def _geneflow(self, pop1, pop2, start, rate):
return msp.MigrationRateChange(time=start,
rate=rate,
matrix_index=(pop1, pop2))
def __init__(self):
super().__init__()
self.generation_time = default_generation_time
T_AF = 148e3 / self.generation_time
T_OOA = 51e3 / self.generation_time
T_EU0 = 23e3 / self.generation_time
T_EG = 5115 / self.generation_time
# Growth rates
r_EU0 = 0.00307
r_EU = 0.0195
r_AF = 0.0166
# population sizes
N_A = 7310
N_AF1 = 14474
N_B = 1861
N_EU0 = 1032
N_EU1 = N_EU0 / math.exp(-r_EU0 * (T_EU0 - T_EG))
# migration rates
m_AF_B = 15e-5
m_AF_EU = 2.5e-5
# present Ne
N_EU = N_EU1 / math.exp(-r_EU * T_EG)
N_AF = N_AF1 / math.exp(-r_AF * T_EG)
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_AF,
growth_rate=r_AF),
msprime.PopulationConfiguration(initial_size=N_EU,
growth_rate=r_EU)
]
self.migration_matrix = [
[0, m_AF_EU],
[m_AF_EU, 0],
]
self.demographic_events = [
msprime.MigrationRateChange(time=T_EG,
rate=m_AF_EU,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EG,
rate=m_AF_EU,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EG,
growth_rate=r_EU0,
initial_size=N_EU1,
population_id=1),
msprime.PopulationParametersChange(time=T_EG,
growth_rate=0,
initial_size=N_AF1,
population_id=0),
msprime.MigrationRateChange(time=T_EU0,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU0,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU0,
initial_size=N_B,
growth_rate=0,
population_id=1),
msprime.MassMigration(time=T_OOA,
source=1,
destination=0,
proportion=1.0),
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
def __init__(self):
super().__init__()
# First we set out the maximum likelihood values of the various parameters
# given in Table 1 (under archaic admixture).
N_0 = 3600
N_YRI = 13900
N_B = 880
N_CEU0 = 2300
N_CHB0 = 650
# Times are provided in years, so we convert into generations.
# In the published model, the authors used a generation time of 29 years to
# convert from genetic to physical units
self.generation_time = 29
T_AF = 300e3 / self.generation_time
T_B = 60.7e3 / self.generation_time
T_EU_AS = 36.0e3 / self.generation_time
T_arch_afr_split = 499e3 / self.generation_time
T_arch_afr_mig = 125e3 / self.generation_time
T_nean_split = 559e3 / self.generation_time
T_arch_adm_end = 18.7e3 / self.generation_time
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two populations
r_CEU = 0.00125
r_CHB = 0.00372
N_CEU = N_CEU0 / math.exp(-r_CEU * T_EU_AS)
N_CHB = N_CHB0 / math.exp(-r_CHB * T_EU_AS)
# Migration rates during the various epochs.
m_AF_B = 52.2e-5
m_YRI_CEU = 2.48e-5
m_YRI_CHB = 0e-5
m_CEU_CHB = 11.3e-5
m_AF_arch_af = 1.98e-5
m_OOA_nean = 0.825e-5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
# We also have two archaic populations, putative Neanderthals and
# archaicAfrican, which are population indices 3=Nean and 4=arch_afr.
# Their sizes are equal to the ancestral reference population size N_0.
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_YRI),
msprime.PopulationConfiguration(initial_size=N_CEU,
growth_rate=r_CEU),
msprime.PopulationConfiguration(initial_size=N_CHB,
growth_rate=r_CHB),
msprime.PopulationConfiguration(initial_size=N_0),
msprime.PopulationConfiguration(initial_size=N_0)
]
self.migration_matrix = [ # noqa
[0, m_YRI_CEU, m_YRI_CHB, 0, 0], # noqa
[m_YRI_CEU, 0, m_CEU_CHB, 0, 0], # noqa
[m_YRI_CHB, m_CEU_CHB, 0, 0, 0], # noqa
[0, 0, 0, 0, 0], # noqa
[0, 0, 0, 0, 0] # noqa
] # noqa
self.demographic_events = [
# first event is migration turned on between modern and archaic humans
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(4, 0)),
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_OOA_nean,
matrix_index=(1, 3)),
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_OOA_nean,
matrix_index=(3, 1)),
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_OOA_nean,
matrix_index=(2, 3)),
msprime.MigrationRateChange(time=T_arch_adm_end,
rate=m_OOA_nean,
matrix_index=(3, 2)),
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(time=T_EU_AS,
source=2,
destination=1,
proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_arch_af,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_arch_af,
matrix_index=(4, 0)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_OOA_nean,
matrix_index=(1, 3)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_OOA_nean,
matrix_index=(3, 1)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(time=T_B,
source=1,
destination=0,
proportion=1.0),
msprime.MigrationRateChange(time=T_B, rate=0),
msprime.MigrationRateChange(time=T_B,
rate=m_AF_arch_af,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_B,
rate=m_AF_arch_af,
matrix_index=(4, 0)),
# Beginning of migration between African and archaic African populations
msprime.MigrationRateChange(time=T_arch_afr_mig, rate=0),
# Size changes to N_0 at T_AF
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_0,
population_id=0),
# Archaic African merges with moderns
msprime.MassMigration(time=T_arch_afr_split,
source=4,
destination=0,
proportion=1.0),
# Neanderthal merges with moderns
msprime.MassMigration(time=T_nean_split,
source=3,
destination=0,
proportion=1.0)
]
def _orangutan():
id = "TwoSpecies_2L11"
description = "Two population orangutan model"
long_description = """
The two orang-utan species, Sumatran (Pongo abelii) and Bornean (Pongo
pygmaeus) inferred from the joint-site frequency spectrum with ten
individuals from each population. This model is an isolation-with-
migration model, with exponential growth or decay in each population
after the split. The Sumatran population grows in size, while the
Bornean population slightly declines.
"""
citations = [_locke2011.because(stdpopsim.CiteReason.DEM_MODEL)]
populations = [
stdpopsim.Population("Bornean", "Pongo pygmaeus (Bornean) population"),
stdpopsim.Population("Sumatran", "Pongo abelii (Sumatran) population"),
]
# Parameters from paper:
# ancestral size, before split
Na = 17934
# time of split
T_split_years = 403149
# get split time in units of generations
generation_time = _species.generation_time
T_split = T_split_years / generation_time
# proportion of ancestral pop to branch B
s = 0.592
# sizes at split
Na_B = 17934 * s
Na_S = 17934 * (1 - s)
# present sizes
N_B = 8805
N_S = 37661
# get growth rates
r_B = -1 * math.log(Na_B / N_B) / T_split
r_S = -1 * math.log(Na_S / N_S) / T_split
# migration rates
m_S_B = 0.395 / 2 / Na
m_B_S = 0.239 / 2 / Na
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=B and 1=S
# initially.
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=long_description,
citations=citations,
populations=populations,
generation_time=generation_time,
population_configurations=[
msprime.PopulationConfiguration(
initial_size=N_B,
growth_rate=r_B,
metadata=populations[0].asdict()), # NOQA
msprime.PopulationConfiguration(
initial_size=N_S,
growth_rate=r_S,
metadata=populations[1].asdict()), # NOQA
],
migration_matrix=[
[0, m_B_S], # NOQA
[m_S_B, 0], # NOQA
],
demographic_events=[
# Merge populations and turn off migration, change to size Na
msprime.MassMigration(time=T_split,
source=1,
destination=0,
proportion=1.0),
msprime.MigrationRateChange(time=T_split, rate=0),
msprime.PopulationParametersChange(time=T_split,
initial_size=Na,
growth_rate=0,
population_id=0),
],
)
def hominin_composite_archaic_africa():
dm = hominin_composite()
id = "HomininComposite2_4G20"
description = "HomininComposite2_4G20 plus archaic lineage in Africa"
generation_time = 29
# Ragsdale & Gravel 2019
N_0 = 3600
T_arch_afr_split = 499e3 / generation_time
T_arch_afr_mig = 125e3 / generation_time
T_arch_adm_end = 18.7e3 / generation_time
m_AF_arch_af = 1.98e-5
T_YRI_CEU_split = 65.7e3 / generation_time
populations = dm.populations + [
stdpopsim.Population(
"ArchaicAFR", "Putative Archaic Africans", sampling_time=None
),
]
population_configurations = dm.population_configurations + [
msprime.PopulationConfiguration(
initial_size=N_0, metadata=populations[-1].asdict()
),
]
pop = {p.id: i for i, p in enumerate(populations)}
demographic_events = dm.demographic_events + [
# migration turned on between Yoruban and archaic African populations
msprime.MigrationRateChange(
time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(pop["YRI"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_arch_adm_end,
