def __init__(self,
sc=None,
app_name="Hail",
master=None,
local='local[*]',
log='hail.log',
quiet=False,
append=False,
min_block_size=1,
branching_factor=50,
tmp_dir='/tmp'):
self._hc1 = hail.HailContext(sc, app_name, master, local, log, quiet,
append, min_block_size, branching_factor,
tmp_dir)
self._counter = 0
#!./bin/pyhail.sh
import pyspark
import hail
from hail import KeyTable
from hail.representation import Interval
hc = hail.HailContext(log='log/08_plink_export.log', tmp_dir='tmp/hail')
#vds = hc.read('../MGRB.phase2.SNPtier12.match.vqsr.minrep.locusannot.WGStier12.unrelated.vds')
vds = hc.read(
'../MGRB.phase2.SNPtier12.match.vqsr.minrep.locusannot.WGStier12.unrelated.nocancer.over70.tgp.hrc.gnomad.dbsnp.clinvar.cato.eigen.vep.vds'
)
tier1_bed = KeyTable.import_bed(
'../../locus-annotations/source_data/HG001_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_nosomaticdel.bed'
)
# Extract good markers for rare variant comparisons.
# Definition of 'good markers':
# * In autosomes
# * In tier 1 regions.
for chrom in range(1, 23):
print 'Chromosome %d...' % chrom
(vds.filter_intervals(Interval.parse(
'%d' % chrom)).annotate_variants_table(
tier1_bed, root='va.tier1bed').filter_variants_expr(
'va.tier1bed == true && v.isAutosomal()',
keep=True).filter_samples_expr(
'"^Z" ~ s',
keep=False).split_multi().min_rep().export_plink(
'tmp/08_genotypes_%d' % chrom))
