pop = sim.Population(size=[6000], loci=1000, infoFields=["qtrait"])
def qtrait(geno):
trait = random.normalvariate(sum(geno) * 5, random.uniform(0.0001, 3))
if trait <= 0:
trait = random.uniform(0.0001, 1)
return trait
pop.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(),
postOps=[
sim.PyQuanTrait(loci=(0, 1, 2, 3, 4, 5, 6, 10, 100),
func=qtrait,
infoFields=["qtrait"])
],
gen=10)
geno = list()
for i in pop.individuals():
geno.append(i.genotype())
pheno = list()
for i in pop.individuals():
pheno.append(i.qtrait)
f = open("qtrait1.txt", "w")
f.write("\n".join(map(lambda x: str(x), pheno)))
f.close()
def main():
# Check for arguments passed
try:
opts, args = getopt.getopt(sys.argv[1:],
shortopts="vhd:p1:p2:s:n:l:e:f:i:m:g:r:",
longopts=[
"verbose", "help", "distribution=",
"parameter1=", "parameter2=", "size=",
"number=", "loci=", "effect=", "mean=",
"filename=", "heritability=", "gen=",
"rrate="
])
except getopt.GetoptError as err:
print(err)
usage()
sys.exit()
verbose = False
filename = "my"
size = 1000
number = 100
heritability = 0.2
mean = 2.0
gen = 5
rrate = 0.0
print "\n"
for o in opts:
if o[0] in ("-v", "--verbose"):
verbose = True
print("Verbose mode")
for o in opts:
if o[0] in ("-d", "--distribution"):
distribution = float(o[1])
if distribution == 0:
parameter1 = None
parameter2 = None
if verbose:
print "Simulation will occur with Normal Distribution"
elif distribution == 1:
if verbose:
print "Simulation will occur with Gamma Distribution"
for o in opts:
if o[0] in ("-p1", "--parameter1"):
parameter1 = float(o[1])
if verbose:
print "Gamma distribution will occur with alpha:", parameter1
for o in opts:
if o[0] in ("-p2", "--parameter2"):
parameter2 = float(o[1])
if verbose:
print "Gamma distribution will occur with beta:", parameter2
elif distribution != 0 or distribution != 1:
sys.exit(
"Error message: Distribution option must be either 0 or 1")
for o in opts:
if o[0] in ("-p2", "--parameter2"):
bbeta = float(o[1])
if verbose:
print "Gamma distribution will occur with beta:", bbeta
for o in opts:
if o[0] in ("-s", "--size"):
individuals = o[1].split(",")
individuals = map(int, individuals)
if verbose:
print "Population size/s is set at", individuals
for o in opts:
if o[0] in ("-h", "--help"):
usage()
sys.exit()
elif o[0] in ("-n", "--number"):
number = o[1]
if verbose:
print "Number of loci per individual is set at", number
elif o[0] in ("-l", "--loci"):
global loci
loci = o[1].split(",")
loci = map(int, loci)
if verbose:
print "Loci positions per individual are:", loci
elif o[0] in ("-e", "--effect"):
global effects
effects = o[1].split(",")
effects = map(float, effects)
if verbose:
print "Effects for loci per individual are:", effects
elif o[0] in ("-f", "--filename"):
filename = o[1]
if verbose:
print "File will be saved as:", filename
elif o[0] in ("-i", "--heritability"):
heritability = float(o[1])
if verbose:
print "Heritability for simulation specified as:", heritability
elif o[0] in ("-m", "--mean"):
mean = o[1].split(",")
mean = map(float, mean)
if len(mean) == 1 and len(individuals) > 1:
mean = numpy.array(mean)
mean = numpy.repeat(mean, len(individuals), axis=0)
mean = list(mean)
if verbose:
print "Population mean/s specified as:", mean
elif o[0] in ("-g", "--gen"):
gen = int(o[1])
if verbose:
print "Generations to evolve specified as:", gen
elif o[0] in ("-r", "--rrate"):
rrate = float(o[1])
if verbose:
print "Recombination will occur with rate:", rrate
## Start quantitative trait simulation
if verbose:
print "Creating population..."
pop = sim.Population(size=individuals,
loci=int(number),
infoFields=["qtrait"])
if verbose:
print "Evolving population..."
pop.evolve(
initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=rrate)),
postOps=[
sim.PyQuanTrait(loci=loci,
func=additive_model,
infoFields=["qtrait"])
],
gen=gen)
if verbose:
print "Coalescent process complete. Population evolved with", pop.numSubPop(
), "sub-populations."
genotypes = list()
for i in pop.individuals():
genotypes.append(i.genotype())
phenotypes = list()
for i in pop.individuals():
phenotypes.append(i.qtrait)
# fun() obtains the heritability equation set to zero for various settings of sigma (standard deviation)
#NOTE: May need to tweak gamma distribution parameters to be appropriate for data!
def fun(sigma, h):
x_exact = list()
count = 0
for i in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_exact.append(current_mean + i)
count += 1
x_random = list()
#bbeta=((sigma**2)/current_mean) #Set up approximate beta variable for gamma distribution
count = 0
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_random.append(random.normalvariate(current_mean + each, sigma))
count += 1
r = pearsonr(x_exact, x_random)[0]
return r - math.sqrt(h)
if verbose:
print "Building polynomial model for variance tuning..."
# Fits a polynomial model in numpy to the values obtained from the fun() function
points = list()
for i in drange(0, max(effects) * 10, 0.001):
points.append(i)
y_points = list()
for i in points:
y_points.append(fun(i, heritability))
z = numpy.polyfit(x=points, y=y_points, deg=3)
p = numpy.poly1d(z)
# Netwon's method finds the polynomial model's roots
def newton(p):
xn = 100
p_d = p.deriv()
count = 0
while abs(p(xn)) > 0.01:
if count > 1000:
print "Unable to converge after 1000 iterations...\nPlease choose different settings."
usage()
sys.exit()
count += 1
xn = xn - p(xn) / p_d(xn)
if xn < 0.0:
xn = 0.0
if verbose:
print "Estimated variance of phenotypes for specified heriability: ", xn
return xn
if verbose:
print "Using Newton's method to find polynomial roots..."
# Files are saved to the specified location
estimated_variance = newton(p)
new_phenotypes = list()
count = 0
for o in opts:
if o[0] in ("-d", "--distribution"):
if distribution == 0:
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
new_phenotypes.append(
random.normalvariate(current_mean + each,
estimated_variance))
count += 1
elif distribution == 1:
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
new_phenotypes.append(
random.gammavariate(
(current_mean + each) / parameter2,
((estimated_variance / parameter1)**0.5)))
count += 1
f = open(filename + "_qtrait.txt", "w")
f.write("\n".join(map(lambda x: str(x), new_phenotypes)))
f.close()
numpy.savetxt(filename + "_kt_ote2.txt",
numpy.column_stack((loci, numpy.array(effects))))
export(pop, format='ped', output=filename + '_genomes.ped')
export(pop, format='map', output=filename + '_genomes.map')
print "\n\n"
def qtrait(geno, age):
'Return two traits that depends on genotype and age'
return random.normalvariate(age * sum(geno), 10)
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(prop=[0.8, 0.2]),
],
matingScheme=sim.RandomMating(),
postOps=[
# use random age for simplicity
sim.InitInfo(lambda: random.randint(20, 75), infoFields='age'),
sim.PyQuanTrait(loci=(1), func=qtrait, infoFields=['qtrait1']),
sim.Stat(meanOfInfo=['qtrait1'],
subPops=[(0, sim.ALL_AVAIL)],
vars='meanOfInfo_sp'),
sim.PyEval(
r"'Mean of trait1: %.3f (age < 40), %.3f (age >=40)\n' % "
"(subPop[(0,0)]['meanOfInfo']['qtrait1'], subPop[(0,1)]['meanOfInfo']['qtrait1'])"
),
],
gen=100)
qtrait1_ls = list()
for ind in pop.individuals():
qtrait1_ls.append(ind.qtrait1)
f = open("qtrait1.txt", "w")
pop.setVirtualSplitter(sim.InfoSplitter(field='age', cutoff=[40]))
def qtrait(geno, age):
'Return two traits that depends on genotype and age'
return random.normalvariate(age * sum(geno),
10), random.randint(0, 10 * sum(geno))
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.2, 0.8]),
],
matingScheme=sim.RandomMating(),
postOps=[
# use random age for simplicity
sim.InitInfo(lambda: random.randint(20, 75), infoFields='age'),
sim.PyQuanTrait(loci=(0, 1),
func=qtrait,
infoFields=['qtrait1', 'qtrait2']),
sim.Stat(meanOfInfo=['qtrait1'],
subPops=[(0, sim.ALL_AVAIL)],
vars='meanOfInfo_sp'),
sim.PyEval(
r"'Mean of trait1: %.3f (age < 40), %.3f (age >=40)\n' % "
"(subPop[(0,0)]['meanOfInfo']['qtrait1'], subPop[(0,1)]['meanOfInfo']['qtrait1'])"
),
],
gen=5)
def main():
## Check for arguments passed
try:
opts, args = getopt.getopt(sys.argv[1:],
shortopts="vhs:n:l:e:f:",
longopts=[
"verbose", "help", "size=", "number=",
"loci=", "effect=", "filename="
])
except getopt.GetoptError as err:
print(err)
usage()
sys.exit()
verbose = False
has_filename = False
print "\n"
for o in opts:
if o[0] in ("-v", "--verbose"):
verbose = True
print("Verbose mode")
for o in opts:
if o[0] in ("-h", "--help"):
usage()
sys.exit()
elif o[0] in ("-s", "--size"):
individuals = o[1]
if verbose:
print "Population size is set at", individuals
elif o[0] in ("-n", "--number"):
number = o[1]
if verbose:
print "Number of loci per individual is set at", number
elif o[0] in ("-l", "--loci"):
loci = o[1].split(",")
loci = map(int, loci)
if verbose:
print "Loci positions per individual are:", loci
elif o[0] in ("-e", "--effect"):
global effects
effects = o[1].split(",")
effects = map(float, effects)
if verbose:
print "Effects for loci per individual are:", effects
elif o[0] in ("-f", "--filename"):
filename = o[1]
has_filename = True
if verbose:
print "File will be saved as:", filename
## Start quantitative trait simulation
if verbose:
print "Creating population..."
pop = sim.Population(size=int(individuals),
loci=int(number),
infoFields=["qtrait"])
if verbose:
print "Evolving population..."
pop.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(),
postOps=[
sim.PyQuanTrait(loci=loci,
func=trait,
infoFields=["qtrait"])
],
gen=5)
genotypes = list()
for i in pop.individuals():
genotypes.append(i.genotype())
phenotypes = list()
for i in pop.individuals():
phenotypes.append(i.qtrait)
if has_filename is False:
filename = "my"
f = open(filename + "_qtrait.txt", "w")
f.write("\n".join(map(lambda x: str(x), phenotypes)))
f.close()
saveCSV(pop, filename + "_genomes.csv")
print "\n\n"
def main():
## Check for arguments passed
try:
opts, args = getopt.getopt(sys.argv[1:],
shortopts="vhs:n:l:e:f:i:",
longopts=[
"verbose", "help", "size=", "number=",
"loci=", "effect=", "filename=",
"herit="
])
except getopt.GetoptError as err:
print(err)
usage()
sys.exit()
verbose = False
has_filename = False
has_heritability = False
filename = "my"
heritability = 0.2
print "\n"
for o in opts:
if o[0] in ("-v", "--verbose"):
verbose = True
print("Verbose mode")
for o in opts:
if o[0] in ("-h", "--help"):
usage()
sys.exit()
elif o[0] in ("-s", "--size"):
individuals = o[1]
if verbose:
print "Population size is set at", individuals
elif o[0] in ("-n", "--number"):
number = o[1]
if verbose:
print "Number of loci per individual is set at", number
elif o[0] in ("-l", "--loci"):
global loci
loci = o[1].split(",")
loci = map(int, loci)
if verbose:
print "Loci positions per individual are:", loci
elif o[0] in ("-e", "--effect"):
global effects
effects = o[1].split(",")
effects = map(float, effects)
if verbose:
print "Effects for loci per individual are:", effects
elif o[0] in ("-f", "--filename"):
filename = o[1]
has_filename = True
if verbose:
print "File will be saved as:", filename
elif o[0] in ("-i", "--herit"):
heritability = float(o[1])
has_heritability = True
if verbose:
print "Heritability for simulation specified as:", heritability
## Start quantitative trait simulation
if verbose:
print "Creating population..."
