#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
def demo(pop):
return int(pop.popSize() * 1.05)
pop = sim.Population(size=10000, loci=1)
pop.evolve(
initOps=[sim.InitSex(), sim.InitGenotype(freq=[0.7, 0.3])],
preOps=[
sim.Stat(popSize=True),
sim.PyEval(r'"%d %s --> " % (gen, subPopSize)'),
sim.ResizeSubPops(0, proportions=[0.5], at=2),
sim.MaPenetrance(loci=0, penetrance=[0.01, 0.2, 0.6], begin=4),
sim.DiscardIf('ind.affected()', exposeInd='ind', begin=4),
sim.Stat(popSize=True),
sim.PyEval(r'"%s --> " % subPopSize'),
],
matingScheme=sim.RandomMating(subPopSize=demo),
postOps=[sim.Stat(popSize=True),
sim.PyEval(r'"%s\n" % subPopSize')],
gen=6)
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(size=2000, loci=1, infoFields='fitness')
pop.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(freq=[.5, .5])],
preOps=[
sim.MaPenetrance(loci=0, penetrance=[0.01, 0.1, 0.2]),
sim.Stat(numOfAffected=True, step=25, vars='propOfAffected'),
sim.PyEval(r"'Percent of affected: %.3f\t' % propOfAffected",
step=50),
sim.InfoExec('fitness = not ind.affected()', exposeInd='ind')
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.PyEval(r"'%.4f\n' % alleleFreq[0][1]", step=50)
],
gen=151)
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
from simuPOP.sampling import indexToID
pop = sim.Population(size=15,
loci=5,
infoFields=['father_idx', 'mother_idx'],
ancGen=2)
pop.evolve(preOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.7, 0.3]),
],
matingScheme=sim.RandomMating(
numOffspring=(sim.UNIFORM_DISTRIBUTION, 2, 4),
ops=[sim.MendelianGenoTransmitter(),
sim.ParentsTagger()]),
postOps=sim.MaPenetrance(loci=3, penetrance=(0.1, 0.4, 0.7)),
gen=5)
indexToID(pop, reset=True)
# three information fields were added
print(pop.infoFields())
# save this population for future use
pop.save('log/pedigree.pop')
from simuPOP.sampling import drawAffectedSibpairSample
pop = sim.loadPopulation('log/pedigree.pop')
sample = drawAffectedSibpairSample(pop, families=2)
collected. A penetrance operator is needed to assign affection status
to each offspring.
'''
pop1 = pop.clone()
pop1.setAncestralDepth(1)
pop1.addInfoFields(['ind_id', 'father_id', 'mother_id'])
pop1.evolve(initOps=sim.IdTagger(),
matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(),
penetrance,
sim.IdTagger(),
sim.PedigreeTagger(),
],
numOffspring=2,
subPopSize=pop.popSize() * 2),
gen=1)
sim.stat(pop1, numOfAffected=True)
return drawAffectedSibpairSample(pop1, nFamilies)
if __name__ == '__main__':
pop = sim.Population(size=10000, loci=1)
sim.initGenotype(pop, freq=[0.5, 0.5])
sim.initSex(pop)
sim.maPenetrance(pop, loci=0, penetrance=[0.05, 0.15, 0.30])
sample = genAffectedSibpairSample(
pop, 100, sim.MaPenetrance(loci=0, penetrance=[0.05, 0.15, 0.30]))
#
sim.stat(sample, numOfAffected=True)
print(sample.dvars().numOfAffected, sample.dvars().numOfUnaffected)
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(1000, loci=[1], infoFields=['aff', 'numOfAff'])
# define virtual subpopulations by affection sim.status
pop.setVirtualSplitter(sim.AffectionSplitter())
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
preOps=[
# get affection sim.status for parents
sim.MaPenetrance(loci=0, wildtype=0, penetrance=[0.1, 0.2, 0.4]),
# set 'aff' of parents
sim.InfoExec('aff = ind.affected()', exposeInd='ind'),
],
# get number of affected parents for each offspring and store in numOfAff
matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(),
sim.SummaryTagger(mode=sim.SUMMATION, infoFields=['aff', 'numOfAff'])
]),
postOps=[
# get affection sim.status for offspring
sim.MaPenetrance(loci=0, wildtype=0, penetrance=[0.1, 0.2, 0.4]),
# calculate mean 'numOfAff' of offspring, for unaffected and affected subpopulations.
sim.Stat(meanOfInfo='numOfAff',
subPops=[(0, 0), (0, 1)],
vars=['meanOfInfo_sp']),
def genCaseControlSample(pop, nCase, nControl, penetrance):
'''Draw nCase affected and nControl unaffected individuals by producing
offspring from pop repeatedly until enough cases and controls are
collected. A penetrance operator is needed to assign affection status
to each offspring.
'''
sample = pop.clone()
sample.setVirtualSplitter(
sim.ProductSplitter([
sim.AffectionSplitter(),
sim.RangeSplitter([[0, nCase], [nCase, nCase + nControl]])
]))
sample.evolve(matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(), penetrance,
sim.DiscardIf(True, subPops=[(0, 0), (0, 3)])
],
subPopSize=nCase + nControl),
gen=1)
return sample
if __name__ == '__main__':
pop = sim.Population(size=10000, loci=1)
sim.initGenotype(pop, freq=[0.8, 0.2])
sim.initSex(pop)
sample = genCaseControlSample(
pop, 500, 500, sim.MaPenetrance(loci=0, penetrance=[0.01, 0.02, 0.10]))
#
sim.stat(sample, numOfAffected=True)
print(sample.dvars().numOfAffected, sample.dvars().numOfUnaffected)
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(5000, loci=3)
sim.initGenotype(pop, freq=[0.2] * 5)
# the multi-loci penetrance
sim.mlPenetrance(pop,
mode=sim.MULTIPLICATIVE,
ops=[
sim.MaPenetrance(loci=loc, penetrance=[0, 0.3, 0.6])
for loc in range(3)
])
# count the number of affected individuals.
sim.stat(pop, numOfAffected=True)
pop.dvars().numOfAffected
from simuPOP.sampling import drawCaseControlSample
def assoTest(pop):
'Draw case-control sample and apply association tests'
sample = drawCaseControlSample(pop, cases=500, controls=500)
sim.stat(sample,
association=(0, 2),
vars=['Allele_ChiSq_p', 'Geno_ChiSq_p', 'Armitage_p'])
print('Allele test: %.2e, %.2e, Geno test: %.2e, %.2e, Trend test: %.2e, %.2e' \
% (sample.dvars().Allele_ChiSq_p[0], sample.dvars().Allele_ChiSq_p[2],
sample.dvars().Geno_ChiSq_p[0], sample.dvars().Geno_ChiSq_p[2],
sample.dvars().Armitage_p[0], sample.dvars().Armitage_p[2]))
return True
pop = sim.Population(size=100000, loci=3)
pop.setVirtualSplitter(sim.ProportionSplitter([0.5, 0.5]))
pop.evolve(initOps=[
sim.InitSex(),
sim.InitGenotype(genotype=[0] * 3, subPops=[(0, 0)]),
sim.InitGenotype(genotype=[1] * 3, subPops=[(0, 1)]),
],
matingScheme=sim.RandomMating(
ops=sim.Recombinator(loci=[0, 1], rates=[0.01, 0.005])),
postOps=[
sim.MaPenetrance(loci=1, penetrance=[0.1, 0.2, 0.4]),
sim.PyOperator(func=assoTest, step=20),
],
gen=100)
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(5000, loci=3)
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.9] + [0.02]*5)
],
matingScheme=sim.RandomMating(),
postOps=[
sim.MaPenetrance(loci=0, penetrance=(0.01, 0.2, 0.3)),
sim.Stat(numOfAffected=True, vars='propOfAffected'),
sim.PyEval(r"'Gen: %d Prevalence: %.1f%%\n' % (gen, propOfAffected*100)"),
],
gen = 5
)
# description of this example.
#
import simuPOP as sim
pop = sim.Population(size=500, loci=1)
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter(),
sim.RangeSplitter([[0,500], [500, 1000]]),
])
)
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
matingScheme=sim.RandomMating(
ops=[
sim.MendelianGenoTransmitter(),
sim.MaPenetrance(loci=0, penetrance=[0, 0.01, 0.1]),
sim.DiscardIf(True, subPops=[
(0, 'Unaffected, Range [0, 500)'),
(0, 'Affected, Range [500, 1000)')])
],
subPopSize=1000,
),
gen = 1
)
sim.stat(pop, numOfAffected=True)
print(pop.dvars().numOfAffected, pop.dvars().numOfUnaffected)
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(10000, loci=1)
pop.setVirtualSplitter(sim.AffectionSplitter())
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(loci=0, freq=[0.8, 0.2])
],
matingScheme=sim.RandomMating(),
postOps=[
sim.MaPenetrance(penetrance=[0.1, 0.4, 0.6], loci=0),
sim.Stat(alleleFreq=0, subPops=[(0, 0), (0, 1)],
vars=['alleleFreq', 'alleleFreq_sp']),
sim.PyEval(r"'Gen: %d, freq: %.2f, freq (aff): %.2f, freq (unaff): %.2f\n' % " + \
"(gen, alleleFreq[0][1], subPop[(0,1)]['alleleFreq'][0][1]," + \
"subPop[(0,0)]['alleleFreq'][0][1])"),
],
gen = 5
)