class HuberTwoEpoch(models.DemographicModel):
populations = [
models.Population(id="ATL", description="A. thalina"),
]
def __init__(self):
# Time of second epoch
T_2 = 568344
# population sizes
N_ANC = 746148
N_2 = 100218
self.population_configurations = [
msprime.PopulationConfiguration(
initial_size=N_2, metadata=self.populations[0].asdict()),
]
self.migration_matrix = [[0]]
self.demographic_events = [
msprime.PopulationParametersChange(time=T_2,
initial_size=N_ANC,
population_id=0),
]
class HuberThreeEpoch(models.DemographicModel):
populations = [
models.Population(id="ATL", description="A. thalina"),
]
def __init__(self):
# Time of second epoch
T_2 = 7420
T_3 = 14534
# population sizes
N_ANC = 161744
N_2 = 24076
N_3 = 203077
self.population_configurations = [
msprime.PopulationConfiguration(
initial_size=N_3, metadata=self.populations[0].asdict()),
]
self.migration_matrix = [[0]]
self.demographic_events = [
msprime.PopulationParametersChange(time=T_3,
initial_size=N_2,
population_id=0),
msprime.PopulationParametersChange(time=T_2 + T_3,
initial_size=N_ANC,
population_id=0),
]
# This script is a QC implementation of the Pongo model
import msprime
import numpy as np
import stdpopsim
import stdpopsim.models as models
_species = stdpopsim.get_species("PonAbe")
# Some generic populations to use for qc
population_sample_0 = models.Population("sampling_0",
"Population that samples at time 0",
0)
class LockePongo(models.DemographicModel):
populations = [population_sample_0] * 2
def __init__(self):
# This is a split-migration style model, with exponential growth or
# decay allowed in each population after the split. They assumed a
# generation time of 20 years and a mutation rate of 2e-8 per bp per gen
generation_time = 20
# Parameters given in Table S21-2
Ne = 17934
s = 0.592
NB0 = s*Ne
NS0 = (1-s)*Ne
NBF = 8805
class DenisovanAncestryInPapuans(models.DemographicModel):
"""
Demographic model from Jacobs et al (2019). The model is
illustrated on Figure S5, parameters are in Table S5
"""
populations = [population_sample_0] * 4 + [
models.Population("", "", 2058),
models.Population("", "", 2612),
] + [population_sample_none] * 4
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": None}),
msprime.PopulationConfiguration( # 7 Den2
initial_size=N_Den2, growth_rate=0,
metadata={"name": "Den2", "sampling_time": None}),
msprime.PopulationConfiguration( # 8 Nean1
initial_size=N_Nean1, growth_rate=0,
metadata={"name": "Nean1", "sampling_time": None}),
msprime.PopulationConfiguration( # 9 Ghost
initial_size=N_Ghost, growth_rate=0,
metadata={"name": "Ghost", "sampling_time": None})
]
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)
class KammAncientSamples(models.DemographicModel):
"""
Demographic inferred by momi described in Kamm et al. (2019). The model is
illustrated in Figure 3, with parameters given in Table 2.
"""
populations = [
population_sample_0,
models.Population("", "", 8e3 / 25),
population_sample_0,
models.Population("", "", 7.5e3 / 25),
models.Population("", "", 24e3 / 25),
population_sample_0,
models.Population("", "", 45e3 / 25),
models.Population("", "", 50e3 / 25),
population_sample_none
]
def __init__(self):
