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model.py
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model.py
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import pathlib
import string
import demes
import msprime
import numpy as np
import dinf
populations = ["YRI", "CEU", "CHB"]
samples = dinf.get_samples_from_1kgp_metadata(
"20130606_g1k_3202_samples_ped_population.txt", populations=populations
)
contig_lengths = dinf.get_contig_lengths(
"GRCh38_full_analysis_set_plus_decoy_hla.fa.fai",
keep_contigs={f"chr{c + 1}" for c in range(21)}, # Exclude chrX, etc.
)
num_individuals = 64
recombination_rate = 1.25e-8
mutation_rate = 1.25e-8
sequence_length = 5_000_000
parameters = dinf.Parameters(
# population sizes
N_anc=dinf.Param(low=100, high=30_000),
N_AMH=dinf.Param(low=100, high=30_000),
N_OOA=dinf.Param(low=100, high=10_000),
N_YRI=dinf.Param(low=100, high=100_000),
N_CEU_start=dinf.Param(low=100, high=10_000),
N_CEU_end=dinf.Param(low=1000, high=100_000),
N_CHB_start=dinf.Param(low=100, high=10_000),
N_CHB_end=dinf.Param(low=1000, high=100_000),
# Time units match the demography, which are specified in "years".
# To avoid explicitly defining constraints such as
# "CEU/CHB split more recently than the OOA event",
# we parameterise times as time spans, rather than absolute times.
# time span of AMH
dT_AMH=dinf.Param(low=10_000, high=200_000),
# time span of OOA
dT_OOA=dinf.Param(low=5_000, high=200_000),
# time span of CEU and CHB.
dT_CEU_CHB=dinf.Param(low=10_000, high=50_000),
# migration rates
m_YRI_OOA=dinf.Param(low=1e-6, high=1e-2),
m_YRI_CEU=dinf.Param(low=1e-6, high=1e-2),
m_YRI_CHB=dinf.Param(low=1e-6, high=1e-2),
m_CEU_CHB=dinf.Param(low=1e-6, high=1e-2),
)
def demography(**theta):
# Arguments are expected to match the parameter names.
assert theta.keys() == parameters.keys()
theta["T_OOA_end"] = theta.pop("dT_CEU_CHB")
theta["T_AMH_end"] = theta["T_OOA_end"] + theta.pop("dT_OOA")
theta["T_anc_end"] = theta["T_AMH_end"] + theta.pop("dT_AMH")
model = string.Template(
"""
description: The Gutenkunst et al. (2009) out-of-Africa model.
doi:
- https://doi.org/10.1371/journal.pgen.1000695
time_units: years
generation_time: 25
demes:
- name: ancestral
epochs:
- {end_time: $T_anc_end, start_size: $N_anc}
- name: AMH
ancestors: [ancestral]
epochs:
- {end_time: $T_AMH_end, start_size: $N_AMH}
- name: OOA
ancestors: [AMH]
epochs:
- {end_time: $T_OOA_end, start_size: $N_OOA}
- name: YRI
ancestors: [AMH]
epochs:
- start_size: 12300
- name: CEU
ancestors: [OOA]
epochs:
- {start_size: $N_CEU_start, end_size: $N_CEU_end}
- name: CHB
ancestors: [OOA]
epochs:
- {start_size: $N_CHB_start, end_size: $N_CHB_end}
migrations:
- {demes: [YRI, OOA], rate: $m_YRI_OOA}
- {demes: [YRI, CEU], rate: $m_YRI_CEU}
- {demes: [YRI, CHB], rate: $m_YRI_CHB}
- {demes: [CEU, CHB], rate: $m_CEU_CHB}
"""
).substitute(**theta)
return demes.loads(model)
features = dinf.MultipleBinnedHaplotypeMatrices(
num_individuals={pop: num_individuals for pop in populations},
num_loci={pop: 128 for pop in populations},
ploidy={pop: 2 for pop in populations},
# The so-called "phased" 1kG vcfs also contain unphased genotypes
# for some individuals at some sites.
global_phased=False,
global_maf_thresh=0.05,
)
def generator(seed, **theta):
"""Simulate the Gutenkunst out-of-Africa model with msprime."""
rng = np.random.default_rng(seed)
graph = demography(**theta)
demog = msprime.Demography.from_demes(graph)
seed1, seed2 = rng.integers(low=1, high=2**31, size=2)
ts = msprime.sim_ancestry(
samples={pop: num_individuals for pop in populations},
demography=demog,
sequence_length=sequence_length,
recombination_rate=recombination_rate,
random_seed=seed1,
record_provenance=False,
)
ts = msprime.sim_mutations(ts, rate=mutation_rate, random_seed=seed2)
individuals = {pop: dinf.ts_individuals(ts, pop) for pop in populations}
labelled_matrices = features.from_ts(ts, individuals=individuals)
return labelled_matrices
vcfs = dinf.BagOfVcf(
pathlib.Path("bcf/").glob("*.bcf"),
samples=samples,
contig_lengths=contig_lengths,
)
def target(seed):
rng = np.random.default_rng(seed)
labelled_matrices = features.from_vcf(
vcfs,
sequence_length=sequence_length,
min_seg_sites=20,
max_missing_genotypes=0,
rng=rng,
)
return labelled_matrices
dinf_model = dinf.DinfModel(
target_func=target,
generator_func=generator,
parameters=parameters,
)