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calc_h2.py
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calc_h2.py
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import numpy as np
import argparse
import scipy.stats as stats
import scipy.linalg.blas as blas
import sklearn.linear_model
import time
import sys
np.set_printoptions(precision=3, linewidth=200)
from . import leapUtils
from . import leapMain
def calcLiabThreholds(U, S, keepArr, phe, numRemovePCs, prev, covar):
#Run logistic regression
if (numRemovePCs > 0):
G = U[:, -numRemovePCs:] * np.sqrt(S[-numRemovePCs:])
else:
G = np.empty((phe.shape[0], 0))
if (covar is not None): G = np.concatenate((G, covar), axis=1)
Logreg = sklearn.linear_model.LogisticRegression(penalty='l2', C=500000, fit_intercept=True)
Logreg.fit(G[keepArr, :], phe[keepArr])
#Compute individual thresholds
Pi = Logreg.predict_proba(G)[:,1]
#Compute thresholds
P = np.sum(phe==1) / float(phe.shape[0])
K = prev
Ki = K*(1-P) / (P*(1-K)) * Pi / (1 + K*(1-P) / (P*(1-K))*Pi - Pi)
thresholds = stats.norm(0,1).isf(Ki)
thresholds[Ki>=1.] = -999999999
thresholds[Ki<=0.] = 999999999
return Pi, thresholds
def calcH2Continuous_twotails(XXT, phe, keepArr, prev):
print('computing h2 for a two-tails ascertained study...')
XXT = XXT[np.ix_(keepArr, keepArr)]
phe = phe[keepArr]
t1 = stats.norm(0,1).ppf(prev)
t2 = stats.norm(0,1).isf(prev)
phit1 = stats.norm(0,1).pdf(t1)
phit2 = stats.norm(0,1).pdf(t2)
K1 = prev
K2 = prev
xCoeff = ((phit2*t2 - phit1*t1 + K1 + K2)**2 * (K1+K2)**2 - (phit2-phit1)**4) / (K1 + K2)**4
intersect = ((phit2-phit1) / (K1+K2))**2
pheMean = 0
pheVar = 1
x = xCoeff * XXT
y = np.outer((phe-pheMean)/np.sqrt(pheVar), (phe-pheMean)/np.sqrt(pheVar))
y -= intersect
y = y[np.triu_indices(y.shape[0], 1)]
x = x[np.triu_indices(x.shape[0], 1)]
slope, intercept, rValue, pValue, stdErr = stats.linregress(x,y)
return slope
def calcH2Continuous(XXT, phe, keepArr, prev):
t = stats.norm(0,1).isf(prev)
phit = stats.norm(0,1).pdf(t)
K1 = 1 - prev
K2 = 1 - K1
P = np.sum(phe<t) / float(phe.shape[0])
P2 = 1.0
P1 = K2*P2*P / (K1*(1-P))
R = P2 / P1
XXT = XXT[np.ix_(keepArr, keepArr)]
phe = phe[keepArr]
xCoeff = (((R-1)*phit*t + K1 + R*K2)**2 * (K1+R*K2)**2 - ((R-1)*phit)**4) / (K1 + R*K2)**4
x = xCoeff * XXT
pheMean = 0
pheVar = 1
y = np.outer((phe-pheMean) / np.sqrt(pheVar), (phe-pheMean)/np.sqrt(pheVar))
y -= ((R-1)*phit / (K1+R*K2))**2
y = y[np.triu_indices(y.shape[0], 1)]
x = x[np.triu_indices(x.shape[0], 1)]
slope, intercept, rValue, pValue, stdErr = stats.linregress(x,y)
return slope
def calcH2Binary(XXT, phe, probs, thresholds, keepArr, prev):
K = prev
P = np.sum(phe>0) / float(phe.shape[0])
XXT = XXT[np.ix_(keepArr, keepArr)]
phe = phe[keepArr]
if (thresholds is None):
t = stats.norm(0,1).isf(K)
phit = stats.norm(0,1).pdf(t)
xCoeff = P*(1-P) / (K**2 * (1-K)**2) * phit**2
y = np.outer((phe-P) / np.sqrt(P*(1-P)), (phe-P) / np.sqrt(P*(1-P)))
x = xCoeff * XXT
else:
probs = probs[keepArr]
thresholds = thresholds[keepArr]
Ki = K*(1-P) / (P*(1-K)) * probs / (1 + K*(1-P) / (P*(1-K))*probs - probs)
phit = stats.norm(0,1).pdf(thresholds)
probsInvOuter = np.outer(probs*(1-probs), probs*(1-probs))
y = np.outer(phe-probs, phe-probs) / np.sqrt(probsInvOuter)
sumProbs = np.tile(np.column_stack(probs).T, (1,probs.shape[0])) + np.tile(probs, (probs.shape[0], 1))
Atag0 = np.outer(phit, phit) * (1 - (sumProbs)*(P-K)/(P*(1-K)) + np.outer(probs, probs)*(((P-K)/(P*(1-K)))**2)) / np.sqrt(probsInvOuter)
B0 = np.outer(Ki + (1-Ki)*(K*(1-P))/(P*(1-K)), Ki + (1-Ki)*(K*(1-P))/(P*(1-K)))
x = Atag0 / B0 * XXT
y = y[np.triu_indices(y.shape[0], 1)]
x = x[np.triu_indices(x.shape[0], 1)]
slope, intercept, rValue, pValue, stdErr = stats.linregress(x,y)
return slope
def calc_h2(pheno, prev, eigen, keepArr, covar, numRemovePCs, lowtail):
pheno = leapUtils._fixup_pheno(pheno)
#Extract phenotype
if isinstance(pheno, dict): phe = pheno['vals']
else: phe = pheno
if (len(phe.shape)==2):
if (phe.shape[1]==1): phe=phe[:,0]
else: raise Exception('More than one phenotype found')
if (keepArr is None): keepArr = np.ones(phe.shape[0], dtype=np.bool)
#Compute kinship matrix
XXT = eigen['XXT']
#Remove top PCs from kinship matrix
if (numRemovePCs > 0):
if (eigen is None): S,U = leapUtils.eigenDecompose(XXT)
else: S, U = eigen['arr_1'], eigen['arr_0']
print('Removing the top', numRemovePCs, 'PCs from the kinship matrix')
XXT -= (U[:, -numRemovePCs:]*S[-numRemovePCs:]).dot(U[:, -numRemovePCs:].T)
else:
U, S = None, None
#Determine if this is a case-control study
pheUnique = np.unique(phe)
if (pheUnique.shape[0] < 2): raise Exception('Less than two different phenotypes observed')
isCaseControl = (pheUnique.shape[0] == 2)
if isCaseControl:
print('Computing h2 for a binary phenotype')
pheMean = phe.mean()
phe[phe <= pheMean] = 0
phe[phe > pheMean] = 1
if (numRemovePCs > 0 or covar is not None):
probs, thresholds = calcLiabThreholds(U, S, keepArr, phe, numRemovePCs, prev, covar)
h2 = calcH2Binary(XXT, phe, probs, thresholds, keepArr, prev)
else: h2 = calcH2Binary(XXT, phe, None, None, keepArr, prev)
else:
if (covar is not None): raise Exception('Covariates with a continuous phenotype are currently not supported')
print('Computing h2 for a continuous phenotype')
if (not lowtail): h2 = calcH2Continuous(XXT, phe, keepArr, prev)
else: h2 = calcH2Continuous_twotails(XXT, phe, keepArr, prev)
if (h2 <= 0): raise Exception("Negative heritability found. Exitting...")
if (np.isnan(h2)): raise Exception("Invalid heritability estimate. Please double-check your input for any errors.")
print('h2: %0.6f'%h2)
return h2
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--bfilesim', metavar='bfilesim', default=None, help='Binary plink file')
parser.add_argument('--extractSim', metavar='extractSim', default=None, help='SNPs subset to use')
parser.add_argument('--prev', metavar='prev', type=float, default=None, help='Trait prevalence')
parser.add_argument('--numRemovePCs', metavar='numRemovePCs', type=int, default=10, help='Number of principal components to fit')
parser.add_argument('--pheno', metavar='pheno', default=None, help='Phenotype file in Plink format')
parser.add_argument('--eigen', metavar='eigen', default=None, help='eigen file')
parser.add_argument('--related', metavar='related', default=None, help='relatedness file')
parser.add_argument('--covar', metavar='covar', default=None, help='covariates file')
parser.add_argument('--lowtail', metavar='lowtail', type=int, default=0, help='Assume that both tails of the liabilities distribution are oversampled (0 or 1 - default 0)')
parser.add_argument('--relCutoff', metavar='relCutoff', type=float, default=0.05, help='relatedness cutoff (set to negative value to override relatedness check)')
parser.add_argument('--missingPhenotype', metavar='missingPhenotype', default='-9', help='identifier for missing values (default: -9)')
args = parser.parse_args()
if (args.bfilesim is None): raise Exception('--bfilesim must be supplied')
if (args.prev is None): raise Exception('--prev must be supplied')
if (args.pheno is None): raise Exception('--pheno must be supplied')
#Read bfilesim and pheno file for heritability computation
bed, phe = leapUtils.loadData(args.bfilesim, args.extractSim, args.pheno, args.missingPhenotype, loadSNPs=(args.eigen is None), standardize=True)
#Read/create eigendecomposition
if (args.eigen is not None): eigen = np.load(args.eigen)
else:
from . import eigenDecompose
eigen = eigenDecompose.eigenDecompose(bed)
#Compute relatedness
if (args.relCutoff <= 0): keepArr = np.ones(bed.iid.shape[0], dtype=bool)
else:
if (args.related is None):
bed2 = bed
if (args.extractSim is not None or args.eigen is not None): bed2, _ = leapUtils.loadData(args.bfilesim, None, args.pheno, args.missingPhenotype, loadSNPs=True)
keepArr = leapUtils.findRelated(bed2, args.relCutoff)
else:
keepArr = leapUtils.loadRelatedFile(bed, args.related)
#Read covar file
if (args.covar is not None):
covar = leapUtils.loadCovars(bed, args.covar)
covar -= covar.mean()
covar /= covar.std()
print('Read', covar.shape[1], 'covariates from file')
else:
covar = None
leapMain.calcH2(phe, args.prev, eigen, keepArr, covar, args.numRemovePCs, args.lowtail==1)