rate=m_AF_arch_af,
matrix_index=(pop["ArchaicAFR"], pop["YRI"]),
),
# YRI merges into Anc: turn off migration between YRI and ArchaicAFR.
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=0,
matrix_index=(pop["YRI"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=0,
matrix_index=(pop["ArchaicAFR"], pop["YRI"]),
),
# migration turned on between African and archaic African populations
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=m_AF_arch_af,
matrix_index=(pop["Anc"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_YRI_CEU_split,
rate=m_AF_arch_af,
matrix_index=(pop["ArchaicAFR"], pop["Anc"]),
),
# Beginning of migration between African and archaic African populations
msprime.MigrationRateChange(
time=T_arch_afr_mig,
rate=0,
matrix_index=(pop["Anc"], pop["ArchaicAFR"]),
),
msprime.MigrationRateChange(
time=T_arch_afr_mig,
rate=0,
matrix_index=(pop["ArchaicAFR"], pop["Anc"]),
),
# Archaic African merges with moderns
msprime.MassMigration(
time=T_arch_afr_split,
source=pop["ArchaicAFR"],
destination=pop["Anc"],
proportion=1.0,
),
]
demographic_events.sort(key=lambda x: x.time)
return stdpopsim.DemographicModel(
id=id,
description=description,
long_description=dm.long_description,
populations=populations,
citations=dm.citations.copy(),
generation_time=generation_time,
population_configurations=population_configurations,
demographic_events=demographic_events,
)
def run_model(self):
# Load recomb map
recomb_map = msprime.RecombinationMap.read_hapmap(self.infile)
# initial population sizes:
N_bronze = 50000
N_Yam = 20000
N_baa = 10000
N_whg = 10000
N_ehg = 10000
N_neo = 50000
N_chg = 10000
N_A = 5000 # Ancestor of WHG and EHG
N_B = 5000 # Ancestor of CHG and Neolithic farmers
# Time of events
T_bronze = 150
T_Yam = 200
T_neo = 250
T_baa = 275
T_near_east = 800
T_europe = 500
T_basal = 1500
# Growth rate and initial population size for present day from bronze age
r_EU = 0.067
N_present = N_bronze / math.exp(-r_EU * T_bronze)
#Populations: 0=present/bronze/neolithic_farmers/Ana/B,1=Yam/CHG,2=WHG/A, 3=EHG, 4=BAA
population_configurations = [
msprime.PopulationConfiguration(initial_size=N_present,
growth_rate=r_EU),
msprime.PopulationConfiguration(initial_size=N_Yam),
msprime.PopulationConfiguration(initial_size=N_whg),
msprime.PopulationConfiguration(initial_size=N_ehg),
msprime.PopulationConfiguration(initial_size=N_baa)
]
bronze_formation = [
msprime.MassMigration(time=T_bronze,
source=0,
dest=1,
proportion=0.5),
msprime.PopulationParametersChange(time=T_bronze,
initial_size=N_neo,
growth_rate=0,
population=0)
]
yam_formation = [
msprime.MassMigration(time=T_Yam, source=1, dest=3,
proportion=0.5),
msprime.PopulationParametersChange(time=T_Yam,
initial_size=N_chg,
population=1),
msprime.MigrationRateChange(time=T_Yam,
rate=self.hg_mig_rate,
matrix_index=(2, 3)),
msprime.MigrationRateChange(time=T_Yam,
rate=self.hg_mig_rate,
matrix_index=(3, 2))
]
european_neolithic = [
msprime.MassMigration(time=T_neo,
source=0,
dest=2,
proportion=1.0 / 4.0)
]
baa_formation = [
msprime.MassMigration(time=T_baa,
source=4,
dest=1,
proportion=1.0 / 4.0)
]
ana_split = [
msprime.MassMigration(time=276, source=4, dest=0, proportion=1)
]
hg_split = [
msprime.MassMigration(time=T_europe,
source=3,
dest=2,
proportion=1),
msprime.MigrationRateChange(time=T_europe, rate=0),
msprime.PopulationParametersChange(time=T_europe,
initial_size=N_A,
population=2)
]
near_east_split = [
msprime.MassMigration(time=T_near_east,
source=1,
dest=0,
proportion=1),
msprime.PopulationParametersChange(time=T_near_east,
initial_size=N_B,
population=0)
]
basal_split = [
msprime.MassMigration(time=T_basal, source=2, dest=0, proportion=1)
]
demographic_events = bronze_formation + yam_formation + european_neolithic + baa_formation + ana_split + hg_split + near_east_split + basal_split
# Define samples
samples = []
for i, p in enumerate(self.populations):
sample = [msprime.Sample(time=self.sample_times[i], population=p)]
samples = samples + sample * self.nhaps[i]
# Debugging the demography
migration_matrix = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
dd = msprime.DemographyDebugger(
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events)
dd.print_history()
# Simulate chromosome 3 only
tree_sequence = msprime.simulate(
recombination_map=recomb_map,
mutation_rate=self.mutation_rate,
population_configurations=population_configurations,
demographic_events=demographic_events,
samples=samples)
return tree_sequence
def _get_demography(self):
"""
Returns demography scenario based on an input tree and admixture
edge list with events in the format (source, dest, start, end, rate).
Time on the tree is defined in units of generations.
"""
# Define demographic events for msprime
demog = set()
# tag min index child for each node, since at the time the node is
# called it may already be renamed by its child index b/c of
# divergence events.
for node in self.tree.treenode.traverse():
if node.children:
node._schild = min([i.idx for i in node.get_descendants()])
else:
node._schild = node.idx
# traverse tree from root to tips
for node in self.tree.treenode.traverse():
# if children add div events
if node.children:
dest = min([i._schild for i in node.children])
source = max([i._schild for i in node.children])
time = int(node.height)
demog.add(ms.MassMigration(time, source, dest))
# for all nodes set Ne changes
demog.add(
ms.PopulationParametersChange(time,
initial_size=node.Ne,
population=dest), )
# tips set populations sizes (popconfig seemings does this too,
# but it didn't actually work for tips until I added this...
else:
time = int(node.height)
demog.add(
ms.PopulationParametersChange(
time,
initial_size=node.Ne,
population=node.idx,
))
# debugging helper
if self._debug:
print(
'div time: {:>9}, {:>2} {:>2}, {:>2} {:>2}, Ne={}'.format(
int(time),
source,
dest,
node.children[0].idx,
node.children[1].idx,
node.Ne,
),
file=sys.stderr,
)
# Add migration pulses
if not self.admixture_type:
for evt in range(self.aedges):
# rate is prop. of population, time is prop. of edge
rate = self.ms_migrate[evt]
time = self.ms_migtime[evt]
source, dest = self.admixture_edges[evt][:2]
# rename nodes at time of admix in case diverge renamed them
snode = self.tree.treenode.search_nodes(idx=source)[0]
dnode = self.tree.treenode.search_nodes(idx=dest)[0]
children = (snode._schild, dnode._schild)
demog.add(
ms.MassMigration(time, children[0], children[1], rate))
if self._debug:
print(
'mig pulse: {:>9}, {:>2} {:>2}, {:>2} {:>2}, rate={:.2f}'
.format(
time,
"",
"", # node.children[0].idx, node.children[1].idx,
snode.name,
dnode.name,
rate),
file=sys.stderr,
)
# Add migration intervals
else:
for evt in range(self.aedges):
rate = self.ms_migration[evt]['mrates']
time = (self.ms_migration[evt]['mtimes']).astype(int)
source, dest = self.admixture_edges[evt][:2]
# rename nodes at time of admix in case diverg renamed them
snode = self.tree.treenode.search_nodes(idx=source)[0]
dnode = self.tree.treenode.search_nodes(idx=dest)[0]
children = (snode._schild, dnode._schild)
demog.add(ms.MigrationRateChange(time[0], rate, children))
demog.add(ms.MigrationRateChange(time[1], 0, children))
if self._debug:
print("mig interv: {}, {}, {}, {}, {:.3f}".format(
time[0], time[1], children[0], children[1], rate),
file=sys.stderr)
# sort events by type (so that mass migrations come before pop size
# changes) and time
demog = sorted(list(demog), key=lambda x: x.type)
demog = sorted(demog, key=lambda x: x.time)
self.ms_demography = demog
def __init__(self):
# Parameters are taken from the Methods - Simulated data section
# Population sizes
N_AF0 = 7310 # Initial african population size
N_AF1 = 14474 # Second african pop. size
N_OOA = 1861 # OOA population size
N_CEU0 = 1032 # European population size at CEU/CHB split
N_CHB0 = 554 # Asian population size at CEU/CHB split
N_ADMIX0 = 30000 # Initial size of admixed population
# Epoch times
T_AF0_AF1 = 5920 # initial increase in african pop. size
T_AF1_OOA = 2040 # Time of OOA event
T_CEU_CHB = 920 # Time of european/asian split
T_ADMIX0 = 12
# Migration rates
m_AF1_OOA = 1.5e-4 # Bidirectional migration rate between african and OOA pops.
m_AF1_CEU0 = 2.5e-5 # Migration rates between AF1 and CEU0
m_AF1_CHB0 = 7.8e-6 # Migration rates between AF1 and CHB0
m_CEU0_CHB0 = 3.11e-5 # Migration rates between CEU0 and CHB0
# Mass migration to create admixed populations
mm_AF1 = 1 / 6
# Adjusted fraction for remaining population after AF migration (5/6 * 2/5 = 1/3)
mm_CEU0 = 2 / 5
# Adjusted fraction for remaining population (1/2 * 1 = 1/2)
mm_CHB0 = 1.0
# Growth rates
r_CEU0 = 3.8e-3
r_CHB0 = 4.8e-3
r_ADMIX0 = 0.05
# Calculate population sizes at modern (T=0) time
N_CEU1 = N_CEU0 * math.exp(r_CEU0 * T_CEU_CHB)
N_CHB1 = N_CHB0 * math.exp(r_CHB0 * T_CEU_CHB)
N_ADMIX1 = N_ADMIX0 * math.exp(r_ADMIX0 * T_ADMIX0)
# Set population sizes at T=0
# pop0 is Africa, pop1 is Europe, pop2 is Asia, pop3 is admixed
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_AF1, growth_rate=0),
msprime.PopulationConfiguration(initial_size=N_CEU1,
growth_rate=r_CEU0),
msprime.PopulationConfiguration(initial_size=N_CHB1,
growth_rate=r_CHB0),
msprime.PopulationConfiguration(initial_size=N_ADMIX1,
growth_rate=r_ADMIX0)
]
# Migration matrix, all migrations to admixed population are 0
self.migration_matrix = [[0, m_AF1_CEU0, m_AF1_CHB0, 0],
[m_AF1_CEU0, 0, m_CEU0_CHB0, 0],
[m_AF1_CHB0, m_CEU0_CHB0, 0, 0], [0, 0, 0, 0]]
# Now we add the demographic events working backwards in time.
self.demographic_events = [
# Admixed population recoalesces with origin populations (T_ADMIX0)
msprime.MassMigration(time=T_ADMIX0,
source=3,
destination=0,
proportion=mm_AF1),
msprime.MassMigration(time=T_ADMIX0 + 0.0001,
source=3,
destination=1,
proportion=mm_CEU0),
msprime.MassMigration(time=T_ADMIX0 + 0.0002,
source=3,
destination=2,
proportion=mm_CHB0),
# Zero out migration rate (desn't matter but added for equality to prod.)
msprime.MigrationRateChange(time=T_CEU_CHB, rate=0.0),
# CEU and CHB coalesce and set population to OOA size (T_CEU_CHB)
msprime.MassMigration(time=T_CEU_CHB + 0.0001,
source=2,
destination=1,
proportion=1.0),
msprime.PopulationParametersChange(time=T_CEU_CHB + 0.0002,
initial_size=N_OOA,
growth_rate=0.0,
population_id=1),
# Set OOA <--> AF migration rate (T_CEU_CHB)
msprime.MigrationRateChange(time=T_CEU_CHB + 0.0003,
rate=m_AF1_OOA,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_CEU_CHB + 0.0003,