pop = sim.Population(size=int(individuals),
loci=int(number),
infoFields=["qtrait"])
if verbose:
print "Evolving population..."
pop.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(),
postOps=[
sim.PyQuanTrait(loci=loci,
func=additive_model,
infoFields=["qtrait"])
],
gen=5)
if verbose:
print "Coalescent process complete. Population evolved."
genotypes = list()
for i in pop.individuals():
genotypes.append(i.genotype())
#print i.genotype()
phenotypes = list()
for i in pop.individuals():
phenotypes.append(i.qtrait)
#print i.qtrait
def fun(sigma, h):
x_exact = phenotypes
x_random = list()
for each in phenotypes:
x_random.append(random.normalvariate(each, sigma))
r = pearsonr(x_exact, x_random)[0]
return r - math.sqrt(h)
#print fun(2.25, 0.25)
if verbose:
print "Building polynomial model for variance tuning..."
points = list()
for i in drange(0, max(effects) * 10, 0.001):
points.append(i)
y_points = list()
for i in points:
y_points.append(fun(i, heritability))
z = numpy.polyfit(x=points, y=y_points, deg=3)
p = numpy.poly1d(z)
def newton(p):
xn = 100
p_d = p.deriv()
count = 0
while abs(p(xn)) > 0.01:
if count > 1000:
print "Unable to converge after 1000 iterations..."
sys.exit()
count += 1
xn = xn - p(xn) / p_d(xn)
if verbose:
print "Estimated variance of phenotypes for specified heriability using Newton's method: ", xn
return xn
if verbose:
print "Using Newton's method to find polynomial roots..."
estimated_variance = newton(p)
new_phenotypes = list()
for each in phenotypes:
new_phenotypes.append(random.normalvariate(each, estimated_variance))
f = open(filename + "_qtrait.txt", "w")
f.write("\n".join(map(lambda x: str(x), new_phenotypes)))
f.close()
saveCSV(pop, filename + "_genomes.csv")
print "\n\n"
sim.IdTagger()
],
# The order should be: becoming a smurf or not since previous day, dying or not, aging one day
# if lucky enough, and then mate at that age.
preOps=[
sim.InfoExec("luck = random.random()"),
sim.InfoExec(
"smurf = 1.0 if ((ind.smurf == 1) or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0",
exposeInd='ind'),
sim.DiscardIf(natural_death),
sim.InfoExec("age += 1")
],
matingScheme=sim.HeteroMating([
sim.CloneMating(subPops=[(0, 0), (0, 1), (0, 2)], weight=-1),
sim.RandomMating(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.PyQuanTrait(loci=[0], func=qtrait, infoFields=['a', 'b']),
sim.InfoExec("smurf = 0.0"),
sim.MendelianGenoTransmitter()
],
weight=1,
subPops=[(0, 1)],
numOffspring=(sim.UNIFORM_DISTRIBUTION, 10, 50))
],
subPopSize=demo),
gen=args.G)
pop = simu.extract(0)
outputStructure(pop)
A = sum(geno[:20]) + normalvariate(0, 2.5)
B = sum(geno[20:40]) + normalvariate(0, 2.5)
I = sum(geno[40:60]) + normalvariate(0, 2.5)
D = B + I - A + normalvariate(0, sigma**2)
return A, B, I, D
pop = sim.Population(100000, loci=[1] * 40, infoFields=['A', 'B', 'I', 'D'])
pop.evolve(
initOps=[
sim.InitSex(maleProp=0.5),
sim.InitGenotype(freq=[0.5, 0.5]),
],
preOps=[
sim.PyQuanTrait(func=traits,
loci=sim.ALL_AVAIL,
infoFields=['A', 'B', 'I', 'D']),
sim.PyOperator(func=lambda pop: pop.sortIndividuals('D') is None),
],
matingScheme=sim.HomoMating(chooser=sim.SequentialParentsChooser(),
generator=sim.OffspringGenerator(
ops=sim.MendelianGenoTransmitter(),
numOffspring=2,
sexMode=(sim.NUM_OF_MALES, 1))),
finalOps=sim.PyQuanTrait(func=traits,
loci=sim.ALL_AVAIL,
infoFields=['A', 'B', 'I', 'D']),
gen=10)
from rpy import r
def main():
# First, grab and interpret command line arguments
parser = argparse.ArgumentParser(
description="Command line arguments for Simulate")
parser.add_argument("-v",
"--verbose",
help="Triggers verbose mode",
action="store_true")
parser.add_argument("-d",
"--distribution",
default="normal",
choices=["normal", "gamma"],
help="Distribution option")
parser.add_argument(
"-p1",
