
ht = hl.linear_mixed_regression_rows((mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) / mt_chr3.stats.stdev,
model)
assert np.allclose(ht.beta.collect(), beta_hail)
assert np.allclose(ht.p_value.collect(), pval_hail)
def test_linear_mixed_regression_full_rank(self):
x_table = hl.import_table(resource('fastlmmCov.txt'), no_header=True, impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'), no_header=True, impute=True, delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
h2_fastlmm = 0.142761
h2_places = 6
beta_fastlmm = [0.012202061, 0.037718282, -0.033572693, 0.29171541, -0.045644170]
pval_hail = [0.84543084, 0.57596760, 0.58788517, 1.4057279e-06, 0.46578204]
mt_chr1 = mt.filter_rows(mt.locus.contig == '1')
model, _ = hl.linear_mixed_model(y=mt_chr1.y, x=[1, mt_chr1.x], z_t=mt_chr1.GT.n_alt_alleles(), p_path=p_path)
model.fit()
self.assertAlmostEqual(model.h_sq, h2_fastlmm, places=h2_places)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3') & (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(stats=hl.agg.stats(mt_chr3.GT.n_alt_alleles()))
ht = hl.linear_mixed_regression_rows((mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) / mt_chr3.stats.stdev,
model)
assert np.allclose(ht.beta.collect(), beta_fastlmm)
assert np.allclose(ht.p_value.collect(), pval_hail)
def test_linear_mixed_regression_low_rank(self):
x_table = hl.import_table(resource('fastlmmCov.txt'),
no_header=True,
impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'),
no_header=True,
impute=True,
delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
h2_hail = 0.10001626
beta_hail = [
0.0073201542, 0.039969148, -0.036727875, 0.29852363, -0.049212500
]
pval_hail = [
0.90685162, 0.54839177, 0.55001054, 9.85247263e-07, 0.42796507
]
mt_chr1 = mt.filter_rows((mt.locus.contig == '1')
& (mt.locus.position < 200))
model, _ = hl.linear_mixed_model(y=mt_chr1.y,
x=[1, mt_chr1.x],
z_t=mt_chr1.GT.n_alt_alleles(),
p_path=p_path)
model.fit()
self.assertTrue(model.low_rank)
self.assertAlmostEqual(model.h_sq, h2_hail)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3')
& (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(
stats=hl.agg.stats(mt_chr3.GT.n_alt_alleles()))
ht = hl.linear_mixed_regression_rows(
(mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) /
mt_chr3.stats.stdev, model)
assert np.allclose(ht.beta.collect(), beta_hail)
assert np.allclose(ht.p_value.collect(), pval_hail)
def test_linear_mixed_regression_full_rank(self):
x_table = hl.import_table(resource('fastlmmCov.txt'),
no_header=True,
impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'),
no_header=True,
impute=True,
delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
h2_fastlmm = 0.142761
h2_places = 6
beta_fastlmm = [
0.012202061, 0.037718282, -0.033572693, 0.29171541, -0.045644170
]
pval_hail = [
0.84543084, 0.57596760, 0.58788517, 1.4057279e-06, 0.46578204
]
mt_chr1 = mt.filter_rows(mt.locus.contig == '1')
model, _ = hl.linear_mixed_model(y=mt_chr1.y,
x=[1, mt_chr1.x],
z_t=mt_chr1.GT.n_alt_alleles(),
p_path=p_path)
model.fit()
self.assertAlmostEqual(model.h_sq, h2_fastlmm, places=h2_places)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3')
& (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(
stats=hl.agg.stats(mt_chr3.GT.n_alt_alleles()))
ht = hl.linear_mixed_regression_rows(
(mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) /
mt_chr3.stats.stdev, model)
assert np.allclose(ht.beta.collect(), beta_fastlmm)
assert np.allclose(ht.p_value.collect(), pval_hail)
def test_linear_mixed_regression_pass_through(self):
x_table = hl.import_table(resource('fastlmmCov.txt'), no_header=True, impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'), no_header=True, impute=True, delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
mt_chr1 = mt.filter_rows((mt.locus.contig == '1') & (mt.locus.position < 200))
model, _ = hl.linear_mixed_model(y=mt_chr1.y, x=[1, mt_chr1.x], z_t=mt_chr1.GT.n_alt_alleles(), p_path=p_path)
model.fit(log_gamma=0)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3') & (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(stats=hl.agg.stats(mt_chr3.GT.n_alt_alleles()), foo=hl.struct(bar=hl.rand_norm(0, 1)))
ht = hl.linear_mixed_regression_rows((mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) / mt_chr3.stats.stdev,
model, pass_through=['stats', mt_chr3.foo.bar, mt_chr3.cm_position])
assert mt_chr3.aggregate_rows(hl.agg.all(mt_chr3.foo.bar == ht[mt_chr3.row_key].bar))
def test_linear_mixed_regression_pass_through(self):
x_table = hl.import_table(resource('fastlmmCov.txt'),
no_header=True,
impute=True).key_by('f1')
y_table = hl.import_table(resource('fastlmmPheno.txt'),
no_header=True,
impute=True,
delimiter=' ').key_by('f1')
mt = hl.import_plink(bed=resource('fastlmmTest.bed'),
bim=resource('fastlmmTest.bim'),
fam=resource('fastlmmTest.fam'),
reference_genome=None)
mt = mt.annotate_cols(x=x_table[mt.col_key].f2)
mt = mt.annotate_cols(y=y_table[mt.col_key].f2).cache()
p_path = utils.new_temp_file()
mt_chr1 = mt.filter_rows((mt.locus.contig == '1')
& (mt.locus.position < 200))
model, _ = hl.linear_mixed_model(y=mt_chr1.y,
x=[1, mt_chr1.x],
z_t=mt_chr1.GT.n_alt_alleles(),
p_path=p_path)
model.fit(log_gamma=0)
mt_chr3 = mt.filter_rows((mt.locus.contig == '3')
& (mt.locus.position < 2005))
mt_chr3 = mt_chr3.annotate_rows(stats=hl.agg.stats(
mt_chr3.GT.n_alt_alleles()),
foo=hl.struct(bar=hl.rand_norm(0, 1)))
ht = hl.linear_mixed_regression_rows(
(mt_chr3.GT.n_alt_alleles() - mt_chr3.stats.mean) /
mt_chr3.stats.stdev,
model,
pass_through=['stats', mt_chr3.foo.bar, mt_chr3.cm_position])
assert mt_chr3.aggregate_rows(
hl.agg.all(mt_chr3.foo.bar == ht[mt_chr3.row_key].bar))