def read_gs_chromosome_template(templatef):
"""
Reads a genomeSIMLA format chromosome template file
:param templatef: The filename of the template file
:type templatef: string
:rtype: A ChromosomeTemplate object corresponding to the file
"""
with smartopen(templatef) as f:
label = f.readline().strip() # The label and
f.readline() # the number of markers, both of which we dont need.
c = pydigree.ChromosomeTemplate(label=label)
# genomeSIMLA chromosome files have marginal recombination probs
# instead of map positions. We'll have to keep track of what the
# last position was and add to it to get it into the shape we want
# it to be in.
last_cm = 0
for line in f:
if line == '\n':
continue
label, _, minf, cm, bp = line.strip().split()
bp = int(bp)
cm = float(cm)
last_cm += cm
c.add_genotype(float(minf), last_cm, label=label, bp=bp)
return c
def test_counters():
nchrom = 50
pop = pydigree.Population()
for i in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
assert pop.chromosomes.nchrom() == nchrom
assert pop.chromosomes.nloci() == nchrom
def test_randomloci():
nchrom = 100
nloc = 50
pop = pydigree.Population()
for i in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
for i in range(100):
locs = list(pop.chromosomes.select_random_loci(nloc))
locs.sort()
# all the chromosomes have 1 marker
assert all(x[1] == 0 for x in locs)
# all the chromosomes are valid indices
assert all(0 <= x[0] < nchrom for x in locs)
# No duplicates!
for i in range(1, nloc):
assert locs[i] != locs[i - 1]
import numpy as np
import pydigree as pyd
from pydigree.simulation import QuantitativeTrait
ninds = 5000
nloc = 1000
maf = 0.5
traitname = 'synthetic'
# Create population
pop = pyd.Population()
# Create chromosomes
for i in range(100):
c = pyd.ChromosomeTemplate()
c.add_genotype(maf, 0)
pop.add_chromosome(c)
# Create trait QuantitativeTrait
trait = QuantitativeTrait('synthetic',
'quantitative',
chromosomes=pop.chromosomes)
for i in range(100):
trait.add_effect((i, 0), 1 * (-1 if i % 2 else 1))
print('Locus mean genotypic value: {}'.format(
trait.effects[0].expected_genotypic_value))
print('Locus variance: {}'.format(trait.effects[0].locus_additive_variance))
print('Expected trait mean: {}'.format(trait.expected_genotypic_value))
#!/usr/bin/env python3
import pydigree
from pydigree.simulation import QuantitativeTrait
nchrom = 1
popsize = 500
ngen = 100
pop = pydigree.Population(popsize)
for x in range(nchrom):
c = pydigree.ChromosomeTemplate()
c.add_genotype(0.1, 0)
pop.add_chromosome(c)
trait = QuantitativeTrait('q', 'quantitative')
for i in range(nchrom):
trait.add_effect_liability((i, 0), 0, 1)
print(trait)
pop.initialize_pool()
for x in range(popsize):
pop.add_founder_individual()
def gen(ninds):
"Advance by one generation, with ninds in the new generation"
pop.advance_generation(ninds)
pop.get_genotypes()
pop.remove_ancestry()
return pop.allele_frequency((0, 0), 1)