rate=m_AF1_OOA,
matrix_index=(1, 0)),
# Zero out migration rate (desn't matter but added for equality to prod.)
msprime.MigrationRateChange(time=T_AF1_OOA, rate=0.0),
# OOA and AF1 coalesce (T_OOA)
msprime.MassMigration(time=T_AF1_OOA + 0.0001,
source=1,
destination=0,
proportion=1.0),
# AF1 -> AF0 population size change (T_AF0_AF1)
msprime.PopulationParametersChange(time=T_AF0_AF1,
initial_size=N_AF0,
population_id=0),
]
def __init__(self):
# All parameters were taken from table 1 of Ragsdale et al. (2019)
generation_time = 29
# Population sizes
N_0 = 3600 # Size of archaic populations
N_YRI = 13900 # Fixed size of YRI population
N_B = 880 # Size of OOA population
N_CEU0 = 2300 # Size of CEU population at CEU-CHB split
N_CHB0 = 650 # Size of CHB population at CEU-CHB split
# Population growth parameters
r_CEU = 0.125e-2
r_CHB = 0.372e-2
# Migration parameters
m_AF_B = 52.2e-5
m_YRI_CEU = 2.48e-5
m_YRI_CHB = 0
m_CEU_CHB = 11.3e-5
m_AF_ARCHAF = 1.98e-5
m_OOA_NEAN = 0.825e-5
# Epoch times
T_AF = 300e3 / generation_time
T_OOA = 60.7e3 / generation_time
T_CEU_CHB = 36e3 / generation_time
T_ARCHAF_split = 499e3 / generation_time
T_ARCHAF_mig = 125e3 / generation_time
T_NEAN_split = 559e3 / generation_time
T_ARCH_ADMIX_end = 18.7e3 / generation_time
# Calculate population sizes at modern (T=0) time
N_CEU1 = N_CEU0 * math.exp(r_CEU * T_CEU_CHB)
N_CHB1 = N_CHB0 * math.exp(r_CHB * T_CEU_CHB)
# Set population sizes at T=0
# pop0 is Africa, pop1 is Europe, pop2 is Asia, pop3 is Neanderthal, pop4 is
# archaic african
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_YRI, growth_rate=0),
msprime.PopulationConfiguration(initial_size=N_CEU1,
growth_rate=r_CEU),
msprime.PopulationConfiguration(initial_size=N_CHB1,
growth_rate=r_CHB),
msprime.PopulationConfiguration(initial_size=N_0, growth_rate=0),
msprime.PopulationConfiguration(initial_size=N_0, growth_rate=0)
]
# Setup initial migration matrix
self.migration_matrix = [
[0, m_YRI_CEU, m_YRI_CHB, 0, 0], # noqa
[m_YRI_CEU, 0, m_CEU_CHB, 0, 0], # noqa
[m_YRI_CHB, m_CEU_CHB, 0, 0, 0], # noqa
[0, 0, 0, 0, 0], # noqa
[0, 0, 0, 0, 0] # noqa
]
self.demographic_events = [
# Migration between YRI and ARCHAF(E1)
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_AF_ARCHAF,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_AF_ARCHAF,
matrix_index=(4, 0)),
# Migration between CEU and NEAN(E1)
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_OOA_NEAN,
matrix_index=(1, 3)),
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_OOA_NEAN,
matrix_index=(3, 1)),
# Migration between CHB and NEAN(E1)
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_OOA_NEAN,
matrix_index=(2, 3)),
msprime.MigrationRateChange(time=T_ARCH_ADMIX_end,
rate=m_OOA_NEAN,
matrix_index=(3, 2)),
# Coalescence of CHB into CEU (E2)
msprime.MassMigration(time=T_CEU_CHB,
source=2,
dest=1,
proportion=1.0),
# Reset migration rates (E2)(redundant)*
msprime.MigrationRateChange(time=T_CEU_CHB, rate=0.0),
# Migration rate change between OOA(CEU) and AF(YRI)(E2)
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_AF_B,
matrix_index=(1, 0)),
# Migration between YRI and ARCHAF (E2)(redundant without mig. rate reset)*
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_AF_ARCHAF,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_AF_ARCHAF,
matrix_index=(4, 0)),
# Migration between CEU and NEAN (E2)(redundant without mig. rate reset)*
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_OOA_NEAN,
matrix_index=(1, 3)),
msprime.MigrationRateChange(time=T_CEU_CHB,
rate=m_OOA_NEAN,
matrix_index=(3, 1)),
# CEU change to fixed population size at the time of the CHB/CEU coal. (E2)
msprime.PopulationParametersChange(time=T_CEU_CHB,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Coalescence between the OOA and AF pops (E3)
msprime.MassMigration(time=T_OOA,
source=1,
destination=0,
proportion=1.0),
# Reset migration rates (E3)
msprime.MigrationRateChange(time=T_OOA, rate=0.0),
# Migration between YRI and ARCHAF (E3)
msprime.MigrationRateChange(time=T_OOA,
rate=m_AF_ARCHAF,
matrix_index=(0, 4)),
msprime.MigrationRateChange(time=T_OOA,
rate=m_AF_ARCHAF,
matrix_index=(4, 0)),
# Migration between archaic african and african pop. "ends" (E4)
msprime.MigrationRateChange(time=T_ARCHAF_mig, rate=0),
# AF reverts to ancestral population size pre OOA (E5)
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_0,
population_id=0),
# Archaic AF population coalesces into AF (E6)
msprime.MassMigration(time=T_ARCHAF_split,
source=4,
dest=0,
proportion=1.0),
# NEAN pop. coalesces into AF (E7)
msprime.MassMigration(time=T_NEAN_split,
source=3,
dest=0,
proportion=1.0)
]
def _sim_admix_12(nreps, Ns=500000, gen=20):
# Set the ML values of various parameters
Taus = np.array([0, 1, 2, 3, 4, 5]) * 1e4 * gen
# Migration rates C -> B and from IJ -> EF
m_C_B = 2e-6
m_IJ_EF = 2e-6
# Population IDs correspond to their indexes in pop_config.
ntips = len(tree.tree)
pop_config = [
ms.PopulationConfiguration(sample_size=2, initial_size=Ns)
for i in range(ntips)
]
## migration matrix all zeros time=0
migmat = np.zeros((ntips, ntips)).tolist()
## set up demography
demog = [
## initial migration from C -> B
ms.MigrationRateChange(time=0, rate=m_C_B, matrix_index=(1, 2)),
ms.MigrationRateChange(time=Taus[1], rate=0),
# merge events at time 1 (b,a), (f,e), (j,i)
ms.MassMigration(time=Taus[1], source=1, destination=0,
proportion=1.0),
ms.MassMigration(time=Taus[1], source=5, destination=4,
proportion=1.0),
ms.MassMigration(time=Taus[1], source=9, destination=8,
proportion=1.0),
## migration from IJ -> EF (backward in time)
ms.MigrationRateChange(time=Taus[1], rate=m_IJ_EF,
matrix_index=(4, 8)),
## merge events at time 2 (c,a), (g,e), (k,i)
ms.MassMigration(time=Taus[2], source=2, destination=0,
proportion=1.0),
ms.MassMigration(time=Taus[2], source=6, destination=4,
proportion=1.0),
ms.MassMigration(time=Taus[2],
source=10,
destination=8,
proportion=1.0),
## end migration at ABC and merge
ms.MigrationRateChange(time=Taus[2], rate=0),
ms.MassMigration(time=Taus[3], source=3, destination=0,
proportion=1.0),
ms.MassMigration(time=Taus[3], source=7, destination=4,
proportion=1.0),
ms.MassMigration(time=Taus[3],
source=11,
destination=8,
proportion=1.0),
## merge EFJH -> IJKL
ms.MassMigration(time=Taus[4], source=8, destination=4,
proportion=1.0),
## merge ABCD -> EFJHIJKL
ms.MassMigration(time=Taus[5], source=4, destination=0,
proportion=1.0),
]
## sim the data
replicates = ms.simulate(population_configurations=pop_config,
migration_matrix=migmat,
demographic_events=demog,
num_replicates=nreps,
length=100,
mutation_rate=1e-9)
return replicates
def out_of_africa(prefix, nhaps, recomb):
"""
Specify the demographic model used in these simulations based on Dr.
Jouganous optimization of Gravel et. al's 2011 model. Function taken
and modified from Dr. Alicia Martin
"""
if os.path.isfile(prefix+'.simulation.hdf5'):
simulation = ms.load(prefix+'.simulation.hdf5')
line = 'Simulation %s has been DONE! Loading %s.simulation.hdf5'
print(line%(prefix, prefix))
else:
## First we set out the maximum likelihood values of the various
## parameters given in Gravel et al, 2017 Table 2 but updated to
## Dr. Jouganous work
N_A = 11273
N_B = 3104
N_AF = 23721
N_EU0 = 2271
N_AS0 = 924
## Times are provided in years, so we convert into generations.
generation_time = 29 # according to doi:10.1086/302770
T_AF = 312e3 / generation_time
T_B = 125e3 / generation_time
T_EU_AS = 42.3e3 / generation_time
## We need to work out the starting (diploid) population sizes based on
## the growth rates provided for these two populations
r_EU = 0.00196
r_AS = 0.00309
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
## Migration rates during the various epochs.
m_AF_B = 15.80e-5
m_AF_EU = 1.10e-5
m_AF_AS = 0.48e-5
m_EU_AS = 4.19e-5
## Population IDs correspond to their indexes in the population
## configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
## initially.
population_configurations = [
ms.PopulationConfiguration(
sample_size=nhaps[0], initial_size=N_AF),
ms.PopulationConfiguration(
sample_size=nhaps[1], initial_size=N_EU, growth_rate=r_EU),
ms.PopulationConfiguration(
sample_size=nhaps[2], initial_size=N_AS, growth_rate=r_AS)
]
## define the migration matrix
migration_matrix = [
[ 0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
## define the demographic events (mergers and splits)
demographic_events = [
## CEU and CHB merge into B with rate changes at T_EU_AS
ms.MassMigration(
time=T_EU_AS, source=2, destination=1, proportion=1.0),
ms.MigrationRateChange(time=T_EU_AS, rate=0),
ms.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(0, 1)),
ms.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(1, 0)),
ms.PopulationParametersChange(
time=T_EU_AS, initial_size=N_B, growth_rate=0, population_id=1),
## Population B merges into YRI at T_B
ms.MassMigration(
time=T_B, source=1, destination=0, proportion=1.0),
## Size changes to N_A at T_AF
ms.PopulationParametersChange(
time=T_AF, initial_size=N_A, population_id=0)
]
## Use the demography debugger to print out the demographic history
## that we have just described.
dp = ms.DemographyDebugger(
Ne=N_A,
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events)
dp.print_history()
with open('%s.demography.txt'%(prefix),'w') as fn:
dp.print_history(output=fn)
settings = {
'population_configurations': population_configurations,
'migration_matrix': migration_matrix,
'demographic_events': demographic_events,
'mutation_rate': 1.44e-8, #according to 10.1371/journal.pgen.1004023
'recombination_map': ms.RecombinationMap.read_hapmap(recomb)
}
print('Starting Simulation...\t%s'%(current_time()))
simulation = ms.simulate(**settings)
simulation.dump('%s.simulation.hdf5'%(prefix), True)
print('Simulation %s DONE!\t%s'%(prefix, current_time()))
return simulation
def sim_ongoing_interval(rec_map=None, L=3e9, Ne=10000, Nadmix=500,
Tadmix_start=4, Tadmix_stop=12, frac_ongoing=0.05,
seed=None, path=None, tszip=None):
"""
Simulate an ongoing model of admixture.
With the disrete-time backwards wright-fisher.
A new population (2) is formed by splitting off from population 0.