"--parameter1",
default=3.0,
type=float,
help="Shape parameter (only used if distribution choice is gamma)")
parser.add_argument(
"-p2",
"--parameter2",
default=1.5,
type=float,
help="Scale parameter (only used if distribution choice is gamma)")
parser.add_argument("-s",
"--size",
required=True,
type=int,
nargs="+",
help="Specify population size(s)")
parser.add_argument("-l",
"--loci",
required=True,
type=int,
nargs="+",
help="Loci with effects")
parser.add_argument("-n",
"--number",
required=True,
default=3000,
type=int,
help="Number of loci per population or sub-population")
parser.add_argument("-e",
"--effect",
required=True,
type=float,
nargs="+",
help="Effect size(s) for the loci specified")
parser.add_argument("-i",
"--heritability",
default=0.2,
type=restricted_float,
help="Heritability coefficient for population")
parser.add_argument("-m",
"--mean",
default=2.0,
type=float,
nargs="+",
help="Mean(s) for population phenotype(s)")
parser.add_argument("-g",
"--gen",
default=5,
type=int,
help="Number of generations for population to evolve")
parser.add_argument("-r",
"--rrate",
default=0.0,
type=restricted_float,
help="Recombination rate for given population")
parser.add_argument("-f",
"--filename",
default="my",
type=str,
help="Prefix for output file set")
args = parser.parse_args()
verbose = args.verbose
if verbose:
print "Verbose mode"
distribution = args.distribution
if verbose:
print "Simulation will occur with " + distribution + " distribution"
parameter1 = args.parameter1
if verbose and distribution == "gamma":
print "Gamma distrbution will occur with alpha parameter:", parameter1
parameter2 = args.parameter2
if verbose and distribution == "gamma":
print "Gamma distribution will occur with beta parameter", parameter2
individuals = args.size
if verbose:
print "Population size(s) set at", individuals
loci = args.loci
if verbose:
print "Loci positions per individual set as", loci
number = args.number
if verbose:
print "Number of loci per population set as", number
global effects
effects = args.effect
if verbose:
print "Effects for loci per individual are", effects
heritability = args.heritability
mean = args.mean
if len(mean) == 1 and len(individuals) > 1:
mean = numpy.array(mean)
mean = numpy.repeat(mean, len(individuals), axis=0)
mean = list(mean)
if verbose:
print "Population mean(s) set as", mean
gen = args.gen
if verbose:
print "Number of generations to evolve set as", gen
rrate = args.rrate
if verbose:
print "Recombination rate set as", rrate
filename = args.filename
if verbose:
"File will be saved as", filename
## Start quantitative trait simulation via simuPOP
if verbose:
print "Creating population..."
pop = sim.Population(size=individuals,
loci=int(number),
infoFields=["qtrait"])
if verbose:
print "Evolving population..."
type(gen)
pop.evolve(
initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=rrate)),
postOps=[
sim.PyQuanTrait(loci=loci,
func=additive_model,
infoFields=["qtrait"])
],
gen=gen)
if verbose:
print "Coalescent process complete. Population evolved with", pop.numSubPop(
), "sub-populations."
genotypes = list()
for i in pop.individuals():
genotypes.append(i.genotype())
phenotypes = list()
for i in pop.individuals():
phenotypes.append(i.qtrait)
# fun() obtains the heritability equation set to zero for various settings of sigma (standard deviation)
def fun(sigma, h):
x_exact = list()
count = 0
for i in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_exact.append(current_mean + i)
count += 1
x_random = list()
count = 0
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_random.append(random.normalvariate(current_mean + each, sigma))
count += 1
r = pearsonr(x_exact, x_random)[0]
return r - math.sqrt(h)
if verbose:
print "Building polynomial model for variance tuning..."
