def test_calch2(self):
logging.info("Leap test_calch2")
related_file = os.path.abspath(
os.path.join(self.gold_dir, 'dataset1.related'))
bed, _ = leapUtils.loadData(self.bedbase,
None,
self.phen_fn,
loadSNPs=False)
keepArr = leapUtils.loadRelatedFile(bed, related_file)
for i in range(1, 11):
h2_file = os.path.abspath(
os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
eigen_file = os.path.abspath(
os.path.join(self.tempout_dir,
'dataset1_nochr{}.npz'.format(i)))
eigen = np.load(eigen_file)
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
expected_h2 = np.loadtxt(h2_file, usecols=[0])
h2 = leapMain.calcH2(self.phen_fn,
0.001,
eigen,
keepArr=keepArr,
h2coeff=1.0)
assert np.abs(
h2 - expected_h2) < 1e-5, 'Incorrect heritability estimated'
def test_calch2(self):
logging.info("Leap test_calch2")
related_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1.related'))
bed, _ = leapUtils.loadData(self.bedbase, None, self.phen_fn, loadSNPs=False)
keepArr = leapUtils.loadRelatedFile(bed, related_file)
for i in xrange(1,11):
h2_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
eigen_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.npz'.format(i)))
eigen = np.load(eigen_file)
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
expected_h2 = np.loadtxt(h2_file, usecols=[0])
h2 = leapMain.calcH2(self.phen_fn, 0.001, eigen, keepArr=keepArr, h2coeff=1.0)
assert np.abs(h2-expected_h2)<1e-5, 'Incorrect heritability estimated'
print('Analyzing chromosome', chrom, '...')
#Create a bed object excluding SNPs from the current chromosome
bedExclude = leapUtils.getExcludedChromosome(bfile, chrom)
#Create a bed object including only SNPs from the current chromosome
bedTest = leapUtils.getChromosome(bfile, chrom)
#Compute eigendecomposition for the data
eigenFile = 'temp_eigen.npz'
eigen = leapMain.eigenDecompose(bedExclude, outFile=eigenFile)
#compute heritability explained by this data
h2 = leapMain.calcH2(phenoFile,
prevalence,
eigen,
keepArr=indsToKeep,
h2coeff=1.0)
#Compute liabilities explained by this data
liabs = leapMain.probit(bedExclude,
phenoFile,
h2,
prevalence,
eigen,
keepArr=indsToKeep)
#perform GWAS, using the liabilities as the observed phenotypes
results_df = leapMain.leapGwas(bedExclude, bedTest, liabs, h2)
frame_list.append(results_df)
for chrom in chromosomes:
print
print 'Analyzing chromosome', chrom, '...'
#Create a bed object excluding SNPs from the current chromosome
bedExclude = leapUtils.getExcludedChromosome(bfile, chrom)
#Create a bed object including only SNPs from the current chromosome
bedTest = leapUtils.getChromosome(bfile, chrom)
#Compute eigendecomposition for the data
eigenFile = 'temp_eigen.npz'
eigen = leapMain.eigenDecompose(bedExclude, outFile=eigenFile)
#compute heritability explained by this data
h2 = leapMain.calcH2(phenoFile, prevalence, eigen, keepArr=indsToKeep)
#Compute liabilities explained by this data
liabs = leapMain.probit(bedExclude, phenoFile, h2, prevalence, eigen, keepArr=indsToKeep)
#perform GWAS, using the liabilities as the observed phenotypes
results_df = leapMain.leapGwas(bedExclude, bedTest, liabs, h2)
frame_list.append(results_df)
#Join together the results of all chromosomes, and print the top ranking SNPs
frame = pd.concat(frame_list)
frame.sort("PValue", inplace=True)
frame.index = np.arange(len(frame))
print 'Top 10 most associated SNPs:'
print frame.head(n=10)