At time=Tadmix_start migration starts from population 1,
with rate frac_ongoing admixture continues until Tadmix_stop.
rec_map = valid msprime recombination map
L = length of genome, in base pairs (ignored if rec_map is specified)
Ne = diploid population size for all three populations
Tadmix = time of admixture
Nadmix = number of observed admixed individuals
seed = seed to pass to msprime.simulate
path = file path, if given will write the ts to this path (NOT IMPLEMENTED)
"""
assert Tadmix_stop > Tadmix_start, "Tadmix_stop must be greater than Tadmix_start"
Tadmix_start = int(Tadmix_start)
Tadmix_stop = int(Tadmix_stop)
Ne = int(Ne)
Nadmix = int(Nadmix)
# recombination map
if rec_map:
recomb_map = rec_map
else:
L = int(L)
recomb_map = msprime.RecombinationMap.uniform_map(L, 1e-8, L)
pop_configs = [
msprime.PopulationConfiguration(initial_size=Ne, growth_rate=0),
msprime.PopulationConfiguration(initial_size=Ne, growth_rate=0),
msprime.PopulationConfiguration(initial_size=Ne, growth_rate=0)
]
mig_mat = [
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
]
admixture_events = [
# migration during the interval Tadmix_start - Tadmix_stop
msprime.MigrationRateChange(time=Tadmix_start, rate=frac_ongoing, matrix_index=(2, 1)),
msprime.MigrationRateChange(time=Tadmix_stop, rate=0, matrix_index=(2, 1)),
# founding of pop 2
msprime.MassMigration(time=Tadmix_stop + 1, source=2, destination=0, proportion=1.0),
]
samps = [msprime.Sample(population=2, time=0)] * 2 * Nadmix
ts_admix = msprime.simulate(
population_configurations=pop_configs,
migration_matrix=mig_mat,
demographic_events=admixture_events,
recombination_map=recomb_map,
mutation_rate=0,
model='dtwf',
samples=samps,
random_seed=seed,
start_time=0,
end_time=Tadmix_stop + 2
)
return(ts_admix)
def test_migration_rate_change(self):
g = 512
Ne = 8192
event = msprime.MigrationRateChange(time=g, rate=1)
self.check_time(event, g, Ne)
def create_simulation_runner(parser, arg_list):
"""
Parses the arguments and returns a SimulationRunner instance.
"""
args = parser.parse_args(arg_list)
if args.mutation_rate == 0 and not args.trees:
parser.error("Need to specify at least one of --theta or --trees")
num_loci = int(args.recombination[1])
if args.recombination[1] != num_loci:
parser.error("Number of loci must be integer value")
if args.recombination[0] != 0.0 and num_loci < 2:
parser.error("Number of loci must > 1")
r = 0.0
# We don't scale recombination or mutation rates by the size
# of the region.
if num_loci > 1:
r = args.recombination[0] / (num_loci - 1)
mu = args.mutation_rate / num_loci
# Check the structure format.
symmetric_migration_rate = 0.0
num_populations = 1
population_configurations = [
msprime.PopulationConfiguration(args.sample_size)
]
migration_matrix = [[0.0]]
if args.structure is not None:
num_populations = convert_int(args.structure[0], parser)
# We must have at least num_population sample_configurations
if len(args.structure) < num_populations + 1:
parser.error("Must have num_populations sample sizes")
population_configurations = [None for j in range(num_populations)]
for j in range(num_populations):
population_configurations[j] = msprime.PopulationConfiguration(
convert_int(args.structure[j + 1], parser))
total = sum(conf.sample_size for conf in population_configurations)
if total != args.sample_size:
parser.error("Population sample sizes must sum to sample_size")
# We optionally have the overall migration_rate here
if len(args.structure) == num_populations + 2:
symmetric_migration_rate = convert_float(
args.structure[num_populations + 1], parser)
check_migration_rate(parser, symmetric_migration_rate)
elif len(args.structure) > num_populations + 2:
parser.error("Too many arguments to --structure/-I")
if num_populations > 1:
migration_matrix = [[
symmetric_migration_rate / (num_populations - 1) * int(j != k)
for j in range(num_populations)
] for k in range(num_populations)]
else:
if len(args.migration_matrix_entry) > 0:
parser.error("Cannot specify migration matrix entries without "
"first providing a -I option")
if args.migration_matrix is not None:
parser.error("Cannot specify a migration matrix without "
"first providing a -I option")
if args.migration_matrix is not None:
migration_matrix = convert_migration_matrix(parser,
args.migration_matrix,
num_populations)
for matrix_entry in args.migration_matrix_entry:
dest = convert_population_id(parser, matrix_entry[0], num_populations)
source = convert_population_id(parser, matrix_entry[1],
num_populations)
rate = matrix_entry[2]
if dest == source:
parser.error("Cannot set diagonal elements in migration matrix")
check_migration_rate(parser, rate)
migration_matrix[dest][source] = rate
# Set the initial demography
demographic_events = []
if args.growth_rate is not None:
for config in population_configurations:
config.growth_rate = args.growth_rate
for population_id, growth_rate in args.population_growth_rate:
pid = convert_population_id(parser, population_id, num_populations)
population_configurations[pid].growth_rate = growth_rate
for population_id, size in args.population_size:
pid = convert_population_id(parser, population_id, num_populations)
population_configurations[pid].initial_size = size
# First we look at population split events. We do this differently
# to ms, as msprime requires a fixed number of population. Therefore,
# modify the number of populations to take into account populations
# splits. This is a messy hack, and will probably need to be changed.
for index, (t, population_id, proportion) in args.admixture:
check_event_time(parser, t)
pid = convert_population_id(parser, population_id, num_populations)
if proportion < 0 or proportion > 1:
parser.error("Proportion value must be 0 <= p <= 1.")
# In ms, the probability of staying in source is p and the probabilty
# of moving to the new population is 1 - p.
event = (index,
msprime.MassMigration(t, pid, num_populations,
1 - proportion))
demographic_events.append(event)
num_populations += 1
# We add another element to each row in the migration matrix
# along with an other row. All new entries are zero.
for row in migration_matrix:
row.append(0)
migration_matrix.append([0 for j in range(num_populations)])
# Add another PopulationConfiguration object with a sample size
# of zero.
population_configurations.append(msprime.PopulationConfiguration(0))
# Add the demographic events
for index, (t, alpha) in args.growth_rate_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eG")
check_event_time(parser, t)
demographic_events.append(
(index,
msprime.PopulationParametersChange(time=t, growth_rate=alpha)))
for index, (t, population_id, alpha) in args.population_growth_rate_change:
pid = convert_population_id(parser, population_id, num_populations)
check_event_time(parser, t)
demographic_events.append(
(index,
msprime.PopulationParametersChange(time=t,
growth_rate=alpha,
population_id=pid)))
for index, (t, x) in args.size_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eN")
check_event_time(parser, t)
demographic_events.append(
(index,
msprime.PopulationParametersChange(time=t,
initial_size=x,
growth_rate=0)))
for index, (t, population_id, x) in args.population_size_change:
check_event_time(parser, t)
pid = convert_population_id(parser, population_id, num_populations)
demographic_events.append(
(index,
msprime.PopulationParametersChange(time=t,
initial_size=x,
growth_rate=0,
population_id=pid)))
for index, (t, source, dest) in args.population_split:
check_event_time(parser, t)
source_id = convert_population_id(parser, source, num_populations)
dest_id = convert_population_id(parser, dest, num_populations)
demographic_events.append(
(index, msprime.MassMigration(t, source_id, dest_id, 1.0)))
# Set the migration rates for source to 0
for j in range(num_populations):
if j != source_id:
event = msprime.MigrationRateChange(t, 0.0, (j, source_id))
demographic_events.append((index, event))
# Demographic events that affect the migration matrix
if num_populations == 1:
condition = (len(args.migration_rate_change) > 0
or len(args.migration_matrix_entry_change) > 0
or len(args.migration_matrix_change) > 0)
if condition:
parser.error("Cannot change migration rates for 1 population")
for index, (t, x) in args.migration_rate_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eM")
check_migration_rate(parser, x)
check_event_time(parser, t)
event = msprime.MigrationRateChange(t, x / (num_populations - 1))
demographic_events.append((index, event))
for index, event in args.migration_matrix_entry_change:
t = event[0]
check_event_time(parser, t)
dest = convert_population_id(parser, event[1], num_populations)
source = convert_population_id(parser, event[2], num_populations)
if dest == source:
parser.error("Cannot set diagonal elements in migration matrix")
rate = event[3]
check_migration_rate(parser, rate)
msp_event = msprime.MigrationRateChange(t, rate, (dest, source))
demographic_events.append((index, msp_event))
for index, event in args.migration_matrix_change:
if len(event) < 3:
parser.error("Need at least three arguments to -ma")
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-ema")
t = convert_float(event[0], parser)
check_event_time(parser, t)
if convert_int(event[1], parser) != num_populations:
parser.error(
"num_populations must be equal for new migration matrix")
matrix = convert_migration_matrix(parser, event[2:], num_populations)
for j in range(num_populations):
for k in range(num_populations):
if j != k:
msp_event = msprime.MigrationRateChange(
t, matrix[j][k], (j, k))
demographic_events.append((index, msp_event))
# We've created all the events, now we need to rescale the migration rates
# We assume Ne = 1 here.
for _, msp_event in demographic_events:
msp_event.time *= 4
if isinstance(msp_event, msprime.PopulationParametersChange):
msp_event.growth_rate /= 4
if isinstance(msp_event, msprime.MigrationRateChange):
# Divide by 4 to get a per-generation rate, assuming Ne=1
msp_event.rate /= 4
# We also need to rescale the migration matrix and growth rates.
migration_matrix = [[m / 4 for m in row] for row in migration_matrix]
for config in population_configurations:
config.growth_rate /= 4
demographic_events.sort(key=lambda x: (x[0], x[1].time))
time_sorted = sorted(demographic_events, key=lambda x: x[1].time)
if demographic_events != time_sorted:
parser.error("Demographic events must be supplied in non-decreasing "
"time order")
runner = SimulationRunner(
sample_size=args.sample_size,
num_loci=num_loci,
migration_matrix=migration_matrix,
population_configurations=population_configurations,
demographic_events=[event for _, event in demographic_events],
num_replicates=args.num_replicates,
scaled_recombination_rate=r,
scaled_mutation_rate=mu,
precision=args.precision,
print_trees=args.trees,
random_seeds=args.random_seeds)
return runner
def out_of_africa():
# First we set out the maximum likelihood values of the various parameters
# given in Table 1.
N_A = 7300
N_B = 2100
N_AF = 12300
N_EU0 = 1000
N_AS0 = 510
# Times are provided in years, so we convert into generations.
generation_time = 25
T_AF = 220e3 / generation_time
T_B = 140e3 / generation_time
T_EU_AS = 21.2e3 / generation_time
# We need to work out the starting population sizes based on the growth
# rates provided for these two populations
r_EU = 0.004
r_AS = 0.0055
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
# Migration rates during the various epochs.
m_AF_B = 25e-5
m_AF_EU = 3e-5
m_AF_AS = 1.9e-5
m_EU_AS = 9.6e-5
# Population IDs correspond to their indexes in the popupulation
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
population_configurations = [
msprime.PopulationConfiguration(
sample_size=0, initial_size=N_AF),
msprime.PopulationConfiguration(
sample_size=1, initial_size=N_EU, growth_rate=r_EU),
msprime.PopulationConfiguration(
sample_size=1, initial_size=N_AS, growth_rate=r_AS)
]
migration_matrix = [
[ 0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(
time=T_EU_AS, source=2, destination=1, proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(0, 1)),
msprime.MigrationRateChange(
time=T_EU_AS, rate=m_AF_B, matrix_index=(1, 0)),
msprime.PopulationParametersChange(
time=T_EU_AS, initial_size=N_B, growth_rate=0, population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(
time=T_B, source=1, destination=0, proportion=1.0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(
time=T_AF, initial_size=N_A, population_id=0)
]