generation_time = 25
# population sizes
N_Losch = 1.92e3
N_Mbu = 1.73e4
N_Mbu_Losch = 2.91e4
N_Han = 6.3e3
N_Han_Losch = 2.34e3
N_Nean_Losch = 1.82e4
N_Nean = 86.9
N_LBK = 75.7
N_Sard = 1.5e4
N_Sard_LBK = 1.2e4
# unknown population sizes
# these should be set to ancestral Eurasian population size,
# but at the moment it's unclear to me if that is N_Han_Losch? N_Losch?
N_Basal = N_Losch
N_Ust = N_Basal
N_MA1 = N_Basal
# population merge times in years, divided by generation time
t_Mbu_Losch = 9.58e4 / generation_time
t_Han_Losch = 5.04e4 / generation_time
t_Ust_Losch = 5.15e4 / generation_time
t_Nean_Losch = 6.96e5 / generation_time
t_MA1_Losch = 4.49e4 / generation_time
t_LBK_Losch = 3.77e4 / generation_time
t_Basal_Losch = 7.98e4 / generation_time
t_Sard_LBK = 7.69e3 / generation_time
# t_GhostWHG_Losch = 1.56e3 / generation_time
# pulse admixture times and fractions
p_Nean_to_Eur = 0.0296
t_Nean_to_Eur = 5.68e4 / generation_time
p_Basal_to_EEF = 0.0936
t_Basal_to_EEF = 3.37e4 / generation_time
p_GhostWHG_to_Sard = 0.0317
t_GhostWHG_to_Sard = 1.23e3 / generation_time
# sample_times (in years), divided by estimated generation time
t_Mbuti = 0
t_Han = 0
t_Sardinian = 0
t_Loschbour = 7.5e3 / generation_time
t_LBK = 8e3 / generation_time
t_MA1 = 24e3 / generation_time
t_UstIshim = 45e3 / generation_time
t_Altai = 50e3 / generation_time
# set up populations
self.population_configurations = [
msprime.PopulationConfiguration( # Mbuti
initial_size=N_Mbu, growth_rate=0,
metadata={"name": "Mbuti", "sampling_time": t_Mbuti}),
msprime.PopulationConfiguration( # LBK
initial_size=N_LBK, growth_rate=0,
metadata={"name": "LBK", "sampling_time": t_LBK}),
msprime.PopulationConfiguration( # Sardinian
initial_size=N_Sard, growth_rate=0,
metadata={"name": "Sardinian", "sampling_time": t_Sardinian}),
msprime.PopulationConfiguration( # Loschbour
initial_size=N_Losch, growth_rate=0,
metadata={"name": "Loschbour", "sampling_time": t_Loschbour}),
msprime.PopulationConfiguration( # MA1
initial_size=N_MA1, growth_rate=0,
metadata={"name": "MA1", "sampling_time": t_MA1}),
msprime.PopulationConfiguration( # Han
initial_size=N_Han, growth_rate=0,
metadata={"name": "Han", "sampling_time": t_Han}),
msprime.PopulationConfiguration( # UstIshim
initial_size=N_Ust, growth_rate=0,
metadata={"name": "UstIshim", "sampling_time": t_UstIshim}),
msprime.PopulationConfiguration( # Neanderthal
initial_size=N_Nean, growth_rate=0,
metadata={"name": "Altai", "sampling_time": t_Altai}),
msprime.PopulationConfiguration( # Basal Eurasian
initial_size=N_Basal, growth_rate=0,
metadata={"name": "Basal", "sampling_time": None})
]
# no migration rates, only pulse events, so set mig mat to zeros
num_pops = len(self.population_configurations)
self.migration_matrix = [[0] * num_pops] * num_pops
# Compute Neanderthal pop size decline rate
# I'm assuming that the N_Nean is the size of Neanderthal population
# at the time of sampling the Altai individual
r_Nean = -math.log(N_Nean_Losch/N_Nean) / (t_Mbu_Losch-t_Altai)
# Using columns in figure in Kamm paper as proxies for pop number
self.demographic_events = [
msprime.MassMigration(
time=t_GhostWHG_to_Sard, source=2,
destination=3, proportion=p_GhostWHG_to_Sard),
msprime.MassMigration(
time=t_Sard_LBK, source=2, destination=1,
proportion=1.),
msprime.PopulationParametersChange(
time=t_Sard_LBK, initial_size=N_Sard_LBK,
population_id=1),
msprime.MassMigration(
time=t_Basal_to_EEF, source=1, destination=8,
proportion=p_Basal_to_EEF),
msprime.MassMigration(
time=t_LBK_Losch, source=1, destination=3,
proportion=1.),
msprime.MassMigration(
time=t_MA1_Losch, source=4, destination=3,
proportion=1.),
msprime.PopulationParametersChange(
time=t_Altai, initial_size=N_Nean,
growth_rate=r_Nean, population_id=7),
msprime.MassMigration(
time=t_Han_Losch, source=5, destination=3,
proportion=1.),
msprime.PopulationParametersChange(
time=t_Han_Losch, initial_size=N_Han_Losch,
population_id=3),
msprime.MassMigration(
time=t_Ust_Losch, source=6, destination=3,
proportion=1.),
msprime.MassMigration(
time=t_Nean_to_Eur, source=3, destination=7,
proportion=p_Nean_to_Eur),
msprime.MassMigration(
time=t_Basal_Losch, source=8, destination=3,
proportion=1.),
msprime.MassMigration(
time=t_Mbu_Losch, source=0, destination=3,
proportion=1.),
msprime.PopulationParametersChange(
time=t_Mbu_Losch, initial_size=N_Mbu_Losch,
population_id=3),
msprime.PopulationParametersChange(
time=t_Mbu_Losch, initial_size=N_Nean_Losch,
growth_rate=0, population_id=7),
msprime.MassMigration(
time=t_Nean_Losch, source=7, destination=3,
proportion=1.),
msprime.PopulationParametersChange(
time=t_Nean_Losch, initial_size=N_Nean_Losch,
population_id=3)
]
import math
import msprime
import stdpopsim
import stdpopsim.models as models
_species = stdpopsim.get_species("HomSap")
# Some generic populations to use for qc
population_sample_0 = models.Population("sampling_0",
"Population that samples at time 0",
0)
population_sample_none = models.Population("sampling_none",
"Population that does not sample",
None)
class TennessenOnePopAfrica(models.DemographicModel):
populations = [population_sample_0]
def __init__(self):
# This model is the same as the Tennessen two population model except
# the European population has been removed.
# Since the Tennessen one 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. Initially we copy over the pre- exponential growth population
# size estimates, migration rates, and epoch times: