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QC for AraTha DFE #1647

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Jan 10, 2025
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QC for AraTha DFE #1647

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27 changes: 13 additions & 14 deletions stdpopsim/catalog/AraTha/dfes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,19 +10,15 @@


def _HuberDFE():
id = "Gamma_H18"
description = "Deleterious Gamma DFE"
id = "GammaAdditive_H18"
description = "Deleterious additive Gamma DFE"
long_description = """
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Ah - could you update the long description, also? How about

    Additive, deleterious DFE from Huber et al. (2018) for Arabidopsis, 
    estimated using <software> from A. lyrata as a Gamma distribution
    of deleterious effects. Parameters are from Supplementary Table 4,
    the "genome-wide, additive-only model for A. lyrata". The DFE for
    lyrata (rather than thaliana) is provided due to challenges with
    simulating selfing.

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ah, I see <software> wasn't some named software package, they just did it using Methods from the paper. I rephrased this to "using the SFS"

Return negative (no neutral, in this case) MutationType()s
representing an Arabidopsis DFE. From Huber et al. (2018).
Gamma parameters are based on Supplementary Table 4,
the genome-wide, additive-only model for A. LYRATA- due to
challenges with simulating with selfing for A. thaliana.
The Supplementary Table 4 DFEs are not noted to contain
any neutral mutations (in contrast, Supplementary Table 3
notes neutral proportions for two other DFEs), and in the
main text including neutral mutations seems like a
supplementary analysis rather than the main strategy.
Additive, deleterious DFE from Huber et al. (2018) for Arabidopsis,
estimated from the SFS of A. lyrata as a Gamma distribution
of deleterious effects. Parameters are from Supplementary Table 4,
the "genome-wide, additive-only model for A. lyrata". The DFE for
A. lyrata (rather than A. thaliana) is provided due to challenges
with simulating selfing.
"""
citations = [
stdpopsim.Citation(
Expand All @@ -34,12 +30,15 @@ def _HuberDFE():
]
neutral = stdpopsim.MutationType()
gamma_shape = 0.27 # shape
gamma_mean = -0.0004 # expected value
gamma_scale = -0.0004 # scale
gamma_mean = gamma_shape * gamma_scale
h = 0.5 # dominance coefficient
negative = stdpopsim.MutationType(
dominance_coeff=h,
distribution_type="g", # gamma distribution
distribution_args=[gamma_mean, gamma_shape],
# extra factor of 2 is to convert dadi to SLiM
# (1+s for homozygote in SLiM versus 1+2s in dadi)
distribution_args=[-2 * gamma_mean, gamma_shape],
)

return stdpopsim.DFE(
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26 changes: 26 additions & 0 deletions stdpopsim/qc/AraTha.py
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Expand Up @@ -208,3 +208,29 @@ def HuberThreeEpoch():


_species.get_demographic_model("African3Epoch_1H18").register_qc(HuberThreeEpoch())


def HuberDFE():
id = "QC-GammaAdditive_H18"
# Huber et al. 2018 Supplementary table 4.
# genome-wide A. lyrata additive fitness effects
neutral = stdpopsim.MutationType()
gamma_shape = 0.27
gamma_scale = 0.0004
gamma_mean = -(gamma_shape * gamma_scale) # negative since deleterious mutations
h = 0.5
negative = stdpopsim.MutationType(
dominance_coeff=h,
distribution_type="g",
distribution_args=[-2 * gamma_mean, gamma_shape],
)
return stdpopsim.DFE(
id=id,
description=id,
long_description=id,
mutation_types=[neutral, negative],
proportions=[0.0, 1.0],
)


_species.get_dfe("GammaAdditive_H18").register_qc(HuberDFE())
2 changes: 1 addition & 1 deletion tests/test_slim_engine.py
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Expand Up @@ -1213,7 +1213,7 @@ def test_not_simulated_outside_region(self):

left, right = 100000, 900000
contig = species.get_contig("1", left=left, right=right)
dfe = species.get_dfe("Gamma_H18")
dfe = species.get_dfe("GammaAdditive_H18")
exons = species.get_annotations("araport_11_exons")
exon_intervals = exons.get_chromosome_annotations("1")
contig.add_dfe(intervals=exon_intervals, DFE=dfe)
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