# Use the demography debugger to print out the demographic history
# that we have just described.
dp = msprime.DemographyDebugger(
Ne=N_A,
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events)
dp.print_history()
def __init__(self):
self.generation_time = 29 # Just information
# sizes of populations
N_YRI = 48433
N_CEU = 6962
N_CHB = 9025
N_Papuan = 8834
N_DenA = 5083
t_DenA = 2058 # generations
N_NeanA = 826
t_NeanA = 2612 # generations
N_Nean1 = 13249
N_Den1 = N_Nean1
N_Den2 = N_Nean1
N_Ghost = 8516
# after coalescences
N_CEU_CHB = 12971
N_Human = 41563
N_DenAnc = 100
N_A = 32671
# bottlenecks
N_CEU_CHB_bot = 2231
N_GhostA_bot = 1394
N_Papuan_bot = 243
# times of coalesces
t_CEU_CHB = 1293
t_CEU_Ghost = 1758
t_Papuan_Ghost = 1784
t_YRI_GhostA = 2218
t_Nean1_NeanA = 3375
t_Den1_DenA = 9750
t_Den1_Den2 = 12500
t_Den_Nean = 15090
t_Human_Den_Nean = 20225
# times of bottlenecks
t_CEU_CHB_bot = 1659
t_Papuan_bot = 1685
t_GhostA_bot = 2119
# migrations
m_YRI_Ghost = 0.000179
m_Ghost_CEU = 0.000442
m_CEU_CHB = 3.14e-5
m_CHB_Papuan = 5.72e-5
m_CEUCHB_Papua = 0.000572
m_Ghost_CEUCHB = 0.000442
# times and proportions of admixtures
p1 = 0.55
t_Nean1_to_CHB = 883
p_Nean1_to_CHB = 0.002
t_Den2_to_Papuan = 45.7e3 / self.generation_time
p_Den2_to_Papuan = (1 - p1) * 0.04
t_Den1_to_Papuan = 29.8e3 / self.generation_time
p_Den1_to_Papuan = p1 * 0.04
t_Nean1_to_Papuan = 1412
p_Nean1_to_Papuan = 0.002
t_Nean1_to_CEU_CHB = 1566
p_Nean1_to_CEU_CHB = 0.011
t_Nean1_to_GhostA = 1853
p_Nean1_to_GhostA = 0.024
# set up populations
self.population_configurations = [
msprime.PopulationConfiguration( # 0 YRI
initial_size=N_YRI,
growth_rate=0,
metadata={
"name": "YRI",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 1 CEU
initial_size=N_CEU,
growth_rate=0,
metadata={
"name": "CEU",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 2 CHB
initial_size=N_CHB,
growth_rate=0,
metadata={
"name": "CHB",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 3 Papuan
initial_size=N_Papuan,
growth_rate=0,
metadata={
"name": "Papuan",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 4 DenA
initial_size=N_DenA,
growth_rate=0,
metadata={
"name": "DenA",
"sampling_time": t_DenA
}),
msprime.PopulationConfiguration( # 5 NeanA
initial_size=N_NeanA,
growth_rate=0,
metadata={
"name": "NeanA",
"sampling_time": t_NeanA
}),
msprime.PopulationConfiguration( # 6 Den1
initial_size=N_Den1,
growth_rate=0,
metadata={
"name": "Den1",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 7 Den2
initial_size=N_Den2,
growth_rate=0,
metadata={
"name": "Den2",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 8 Nean1
initial_size=N_Nean1,
growth_rate=0,
metadata={
"name": "Nean1",
"sampling_time": 0
}),
msprime.PopulationConfiguration( # 9 Ghost
initial_size=N_Ghost,
growth_rate=0,
metadata={
"name": "Ghost",
"sampling_time": 0
})
]
self.migration_matrix = [[0] * 10 for _ in range(10)]
self.migration_matrix[0][9] = m_YRI_Ghost
self.migration_matrix[9][0] = m_YRI_Ghost
self.migration_matrix[1][9] = m_Ghost_CEU
self.migration_matrix[9][1] = m_Ghost_CEU
self.migration_matrix[1][2] = m_CEU_CHB
self.migration_matrix[2][1] = m_CEU_CHB
self.migration_matrix[2][3] = m_CHB_Papuan
self.migration_matrix[3][2] = m_CHB_Papuan
self.demographic_events = [
# Coalescence of CEU and CHB into CHB
msprime.MassMigration(time=t_CEU_CHB,
source=1,
destination=2,
proportion=1.),
# Set size of CEU+CHB population
msprime.PopulationParametersChange(time=t_CEU_CHB,
initial_size=N_CEU_CHB,
population_id=2),
# Change migration matrix
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(2, 1)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(1, 2)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(3, 2)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(2, 3)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(9, 1)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=0,
matrix_index=(1, 9)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=m_CEUCHB_Papua,
matrix_index=(2, 3)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=m_CEUCHB_Papua,
matrix_index=(3, 2)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=m_Ghost_CEUCHB,
matrix_index=(2, 9)),
msprime.MigrationRateChange(time=t_CEU_CHB,
rate=m_Ghost_CEUCHB,
matrix_index=(9, 2)),
# Set bottleneck size of CEU+CHB population
msprime.PopulationParametersChange(time=t_CEU_CHB_bot,
initial_size=N_CEU_CHB_bot,
population_id=2),
# Change migration matrix
msprime.MigrationRateChange(time=t_CEU_CHB_bot, rate=0),
# Set bottleneck size of Papuan population
msprime.PopulationParametersChange(time=t_Papuan_bot,
initial_size=N_Papuan_bot,
population_id=3),
# Coalescence of CEU+CHB and Ghost to Ghost
msprime.MassMigration(time=t_CEU_Ghost,
source=2,
destination=9,
proportion=1.),
# Coalescence of Papuan and Ghost to GhostA
msprime.MassMigration(time=t_Papuan_Ghost,
source=3,
destination=9,
proportion=1.),
# Set bottleneck size of GhostA population
msprime.PopulationParametersChange(time=t_GhostA_bot,
initial_size=N_GhostA_bot,
population_id=9),
# Coalescence of Ghost and YRI into Human (YRI)
msprime.MassMigration(time=t_YRI_GhostA,
source=9,
destination=0,
proportion=1.),
# Set size of Human population
msprime.PopulationParametersChange(time=t_YRI_GhostA,
initial_size=N_Human,
population_id=0),
# Coalescence of NeanA and Nean1 into NeanAnc
msprime.MassMigration(time=t_Nean1_NeanA,
source=8,
destination=5,
proportion=1.),
# Set size of NeanAnc population
msprime.PopulationParametersChange(time=t_Nean1_NeanA,
initial_size=N_Nean1,
population_id=5),
# Coalescence of Den1 and DenA into DenAnc (DenA)
msprime.MassMigration(time=t_Den1_DenA,
source=6,
destination=4,
proportion=1.),
# Set size of DenA population
msprime.PopulationParametersChange(time=t_Den1_DenA,
initial_size=N_DenAnc,
population_id=4),
# Coalescence of DenAnc and Den2 into DenAnc
msprime.MassMigration(time=t_Den1_Den2,
source=7,
destination=4,
proportion=1.),
# Set size of DenA population
msprime.PopulationParametersChange(time=t_Den1_Den2,
initial_size=N_DenAnc,
population_id=4),
# Coalescence of DenAnc and NeanAnc into Den_Nean (Nean1)
msprime.MassMigration(time=t_Den_Nean,
source=5,
destination=4,
proportion=1.),
# Set size of Den_Nean population
msprime.PopulationParametersChange(time=t_Den_Nean,
initial_size=N_Nean1,
population_id=4),
# Coalescence of Den_Nean and Human into Anc (YRI)
msprime.MassMigration(time=t_Human_Den_Nean,
source=4,
destination=0,
proportion=1.),
# Set ancestral size of population
msprime.PopulationParametersChange(time=t_Human_Den_Nean,
initial_size=N_A,
population_id=0),
# Admixture events
# Admixture from Den1 to Papuans
msprime.MassMigration(time=t_Den1_to_Papuan,
source=3,
destination=6,
proportion=p_Den1_to_Papuan),
# Admixture from Den2 to Papuans
msprime.MassMigration(time=t_Den2_to_Papuan,
source=3,
destination=7,
proportion=p_Den2_to_Papuan),
# Admixture from Nean1 to GhostA
msprime.MassMigration(time=t_Nean1_to_GhostA,
source=9,
destination=8,
proportion=p_Nean1_to_GhostA),
# Admixture from Nean1 to CEU+CHB
msprime.MassMigration(time=t_Nean1_to_CEU_CHB,
source=2,
destination=8,
proportion=p_Nean1_to_CEU_CHB),
# Admixture from Nean1 to Papuans
msprime.MassMigration(time=t_Nean1_to_Papuan,
source=3,
destination=8,
proportion=p_Nean1_to_Papuan),
# Admixture from Neandertal to East Asia population
msprime.MassMigration(time=t_Nean1_to_CHB,
proportion=p_Nean1_to_CHB,
source=2,
destination=8),
]
self.demographic_events.sort(key=lambda x: x.time)
def out_of_africa(sample_n_AF, sample_n_EU, sample_n_AS):
# First we set out the maximum likelihood values of the various parameters
# given in Table 1.
N_A = 7310
N_B = 1861
N_AF = 14474 # 12300
N_EU0 = 1032 # 1000
N_AS0 = 554 # 510
# Times are provided in years, so we convert into generations.
generation_time = 25
T_AF = 148e3 / generation_time # 220e3
T_B = 51e3 / generation_time # 140e3
T_EU_AS = 23e3 / generation_time # 21.2e3
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two populations
r_EU = 0.0038 # 0.004
r_AS = 0.0048 # 0.0055
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
# Migration rates during the various epochs.
m_AF_B = 15e-5 # 25e-5
m_AF_EU = 2.5e-5 # 3e-5
m_AF_AS = 0.78e-5 # 1.9e-5
m_EU_AS = 3.11e-5 # 9.6e-5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
# Set sample_size of YRI, CEU and CHB you want to output in the current generation
population_configurations = [
msprime.PopulationConfiguration(sample_size=sample_n_AF,
initial_size=N_AF),
msprime.PopulationConfiguration(sample_size=sample_n_EU,
initial_size=N_EU,
growth_rate=r_EU),
msprime.PopulationConfiguration(sample_size=sample_n_AS,
initial_size=N_AS,
growth_rate=r_AS)
]