# Polyfit fits a polynomial model in numpy to the values obtained from the fun() function
points = list()
for i in drange(0, max(effects) * 10, 0.001):
points.append(i)
y_points = list()
for i in points:
y_points.append(fun(i, heritability))
z = numpy.polyfit(x=points, y=y_points, deg=3)
p = numpy.poly1d(z)
# Netwon's method finds the polynomial model's roots
def newton(p):
xn = 100
p_d = p.deriv()
count = 0
while abs(p(xn)) > 0.01:
if count > 1000:
print "Unable to converge after 1000 iterations...\nPlease choose different settings."
sys.exit()
count += 1
xn = xn - p(xn) / p_d(xn)
if xn < 0.0:
xn = 0.0
if verbose:
print "Estimated variance of phenotypes for specified heriability: ", xn
return xn
if verbose:
print "Using Newton's method to find polynomial roots..."
# Files are saved to the specified location
estimated_variance = newton(p)
new_phenotypes = list()
count = 0
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
if distribution == "normal":
new_phenotypes.append(
random.normalvariate(current_mean + each, estimated_variance))
elif distribution == "gamma":
new_phenotypes.append(
random.gammavariate(
(current_mean + each) / parameter2,
numpy.sqrt(estimated_variance / parameter1)))
count += 1
f = open(filename + "_qtrait.txt", "w")
f.write("\n".join(map(lambda x: str(x), new_phenotypes)))
f.close()
numpy.savetxt(filename + "_kt_ote.txt",
numpy.column_stack((loci, numpy.array(effects))),
fmt='%i %10.7f')
saveCSV(pop, filename + "_genomes.csv")
# Call the convert.R script to convert the output into usable PLINK files
# Will probably need to change this line to something more generalizable in the near future
os.system("Rscript convert.R " + filename)
print "\n\n"
# Random mating with fixed number of offspring
# https://bopeng.github.io/simuPOP/userGuide_ch6_sec1.html#determine-the-number-of-offspring-during-mating
matingScheme = sim.RandomMating(
ops = [
# average recombination across all chromosomes
sim.Recombinator((1*len(pop.numLoci()))/sum(pop.numLoci())), # avg of 2 recombinations == poisson with rate 2
sim.PedigreeTagger(),
sim.IdTagger()
],
# each cross results in 10 offspring
numOffspring = 10
),
# after mating assign quantitative trait based on genotype
postOps = [
sim.PyQuanTrait(
func = qtrait,
loci = adv_loci,
infoFields = ['trait', 'nalleles'])
],
# 11 generations (the first generation are the founders)
gen = 11
)
#### save results ####
def write_pedigree(pop, outfile):
out = open(outfile, "w")
# get info field names
info_names = pop.infoFields()
def main():
## Check for arguments passed
try:
opts, args = getopt.getopt(sys.argv[1:],
shortopts="vhs:n:l:e:f:i:m:g:r:",
longopts=[
"verbose", "help", "size=", "number=",
"loci=", "effect=", "mean=",
"filename=", "heritability=", "gen=",
"rrate="
])
except getopt.GetoptError as err:
print(err)
usage()
sys.exit()
verbose = False
filename = "my"
size = 1000
number = 100
heritability = 0.2
mean = 2.0
gen = 5
rrate = 0.0
print "\n"
for o in opts:
if o[0] in ("-v", "--verbose"):
verbose = True
print("Verbose mode")
for o in opts:
if o[0] in ("-s", "--size"):
individuals = o[1].split(",")
individuals = map(int, individuals)
if verbose:
print "Population size/s is set at", individuals
for o in opts:
if o[0] in ("-h", "--help"):
usage()
sys.exit()
elif o[0] in ("-n", "--number"):
number = o[1]
if verbose:
print "Number of loci per individual is set at", number
elif o[0] in ("-l", "--loci"):
global loci
loci = o[1].split(",")
loci = map(int, loci)
if verbose:
print "Loci positions per individual are:", loci
elif o[0] in ("-e", "--effect"):
global effects
effects = o[1].split(",")
effects = map(float, effects)
if verbose:
print "Effects for loci per individual are:", effects
elif o[0] in ("-f", "--filename"):
filename = o[1]
if verbose:
print "File will be saved as:", filename
elif o[0] in ("-i", "--heritability"):
heritability = float(o[1])
if verbose:
print "Heritability for simulation specified as:", heritability
elif o[0] in ("-m", "--mean"):
mean = o[1].split(",")
mean = map(float, mean)
if len(mean) == 1 and len(individuals) > 1:
mean = numpy.array(mean)
mean = numpy.repeat(mean, len(individuals), axis=0)
mean = list(mean)
if verbose:
print "Population mean/s specified as:", mean
elif o[0] in ("-g", "--gen"):
gen = int(o[1])
if verbose:
print "Generations to evolve specified as:", gen
elif o[0] in ("-r", "--rrate"):
rrate = float(o[1])
if verbose:
print "Recombination will occur with rate:", rrate
## Start quantitative trait simulation
if verbose:
print "Creating population..."
