def obtainSignificantHitSNPs (chromosomes, individuals=('parents')):
hit_snps = [] # list of all significant hit snps
# process each chromosome in the list chromosomes
for chromosome in chromosomes:
print 'Processing chromosome ', chromosome
# obtain the SNP data (position and hit allele)
[hit_snps_positions, allele_info] = readHitSNPData (chromosome)
# create the vcf file object
vcf_filename = returnVCFFileName (chromosome)
# retrieve indices of the individuals
indices_individuals = vcf.returnColumns (vcf_filename, individuals)
n_individuals = len(indices_individuals)
# retrieve the deletions on the chromosome
deletions = vcf.returnDeletions (chromosome, indices_individuals, .96)
# retrieve the already processed hit snp data and incorporate the relevant info
hit_snps_chr = collectAllData (chromosome, individuals, deletions, allele_info, n_individuals)
# add the snps for this chromosome to the list
for hit_snp in hit_snps_chr:
hit_snps.append(hit_snp)
print 'Before FDR: ', len(hit_snps)
# apply fdr
hit_snps = fdr(hit_snps, 0.05)
print 'After FDR: ', len(hit_snps)
# apply material significance level (r^2 >= .8)
hit_snps = materialSignificanceTest (hit_snps)
print 'After Testing for material significance (R^2 >= .8): ', len(hit_snps)
writeToOutputFile (hit_snps, individuals)
def processSNPs (vcf_filename, chromosome, individuals=('parents', 'children')):
# gather the required data
[hit_snps_positions, hit_snps, discarded_snps_positions] = pr.readHitSNPs (chromosome)
genes = hg19.read (chromosome)
indices_individuals = vcf.returnColumns (vcf_filename, individuals)
deletions = vcf.returnDeletions (chromosome, indices_individuals, MAJOR_AF_THRESHOLD)
# open file
vcf_file = vcf.openVCFFile(vcf_filename)
vcf.discardVCFHeaders(vcf_file)
vcf_file.readline() # discard the column description header
for variant in vcf_file.readlines(): # move through the genetic variants
snp_output = []
data = [value for value in variant.split()]
if len(data[3]) == 1: # variant is a SNP and no deletion
snp_pos = int(data[1])
snp_type = hg19.determineType(snp_pos, genes)
dist_tss = hg19.distanceToTSS(snp_pos, genes)
if snp_pos in hit_snps_positions: # SNP is a hit SNP
[phase, snp_af] = pr.processHitSNP (snp_pos, data, hit_snps, indices_individuals)
if 1 - MAJOR_AF_THRESHOLD <= snp_af <= MAJOR_AF_THRESHOLD:
snp_output.append(['HITSNP', snp_pos, snp_af, snp_type, dist_tss])
dels = returnDeletions(snp_pos, snp_af, phase, deletions) # TODO implement
if len(dels) > 0:
snp_output.append(dels)
elif snp_pos not in discarded_snps_positions:
phase = vcf.returnPhase(data, indices_individuals)
snp_af = vcf.determineAlleleFrequency (phase)
if 1 - MAJOR_AF_THRESHOLD <= snp_af <= MAJOR_AF_THRESHOLD:
snp_output.append(['NONHITSNP', snp_pos, snp_af, snp_type, dist_tss])
dels = returnDeletions(snp_pos, snp_af, phase, deletions) # TODO implement
if len(dels) > 0:
snp_output.append(dels)
if len(snp_output) > 0:
writeSNPToOutputFile(snp_output, chromosome)
vcf_file.close()
def gatherData (vcf_filename, chromosome, individuals): [hit_snps_positions, hit_snps, discarded_snps_positions] = readHitSNPs (chromosome) genes = hg19.read (chromosome) indices_individuals = vcf.returnColumns (vcf_filename, individuals) deletions = vcf.returnDeletions (chromosome, indices_individuals, MAJOR_AF_THRESHOLD) return [hit_snps_positions, hit_snps, discarded_snps_positions, genes, indices_individuals, deletions]