migration_matrix = [
[0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(time=T_EU_AS,
source=2,
destination=1,
proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(time=T_B,
source=1,
destination=0,
proportion=1.0),
msprime.MigrationRateChange(
time=T_B,
rate=0), ## Missing in the old tutorial. update 03 06 2020
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
# Use the demography debugger to print out the demographic history
# that we have just described.
dd = msprime.DemographyDebugger(
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events)
dd.print_history()
# set mutation_rate to you need to genotypes
return msprime.simulate(
population_configurations=population_configurations,
migration_matrix=migration_matrix,
demographic_events=demographic_events,
length=2.5e8,
recombination_rate=1e-8,
mutation_rate=1.25e-8)
def __init__(self):
# Since the Tennessen two population model largely uses parameters from
# the Gravel et al 2001, we begin by taking the maximum likelihood
# value from the table 2 of Gravel et al. 2011 using the Low-coverage +
# exons data. We ignore all values related to the asian (AS) population
# as it is not present in the Tennessen two population model. Initially
# we copy over the pre- exponential growth population size estimates,
# migration rates, and epoch times:
generation_time = 25
N_A = 7310 # Ancient population size
N_AF0 = 14474 # Pre-modern african population size (pre and post OOA)
N_B = 1861 # OOA population size, pre-expansion
N_EU0 = 1032 # European population size, pre-expansion
m_AF0_B = 15e-5 # migration from pre-expansion africa to pre-expansion OOA
m_AF1_EU1 = 2.5e-5 # migration from pre-expansion africa to 2nd-expansion euro
T_AF = 148000 / generation_time # Epoch transition from ancient to AF0
T_B = 51000 / generation_time # OOA time
# The european asian split time, begins 1st growth period
T_EU_AS = 23000 / generation_time
# Next we include the additional parameters from Tennessen et al 2012
# which include all exponential growth rates and the time of the second
# round of growth in the European population/first round in the African
# population. These parameters are copied from the section titled
# "Abundance of rare variation explained by human demographic history"
# in Tennessen et al.
r_EU0 = 0.307e-2 # The growth rate for the 1st european expansion
r_EU1 = 1.95e-2 # The growth rate for the 2nd european expansion
r_AF0 = 1.66e-2 # The growth rate for the 1st african expansion
T_AG = 5115 / generation_time # start of 2nd european growth epoch
# For the post exponenential growth popuation sizes we can calcuate the
# population sizes at the start of the epoch using the formula
# f(t) = x_0 * exp(r * (t_0-t)) European population size after 1st expansion
N_EU1 = N_EU0 * math.exp(r_EU0 * (T_EU_AS - T_AG))
# European population size after 2nd expansion
N_EU2 = N_EU1 * math.exp(r_EU1 * T_AG)
# African population size after 1st expansion
N_AF1 = N_AF0 * math.exp(r_AF0 * T_AG)
# Now we set up the population configurations. The population IDs are
# 0=CEU and 1=YRI. This includes both the inital sizes, growth rates,
# and migration rates.
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_AF1,
growth_rate=r_AF0),
msprime.PopulationConfiguration(initial_size=N_EU2,
growth_rate=r_EU1)
]
self.migration_matrix = [
[0, m_AF1_EU1],
[m_AF1_EU1, 0],
]
# Now we add the demographic events working backwards in time. Starting
# with the growth slowdown in Europeans and the transition to a fixed
# population size in Africans.
self.demographic_events = [
# Set the migration rate for 1st CEU growth period (for now stays same)
msprime.MigrationRateChange(time=T_AG,
rate=m_AF1_EU1,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_AG,
rate=m_AF1_EU1,
matrix_index=(1, 0)),
# Growth slowdown in Europeans
msprime.PopulationParametersChange(time=T_AG,
initial_size=N_EU1,
growth_rate=r_EU0,
population_id=1),
# Reversion to fixed population size in Africans
msprime.PopulationParametersChange(time=T_AG,
initial_size=N_AF0,
growth_rate=0,
population_id=0),
# Set the migration rate for pre CEU/CHB split
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF0_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF0_B,
matrix_index=(1, 0)),
# Reversion to fixed population size at the time of the CHB/CEU split
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Coalescence between the OOA and YRI pops
msprime.MassMigration(time=T_B,
source=1,
destination=0,
proportion=1.0),
# Change to ancestral population size pre OOA
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
opts['m'] = opts['m_rel'] / (4 * opts['Ne'])
opts['M'] = opts['M_rel'] / (4 * opts['Ne'])
opts['T'] = opts['T_rel'] * (4 * opts['Ne'])
npops = 3
pops = [
msprime.PopulationConfiguration(sample_size=opts['nsamples'],
initial_size=opts['Ne'],
growth_rate=0.0) for _ in range(npops)
]
migr_init = [[0, opts['M'], opts['M']], [opts['m'], 0, opts['M']],
[opts['M'], opts['M'], 0]]
migr_change = [ msprime.MigrationRateChange(opts['T'], opts['m'], matrix_index=x) for x in [(1,2)] ] \
+ [ msprime.MigrationRateChange(opts['T'], opts['M'], matrix_index=x) for x in [(1,0)] ]
ts = msprime.simulate(Ne=opts['Ne'],
length=opts['chrom_len'],
recombination_rate=opts['recomb_rate'],
population_configurations=pops,
migration_matrix=migr_init,
demographic_events=migr_change)
logfile.write(" done simulating! Generating mutations.\n")
logfile.write(time.strftime(' %X %x %Z\n'))
logfile.flush()
rng = msprime.RandomGenerator(mut_seed)
nodes = msprime.NodeTable()
def __init__(self):
generation_time = 25
# Population sizes
N_A = 7300
N_AF = 12300
N_B = 2100
N_EU0 = 1000
N_AS0 = 510
# Growth rates per generation
r_EU = 0.4e-2
r_AS = 0.55e-2
# Migration rates
m_AF_B = 25e-5
m_AF_EU = 3e-5
m_AF_AS = 1.9e-5
m_EU_AS = 9.6e-5
# Epoch times
T_AF = 220e3 / generation_time
T_B = 140e3 / generation_time
T_EU_AS = 21.2e3 / generation_time
# Calculate population sizes at modern (T=0) time
N_EUF = N_EU0 * math.exp(r_EU * T_EU_AS)
N_ASF = N_AS0 * math.exp(r_AS * T_EU_AS)
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_AF, growth_rate=0),
msprime.PopulationConfiguration(initial_size=N_EUF,
growth_rate=r_EU),
msprime.PopulationConfiguration(initial_size=N_ASF,
growth_rate=r_AS)
]
# Setup initial migration matrix
self.migration_matrix = [[0, m_AF_EU, m_AF_AS], [m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0]]
self.demographic_events = [
# CEU and CHB merge into B, reset migration rates to Af-B, change pop size
msprime.MassMigration(time=T_EU_AS, source=2, dest=1,
proportion=1),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
population_id=1,
growth_rate=0),
# B and AF merge, turn migration off, reset population size
msprime.MassMigration(time=T_B, source=1, dest=0, proportion=1),
msprime.MigrationRateChange(time=T_B, rate=0),
# Ancestral size change, reset population size
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
def out_of_africa(N_haps, no_migration):
N_A = 7300
N_B = 2100
N_AF = 12300
N_EU0 = 1000
N_AS0 = 510
# Times are provided in years, so we convert into generations.
generation_time = 25
T_AF = 220e3 / generation_time
T_B = 140e3 / generation_time
T_EU_AS = 21.2e3 / generation_time
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two populations
r_EU = 0.004
r_AS = 0.0055
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
# Migration rates during the various epochs.
if no_migration:
m_AF_B = 0
m_AF_EU = 0
m_AF_AS = 0
m_EU_AS = 0
else:
m_AF_B = 25e-5
m_AF_EU = 3e-5
m_AF_AS = 1.9e-5
m_EU_AS = 9.6e-5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
n_pops = 3
population_configurations = [
msprime.PopulationConfiguration(sample_size=2 * N_haps[0],
initial_size=N_AF),
msprime.PopulationConfiguration(sample_size=2 * N_haps[1],
initial_size=N_EU,
growth_rate=r_EU),
msprime.PopulationConfiguration(sample_size=2 * N_haps[2],
initial_size=N_AS,
growth_rate=r_AS)
]
migration_matrix = [
[0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(time=T_EU_AS,
source=2,
destination=1,
proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(time=T_B,
source=1,
destination=0,
proportion=1.0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
# Return the output required for a simulation study.
return population_configurations, migration_matrix, demographic_events, N_A, n_pops
msprime.PopulationConfiguration(sample_size=1,
initial_size=N_AS,
growth_rate=r_AS)
]
migration_matrix = [
[0, m_AF_EU, m_AF_AS],
[m_AF_EU, 0, m_EU_AS],
[m_AF_AS, m_EU_AS, 0],
]
demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(time=T_EU_AS,
source=2,
destination=1,
proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS, rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS, rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(time=T_B, source=1, destination=0, proportion=1.0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
def f(time=1, rate=1, matrix_index=None):
return msprime.MigrationRateChange(
time=time, rate=rate, matrix_index=matrix_index)
def main(nhaps=None, nvars=None, rec_map=None, maf=None, to_bed=False,
threads=1, labels=['AFR', 'EUR', 'ASN', 'MX', 'AD'], split_out=False,
plot_pca=True, focus_pops=['AFR', 'EUR']):
if nhaps is None:
nhaps = [45000] * 5
if nvars is None:
nvars = int(1e6)
# First we set out the maximum likelihood values of the various parameters
# given in Table 1.
N_A = 11273
N_B = 3104
N_AF = 23721
N_EU0 = 2271
N_AS0 = 924
N_MX0 = 800 # From Table 2 Gutenkust 2009
# Times are provided in years, so we convert into generations.
# from Jouganous et al. 2017:
generation_time = 29
T_AF = 312e3 / generation_time
T_B = 125e3 / generation_time
T_EU_AS = 42.3e3 / generation_time
T_MX = 12.2e3 / generation_time # from table 1 Gravel 2013
T_AD = 18
# confidence interval
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two population
r_EU = 0.0019
r_AS = 0.00309
r_MX = 0.0050 # From Table 2 Gutenkust
r_AD = 0.05
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
N_MX = N_MX0 / math.exp(-r_MX * T_MX)
N_AD0 = N_MX0 # / math.exp(-r_MX * T_AD)) / 3
N_AD = (N_AD0 / math.exp(-r_AD * T_AD))# * 0.45
# Migration rates during the various epochs.
m_AF_B = 15.8e-5
m_AF_EU = 1.1e-5
m_AF_AS = 0.48e-5
m_EU_AS = 4.19e-5
m_MX_AD = 5e-5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU, 2=CHB, and 3=MX