pop = sim.Population(size=individuals,
loci=int(number),
infoFields=["qtrait"])
if verbose:
print "Evolving population..."
pop.evolve(
initOps=[sim.InitSex(),
sim.InitGenotype(prop=[0.7, 0.3])],
matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=rrate)),
postOps=[
sim.PyQuanTrait(loci=loci,
func=additive_model,
infoFields=["qtrait"])
],
gen=gen)
if verbose:
print "Coalescent process complete. Population evolved with", pop.numSubPop(
), "sub-populations."
genotypes = list()
for i in pop.individuals():
genotypes.append(i.genotype())
phenotypes = list()
for i in pop.individuals():
phenotypes.append(i.qtrait)
def fun(sigma, h):
x_exact = list()
count = 0
for i in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_exact.append(current_mean + i)
count += 1
x_random = list()
count = 0
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
x_random.append(random.normalvariate(current_mean + each, sigma))
count += 1
r = pearsonr(x_exact, x_random)[0]
return r - math.sqrt(h)
if verbose:
print "Building polynomial model for variance tuning..."
points = list()
for i in drange(0, max(effects) * 10, 0.001):
points.append(i)
y_points = list()
for i in points:
y_points.append(fun(i, heritability))
z = numpy.polyfit(x=points, y=y_points, deg=3)
p = numpy.poly1d(z)
def newton(p):
xn = 100
p_d = p.deriv()
count = 0
while abs(p(xn)) > 0.01:
if count > 1000:
print "Unable to converge after 1000 iterations...\nPlease choose different settings."
usage()
sys.exit()
count += 1
xn = xn - p(xn) / p_d(xn)
if xn < 0.0:
xn = 0.0
if verbose:
print "Estimated variance of phenotypes for specified heriability: ", xn
return xn
if verbose:
print "Using Newton's method to find polynomial roots..."
estimated_variance = newton(p)
new_phenotypes = list()
count = 0
for each in phenotypes:
current_mean = mean[pop.subPopIndPair(count)[0]]
new_phenotypes.append(
random.normalvariate(current_mean + each, estimated_variance))
count += 1
f = open(filename + "_qtrait.txt", "w")
f.write("\n".join(map(lambda x: str(x), new_phenotypes)))
f.close()
numpy.savetxt(filename + "_kt_ote2.txt",
numpy.column_stack((loci, numpy.array(effects))))
saveCSV(pop, filename + "_genomes.csv")
print "\n\n"
sim.InfoExec("luck = random.random()"),
sim.InfoExec("smurf = 1.0 if (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0", exposeInd='ind'),
sim.DiscardIf(natural_death),
sim.InfoExec("age += 1"),
sim.PySelector(loci=[0], func=fitness_func)
],
matingScheme = sim.HeteroMating(
[
sim.CloneMating(subPops = [(0,0), (0,1), (0,2)], weight = -1),
sim.RandomMating(
ops = [
sim.IdTagger(),
sim.PedigreeTagger(),
sim.InfoExec("smurf = 0.0"),
sim.MendelianGenoTransmitter(),
sim.PyQuanTrait(loci = sim.ALL_AVAIL, func = MaleEffect, infoFields = ['a', 'b', 't0'])
],
weight = 1,
subPops = [(0,1)],
numOffspring = 1
)
],
subPopSize = demo
),
postOps = [
sim.PyOperator(func=OutputStats, step=100)
],
gen=args.G
)
pop = simu.extract(0)
def MutationSelection(N=1000,
generations=10000,
X_loci=100,
A_loci=0,
AgingModel='two_phases',
seed=2001,
reps=1,
InitMutFreq=0.001,
aging_a1=0.003,
aging_a2=0.05,
aging_b=-0.019,
aging_k=0.1911,
MutRate=0.001,
StatsStep=100,
OutPopPrefix='z1',
PrintFreqs=False,
debug=False):
'''Creates and evolves a population to reach mutation-selection balance.'''
if debug:
sim.turnOnDebug('DBG_ALL')
else:
sim.turnOffDebug('DBG_ALL')
sim.setRNG('mt19937', seed)
pop = sim.Population(N,
loci=[X_loci, A_loci],
ploidy=2,
chromTypes=[sim.CHROMOSOME_X, sim.AUTOSOME],
infoFields=[
'age', 'a', 'b', 'smurf', 'ind_id', 'father_id',
'mother_id', 'luck', 't0', 'fitness'
])
pop.setVirtualSplitter(
sim.CombinedSplitter(
splitters=[
sim.ProductSplitter(splitters=[
sim.InfoSplitter(field='age', cutoff=9),
sim.InfoSplitter(field='smurf', values=[0, 1])
]),
sim.SexSplitter(),
sim.InfoSplitter(field='age', values=0)
],
vspMap=[(0), (2), (1, 3), (4), (5), (6)],
names=['larvae', 'adults', 'smurfs', 'males', 'females', 'zero']))
pop.dvars().k = aging_k
pop.dvars().N = N
pop.dvars().seed = seed
pop.dvars().X_loci = X_loci
pop.dvars().A_loci = A_loci
pop.dvars().AgingModel = AgingModel
exec("import random\nrandom.seed(seed)", pop.vars(), pop.vars())
exec("import math", pop.vars(), pop.vars())
simu = sim.Simulator(pop, rep=reps)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[1 - InitMutFreq, InitMutFreq]),
sim.InitInfo([0], infoFields='age'),
sim.InitInfo([aging_a1], infoFields='a'),
sim.InitInfo([aging_b], infoFields='b'),
sim.InitInfo(lambda: random.random(), infoFields='luck'),
sim.InfoExec('t0 = -ind.b / ind.a', exposeInd='ind'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.IdTagger(),
sim.PyExec('XFreqChange={}'),
sim.PyExec('AFreqChange={}')
],
preOps=[
sim.InfoExec('luck = random.random()'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.DiscardIf(natural_death(AgingModel)),
sim.InfoExec('age += 1'),
sim.PySelector(func=fitness_func1)
],
matingScheme=sim.HeteroMating([
sim.CloneMating(subPops=[(0, 0), (0, 1), (0, 2)], weight=-1),
sim.RandomMating(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.InfoExec('smurf = 0.0'),
sexSpecificRecombinator(
rates=[0.75 / X_loci for x in range(X_loci)] +
[2.07 / A_loci for x in range(A_loci)],
maleRates=0.0),
sim.PyQuanTrait(loci=sim.ALL_AVAIL,
func=TweakAdditiveRecessive(
aging_a1, aging_a2, aging_b, X_loci),
infoFields=['a', 'b'])
],
weight=1,
subPops=[(0, 1)],
numOffspring=1)
],
subPopSize=demo),
postOps=[
sim.SNPMutator(u=MutRate, subPops=[(0, 5)]),
sim.Stat(alleleFreq=sim.ALL_AVAIL, step=StatsStep),
sim.IfElse(
'X_loci > 0',
ifOps=[
sim.PyExec(
'XFreqChange[gen] = [alleleFreq[x][1] for x in range(X_loci)]'
)
],
elseOps=[sim.PyExec('XFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
'A_loci > 0',
ifOps=[
sim.PyExec(
'AFreqChange[gen] = [alleleFreq[a][1] for a in range(X_loci, pop.totNumLoci())]',
exposePop='pop')
],
elseOps=[sim.PyExec('AFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
PrintFreqs,
ifOps=[
sim.PyEval(
r"str(rep) + '\t' + str(gen) + '\t' + '\t'.join(map('{0:.4f}'.format, XFreqChange[gen])) + '\t\t' + '\t'.join(map('{0:.4f}'.format, AFreqChange[gen])) + '\n'"
)
],
step=StatsStep),
sim.TerminateIf(
'sum([alleleFreq[x][0] * alleleFreq[x][1] for x in range(X_loci + A_loci)]) == 0'
)
],
gen=generations)
i = 0
for pop in simu.populations():
pop.save('{}_{}.pop'.format(OutPopPrefix, i))
i += 1