# initially.
population_configurations = [
msprime.PopulationConfiguration(sample_size=nhaps[0], initial_size=N_AF
),
msprime.PopulationConfiguration(sample_size=nhaps[1], initial_size=N_EU,
growth_rate=r_EU),
msprime.PopulationConfiguration(sample_size=nhaps[2], initial_size=N_AS,
growth_rate=r_AS),
msprime.PopulationConfiguration(sample_size=nhaps[3], initial_size=N_MX,
growth_rate=r_MX),
msprime.PopulationConfiguration(sample_size=nhaps[4], initial_size=N_AD,
growth_rate=r_AD)
]
# Migrations AFR EUR ASN MX AD
migration_matrix = [[ 0, m_AF_EU, m_AF_AS, 0, 0], # AFR
[m_AF_EU, 0, m_EU_AS, 0, 0], # EUR
[m_AF_AS, m_EU_AS, 0, 0, 0], # ASN
[ 0, 0, 0, 0, m_MX_AD], # MX
[ 0, 0, 0, m_MX_AD, 0]] # AD
demographic_events = [
# Slaves arrival
msprime.MassMigration(time=13.758620689655173, source=4, destination=0,
proportion=0.098),
# Colonials arrival
msprime.MassMigration(time=T_AD-2, source=4, destination=1,
proportion=0.443),
# Admixed fraction grow from N_AD0 at rate r_AD at time T_MX
msprime.PopulationParametersChange(
time=T_AD, initial_size=N_AD0, growth_rate=r_AD, population_id=4),
# As the admixed merge to MX, turn off their migration rates
msprime.MigrationRateChange(time=T_AD, rate=0, matrix_index=(3, 4)),
msprime.MigrationRateChange(time=T_AD, rate=0, matrix_index=(4, 3)),
# Admixed fraction merges with MX trunk
msprime.MassMigration(time=T_AD, source=4, destination=3,
proportion=1.0),#0.459),
# switch to standard coalescent
#msprime.SimulationModelChange(T_AD, msprime.StandardCoalescent(1)),
# Natives grow from N_MX0 at rate r_MX at time T_MX
msprime.PopulationParametersChange(
time=T_MX, initial_size=N_MX0, growth_rate=r_MX, population_id=3),
# Natives merge into asian trunk
msprime.MassMigration(time=T_MX, source=3, destination=2, proportion=1.0
),
# As the natives merge to the asians, turn off their growth rates
msprime.PopulationParametersChange(
time=T_MX, initial_size=N_MX0, growth_rate=0, population_id=3),
# Asians grow from N_AS0 at rate r_AS at time T_EU_AS
msprime.PopulationParametersChange(
time=T_EU_AS, initial_size=N_AS0, growth_rate=r_AS, population_id=2
),
# Merge asian trunk wih european
msprime.MassMigration(time=T_EU_AS, source=2, destination=1,
proportion=1.0),
# As the Asians merge to EUR, turn off their migration rates
msprime.MigrationRateChange(time=T_EU_AS, rate=0, matrix_index=(1, 2)),
msprime.MigrationRateChange(time=T_EU_AS, rate=0, matrix_index=(2, 1)),
msprime.MigrationRateChange(time=T_EU_AS, rate=0, matrix_index=(0, 2)),
msprime.MigrationRateChange(time=T_EU_AS, rate=0, matrix_index=(2, 0)),
# As the Asians merge to EUR, turn off their growth rates
msprime.PopulationParametersChange(
time=T_EU_AS, initial_size=N_AS0, growth_rate=0, population_id=2),
# Europeans grow from N_EU0 at rate r_EU at time T_EU_AS
msprime.PopulationParametersChange(
time=T_EU_AS, initial_size=N_EU0, growth_rate=r_EU, population_id=1
),
# Pop 1 (EUR/AS) size change at time T_B
msprime.PopulationParametersChange(time=T_B, initial_size=N_EU0,
growth_rate=0, population_id=1),
# YRI merges with B at T_B
msprime.MassMigration(time=T_B, source=1, destination=0, proportion=1.0
),
# Set migrations to 0
msprime.MigrationRateChange(time=T_B, rate=0),
# Pop 0 (AFR) size change at time T_B
msprime.PopulationParametersChange(time=T_B, initial_size=N_B,
growth_rate=0, population_id=0),
# msprime.MigrationRateChange(time=T_B, rate=0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(time=T_AF, initial_size=N_A,
population_id=0)
]
# dp = msprime.DemographyDebugger(
# Ne=N_A,
# population_configurations=population_configurations,
# migration_matrix=migration_matrix,
# demographic_events=demographic_events)
# dp.print_history()
# with open('demography.txt', 'w') as fn:
# dp.print_history(output=fn)
if rec_map is not None:
rmap = msprime.RecombinationMap.read_hapmap(rec_map)
nvars = None
rr = None
else:
rmap = None
rr = 2e-8
settings = {
#'model': msprime.DiscreteTimeWrightFisher(0.25),
'population_configurations': population_configurations,
'migration_matrix': migration_matrix,
'recombination_rate': rr,
'demographic_events': demographic_events,
'mutation_rate': 1.44e-8, # according to 10.1371/journal.pgen.1004023
'recombination_map': rmap,
'length': nvars
}
vcf_filename = "OOA_Latino.vcf.gz"
if not os.path.isfile('Latino.hdf5'):
ts = msprime.simulate(**settings)
print("Original file contains ", ts.get_num_mutations(), "mutations")
if maf is not None:
ts = strip_singletons(ts, maf)
print("New file contains ", ts.get_num_mutations(), "mutations")
ts.dump('Latino.hdf5', True)
with open(vcf_filename, "w") as vcf_file:
ts.write_vcf(vcf_file, 2)
else:
ts = msprime.load('Latino.hdf5')
if to_bed is not None:
if split_out:
split = zip(labels, nhaps)
else:
split = False
make_plink(vcf_filename, to_bed, threads, split, focus_pops)
if plot_pca:
pca = skpca(vcf_filename[: vcf_filename.rfind('.vcf')], 2, threads,
None, None)
count = 0
for i, haps in enumerate(nhaps):
haps = haps // 2
pca.loc[count:(count + haps - 1), 'continent'] = labels[i]
count += haps
colors = iter(['k', 'b', 'y', 'r', 'g', 'c'])
fig, ax = plt.subplots()
for c, df in pca.groupby('continent'):
df.plot.scatter(x='PC1', y='PC2', c=next(colors), ax=ax, label=c)
plt.savefig('simulationPCA.pdf')
def __init__(self):
super().__init__()
# First we set out the maximum likelihood values of the various parameters
# given in Table 1.
N_A = 7300
N_B = 2100
N_AF = 12300
N_EU0 = 1000
N_AS0 = 510
# Times are provided in years, so we convert into generations.
self.generation_time = default_generation_time
T_AF = 220e3 / self.generation_time
T_B = 140e3 / self.generation_time
T_EU_AS = 21.2e3 / self.generation_time
# We need to work out the starting (diploid) population sizes based on
# the growth rates provided for these two populations
r_EU = 0.004
r_AS = 0.0055
N_EU = N_EU0 / math.exp(-r_EU * T_EU_AS)
N_AS = N_AS0 / math.exp(-r_AS * T_EU_AS)
# Migration rates during the various epochs.
m_AF_B = 25e-5
m_AF_EU = 3e-5
m_AF_AS = 1.9e-5
m_EU_AS = 9.6e-5
# Population IDs correspond to their indexes in the population
# configuration array. Therefore, we have 0=YRI, 1=CEU and 2=CHB
# initially.
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=N_AF),
msprime.PopulationConfiguration(initial_size=N_EU,
growth_rate=r_EU),
msprime.PopulationConfiguration(initial_size=N_AS,
growth_rate=r_AS)
]
self.migration_matrix = [
[0, m_AF_EU, m_AF_AS], # noqa
[m_AF_EU, 0, m_EU_AS], # noqa
[m_AF_AS, m_EU_AS, 0], # noqa
]
self.demographic_events = [
# CEU and CHB merge into B with rate changes at T_EU_AS
msprime.MassMigration(time=T_EU_AS,
source=2,
destination=1,
proportion=1.0),
msprime.MigrationRateChange(time=T_EU_AS, rate=0),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=T_EU_AS,
rate=m_AF_B,
matrix_index=(1, 0)),
msprime.PopulationParametersChange(time=T_EU_AS,
initial_size=N_B,
growth_rate=0,
population_id=1),
# Population B merges into YRI at T_B
msprime.MassMigration(time=T_B,
source=1,
destination=0,
proportion=1.0),
# Size changes to N_A at T_AF
msprime.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0)
]
def test_migration_rate_change(self):
examples = [
msprime.MigrationRateChange(time=1, rate=1),
msprime.MigrationRateChange(time=1, rate=1, source=1, dest=2),
]
self.assert_repr_round_trip(examples)
def __init__(self):
super().__init__()
N0 = 7310 # initial population size
Thum = 5920 # time (gens) of advent of modern humans
Naf = 14474 # size of african population
Tooa = 2040 # number of generations back to Out of Africa
Nb = 1861 # size of out of Africa population
mafb = 1.5e-4 # migration rate Africa and Out-of-Africa
Teu = 920 # number generations back to Asia-Europe split
Neu = 1032 # bottleneck population sizes
Nas = 554
mafeu = 2.5e-5 # mig. rates
mafas = 7.8e-6
meuas = 3.11e-5
reu = 0.0038 # growth rate per generation in Europe
ras = 0.0048 # growth rate per generation in Asia
Tadmix = 12 # time of admixture
Nadmix = 30000 # initial size of admixed population
radmix = .05 # growth rate of admixed population
# pop0 is Africa, pop1 is Europe, pop2 is Asia, pop3 is admixed
self.population_configurations = [
msprime.PopulationConfiguration(initial_size=Naf, growth_rate=0.0),
msprime.PopulationConfiguration(initial_size=Neu *
math.exp(reu * Teu),
growth_rate=reu),
msprime.PopulationConfiguration(initial_size=Nas *
math.exp(ras * Teu),
growth_rate=ras),
msprime.PopulationConfiguration(initial_size=Nadmix *
math.exp(radmix * Tadmix),
growth_rate=radmix)
]
self.migration_matrix = [[0, mafeu, mafas, 0], [mafeu, 0, meuas, 0],
[mafas, meuas, 0, 0], [0, 0, 0, 0]]
# Admixture event, 1/6 Africa, 2/6 Europe, 3/6 Asia
admixture_event = [
msprime.MassMigration(time=Tadmix,
source=3,
destination=0,
proportion=1.0 / 6.0),
msprime.MassMigration(time=Tadmix + 0.0001,
source=3,
destination=1,
proportion=2.0 / 5.0),
msprime.MassMigration(time=Tadmix + 0.0002,
source=3,
destination=2,
proportion=1.0)
]
# Asia and Europe split
eu_event = [
msprime.MigrationRateChange(time=Teu, rate=0.0),
msprime.MassMigration(time=Teu + 0.0001,
source=2,
destination=1,
proportion=1.0),
msprime.PopulationParametersChange(time=Teu + 0.0002,
initial_size=Nb,
growth_rate=0.0,
population_id=1),
msprime.MigrationRateChange(time=Teu + 0.0003,
rate=mafb,
matrix_index=(0, 1)),
msprime.MigrationRateChange(time=Teu + 0.0003,
rate=mafb,
matrix_index=(1, 0))
]
# Out of Africa event
ooa_event = [
msprime.MigrationRateChange(time=Tooa, rate=0.0),
msprime.MassMigration(time=Tooa + 0.0001,
source=1,
destination=0,
proportion=1.0)
]
# initial population size
init_event = [
msprime.PopulationParametersChange(time=Thum,
initial_size=N0,
population_id=0)
]
self.demographic_events = admixture_event + eu_event + ooa_event + init_event
def __init__(self,
ta,
n0=1,
na=1,
Ne=1e4,
rec_rate=1e-4,
loci=2,
reps=100):
"""Initialize the model."""
generation_time = 25
T_AF = 148e3 / generation_time
T_OOA = 51e3 / generation_time
T_EU0 = 23e3 / generation_time
T_EG = 5115 / generation_time
# Growth rates
r_EU0 = 0.00307
r_EU = 0.0195
r_AF = 0.0166
# population sizes
N_A = 7310
N_AF1 = 14474
N_B = 1861
N_EU0 = 1032
N_EU1 = N_EU0 / np.exp(-r_EU0 * (T_EU0 - T_EG))
# migration rates
m_AF_B = 15e-5
m_AF_EU = 2.5e-5
# present Ne
N_EU = N_EU1 / np.exp(-r_EU * T_EG)
N_AF = N_AF1 / np.exp(-r_AF * T_EG)
population_configurations = [
msp.PopulationConfiguration(initial_size=N_AF, growth_rate=r_AF),
msp.PopulationConfiguration(initial_size=N_EU, growth_rate=r_EU),
]
migration_matrix = [[0, m_AF_EU], [m_AF_EU, 0]]
demographic_events = [
msp.MigrationRateChange(time=T_EG,
rate=m_AF_EU,
matrix_index=(0, 1)),
msp.MigrationRateChange(time=T_EG,
rate=m_AF_EU,
matrix_index=(1, 0)),
msp.PopulationParametersChange(time=T_EG,
growth_rate=r_EU0,
initial_size=N_EU1,
population_id=1),
msp.PopulationParametersChange(time=T_EG,
growth_rate=0,
initial_size=N_AF1,
population_id=0),
msp.MigrationRateChange(time=T_EU0,
rate=m_AF_B,
matrix_index=(0, 1)),
msp.MigrationRateChange(time=T_EU0,
rate=m_AF_B,
matrix_index=(1, 0)),
msp.PopulationParametersChange(time=T_EU0,
initial_size=N_B,
growth_rate=0,
population_id=1),
msp.MassMigration(time=T_OOA,
source=1,
destination=0,
proportion=1.0),
msp.PopulationParametersChange(time=T_AF,
initial_size=N_A,
population_id=0),
]
self.pop_config = population_configurations
self.migration_matrix = migration_matrix
self.demography = demographic_events
self.rec_rate = rec_rate
self.loci = loci
self.samples1 = [msp.Sample(population=1, time=0) for i in range(n0)]
self.samples2 = [msp.Sample(population=1, time=ta) for i in range(na)]
self.samples = self.samples1 + self.samples2
self.reps = reps
self.Ne = Ne
self.treeseq = None
def create_simulation_runner(parser, arg_list):
"""
Parses the arguments and returns a SimulationRunner instance.
"""
args = parser.parse_args(arg_list)
if args.mutation_rate == 0 and not args.trees:
parser.error("Need to specify at least one of --theta or --trees")
num_loci = int(args.recombination[1])
if args.recombination[1] != num_loci:
parser.error("Number of loci must be integer value")
if args.recombination[0] != 0.0 and num_loci < 2:
parser.error("Number of loci must > 1")
r = 0.0
# We don't scale recombination or mutation rates by the size
# of the region.
if num_loci > 1:
r = args.recombination[0] / (num_loci - 1)
mu = args.mutation_rate / num_loci
# ms uses a ratio to define the GC rate, but if the recombination rate
# is zero we define the gc rate directly.
gc_param, gc_tract_length = args.gene_conversion
gc_rate = 0
if r == 0.0:
if num_loci > 1:
gc_rate = gc_param / (num_loci - 1)
else:
gc_rate = r * gc_param
demography = msprime.Demography.isolated_model([1])
# Check the structure format.
symmetric_migration_rate = 0.0
num_populations = 1
migration_matrix = [[0.0]]
num_samples = [args.sample_size]
if args.structure is not None:
num_populations = convert_int(args.structure[0], parser)
# We must have at least num_population sample_configurations
if len(args.structure) < num_populations + 1:
parser.error("Must have num_populations sample sizes")
demography = msprime.Demography.isolated_model([1] * num_populations)
num_samples = [0] * num_populations
for j in range(num_populations):
num_samples[j] = convert_int(args.structure[j + 1], parser)
if sum(num_samples) != args.sample_size:
parser.error("Population sample sizes must sum to sample_size")
# We optionally have the overall migration_rate here
if len(args.structure) == num_populations + 2:
symmetric_migration_rate = convert_float(
args.structure[num_populations + 1], parser
)
check_migration_rate(parser, symmetric_migration_rate)
elif len(args.structure) > num_populations + 2:
parser.error("Too many arguments to --structure/-I")
if num_populations > 1:
migration_matrix = [
[
symmetric_migration_rate / (num_populations - 1) * int(j != k)
for j in range(num_populations)
]
for k in range(num_populations)
]
else:
if len(args.migration_matrix_entry) > 0:
parser.error(
"Cannot specify migration matrix entries without "
"first providing a -I option"
)
if args.migration_matrix is not None:
parser.error(
"Cannot specify a migration matrix without "
"first providing a -I option"
)
if args.migration_matrix is not None:
migration_matrix = convert_migration_matrix(
parser, args.migration_matrix, num_populations
)
for matrix_entry in args.migration_matrix_entry:
pop_i = convert_population_id(parser, matrix_entry[0], num_populations)
pop_j = convert_population_id(parser, matrix_entry[1], num_populations)
rate = matrix_entry[2]
if pop_i == pop_j:
parser.error("Cannot set diagonal elements in migration matrix")
check_migration_rate(parser, rate)
migration_matrix[pop_i][pop_j] = rate
# Set the initial demography
if args.growth_rate is not None:
for population in demography.populations:
population.growth_rate = args.growth_rate
for population_id, growth_rate in args.population_growth_rate:
pid = convert_population_id(parser, population_id, num_populations)
demography.populations[pid].growth_rate = growth_rate
for population_id, size in args.population_size:
pid = convert_population_id(parser, population_id, num_populations)
demography.populations[pid].initial_size = size
demographic_events = []
# First we look at population split events. We do this differently
# to ms, as msprime requires a fixed number of population. Therefore,
# modify the number of populations to take into account populations
# splits. This is a messy hack, and will probably need to be changed.
for index, (t, population_id, proportion) in args.admixture:
check_event_time(parser, t)
pid = convert_population_id(parser, population_id, num_populations)
if proportion < 0 or proportion > 1:
parser.error("Proportion value must be 0 <= p <= 1.")
# In ms, the probability of staying in source is p and the probabilty
# of moving to the new population is 1 - p.
event = (index, msprime.MassMigration(t, pid, num_populations, 1 - proportion))
demographic_events.append(event)
num_populations += 1
# We add another element to each row in the migration matrix
# along with an other row. All new entries are zero.
for row in migration_matrix:
row.append(0)
migration_matrix.append([0 for j in range(num_populations)])
demography.populations.append(msprime.Population(initial_size=1))
num_samples.append(0)
# Add the demographic events
for index, (t, alpha) in args.growth_rate_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eG")
check_event_time(parser, t)
demographic_events.append(
(index, msprime.PopulationParametersChange(time=t, growth_rate=alpha))
)
for index, (t, population_id, alpha) in args.population_growth_rate_change:
pid = convert_population_id(parser, population_id, num_populations)
check_event_time(parser, t)
demographic_events.append(
(
index,
msprime.PopulationParametersChange(
time=t, growth_rate=alpha, population_id=pid
),
)
)
for index, (t, x) in args.size_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eN")
check_event_time(parser, t)
demographic_events.append(
(
index,
msprime.PopulationParametersChange(
time=t, initial_size=x, growth_rate=0
),
)
)
for index, (t, population_id, x) in args.population_size_change:
check_event_time(parser, t)
pid = convert_population_id(parser, population_id, num_populations)
demographic_events.append(
(
index,
msprime.PopulationParametersChange(
time=t, initial_size=x, growth_rate=0, population_id=pid
),
)
)
for index, (t, pop_i, pop_j) in args.population_split:
check_event_time(parser, t)
pop_i = convert_population_id(parser, pop_i, num_populations)
pop_j = convert_population_id(parser, pop_j, num_populations)
demographic_events.append((index, msprime.MassMigration(t, pop_i, pop_j, 1.0)))
# Migration rates from subpopulation i (M[k, i], k != i) are set to zero.
for k in range(num_populations):
if k != pop_i:
event = msprime.MigrationRateChange(t, 0.0, matrix_index=(k, pop_i))
demographic_events.append((index, event))
# Demographic events that affect the migration matrix
if num_populations == 1:
condition = (
len(args.migration_rate_change) > 0
or len(args.migration_matrix_entry_change) > 0
or len(args.migration_matrix_change) > 0
)
if condition:
parser.error("Cannot change migration rates for 1 population")
for index, (t, x) in args.migration_rate_change:
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-eM")
check_migration_rate(parser, x)
check_event_time(parser, t)
event = msprime.MigrationRateChange(t, x / (num_populations - 1))
demographic_events.append((index, event))
for index, event in args.migration_matrix_entry_change:
t = event[0]
check_event_time(parser, t)
pop_i = convert_population_id(parser, event[1], num_populations)
pop_j = convert_population_id(parser, event[2], num_populations)
if pop_i == pop_j:
parser.error("Cannot set diagonal elements in migration matrix")
rate = event[3]
check_migration_rate(parser, rate)
msp_event = msprime.MigrationRateChange(t, rate, matrix_index=(pop_i, pop_j))
demographic_events.append((index, msp_event))
for index, event in args.migration_matrix_change:
if len(event) < 3:
parser.error("Need at least three arguments to -ma")
if len(args.admixture) != 0:
raise_admixture_incompatability_error(parser, "-ema")
t = convert_float(event[0], parser)
check_event_time(parser, t)
if convert_int(event[1], parser) != num_populations:
parser.error("num_populations must be equal for new migration matrix")
matrix = convert_migration_matrix(parser, event[2:], num_populations)
for j in range(num_populations):
for k in range(num_populations):
if j != k:
msp_event = msprime.MigrationRateChange(
t, matrix[j][k], matrix_index=(j, k)
)
demographic_events.append((index, msp_event))
demographic_events.sort(key=lambda x: (x[0], x[1].time))
time_sorted = sorted(demographic_events, key=lambda x: x[1].time)
if demographic_events != time_sorted:
parser.error("Demographic events must be supplied in non-decreasing time order")
demography.events = [event for _, event in demographic_events]
demography.migration_matrix = migration_matrix
# Adjust the population sizes so that the timescales agree. In principle
# we could correct this with a ploidy value=0.5, but what we have here
# seems less awful.
for msp_event in demography.events:
if isinstance(msp_event, msprime.PopulationParametersChange):
if msp_event.initial_size is not None:
msp_event.initial_size /= 2
for j, pop in enumerate(demography.populations):
pop.initial_size /= 2
pop.name = f"pop_{j}"
runner = SimulationRunner(
num_samples,
demography,
num_loci=num_loci,
num_replicates=args.num_replicates,
recombination_rate=r,
mutation_rate=mu,
gene_conversion_rate=gc_rate,
gene_conversion_tract_length=gc_tract_length,
precision=args.precision,
print_trees=args.trees,
ms_random_seeds=args.random_seeds,
hotspots=args.hotspots,
)
return runner
#set up the initial population
ss_each = args.ss * 2
sample_sizes = [ss_each] * (d)
population_configurations = [
msprime.PopulationConfiguration(sample_size=k) for k in sample_sizes
]
population_configurations.extend(
[msprime.PopulationConfiguration(sample_size=0)] * 3)
############ set up the demography
demog_list = [
#change migration rate to 0 on the 100th generation
[msprime.MigrationRateChange(time=100, rate=0)],
#move lineages to the north (deme = 0) or south (deme = 35) to create N-S gradient
[
msprime.MassMigration(time=100,
source=i,
destination=0,
proportion=1.0) for i in range(1, 6)
],
[
msprime.MassMigration(time=100,
source=i,
destination=0,
proportion=0.8) for i in range(6, 12)
],
[
msprime.MassMigration(time=100,
