DroMel too

stdpopsim/catalog/DroMel/dfes.py

@@ -36,20 +36,83 @@ def _HuberDFE():
         distribution_type="g",  # gamma distribution
         distribution_args=[gamma_mean, gamma_shape],
     )
+    # p. 2 in supplement says that the total sequence length of synonymous sites LS
+    # related to the total sequence length LNS by LNS = 2.85 * LS;
+    # so this is 1 / (1 + 2.85) = 0.2597402597402597
+    prop_synonymous = 0.26
 
     return stdpopsim.DFE(
         id=id,
         description=description,
         long_description=long_description,
         mutation_types=[neutral, negative],
-        proportions=[0.26, 0.74],  # LNS = 2.85 x LS
+        proportions=[prop_synonymous, 1 - prop_synonymous],
         citations=citations,
     )
 
 
 _species.add_dfe(_HuberDFE())
 
 
+def _ZhenDFE():
+    id = "GammaPos_H17"
+    description = "Deleterious Gamma DFE with fixed-s beneficials"
+    long_description = """
+    The DFE estimated from D.melanogaster-simulans data estimated in Zhen et al
+    (2021, https://dx.doi.org/10.1101/gr.256636.119).
+    This uses the demographic model and deleterious-only DFE from
+    Huber et al (2017), and then the number of nonsynonymous differences
+    to simulans to estimate a proportion of nonsynonmyous differences
+    and (single) selection coefficient. So, this is the "Gamma_H17" DFE,
+    with some proportion of positive selection with fixed s.
+    """
+    citations = [
+        stdpopsim.Citation(
+            author="Zhen et al.",
+            year=2021,
+            doi="https://dx.doi.org/10.1101/gr.256636.119",
+            reasons={stdpopsim.CiteReason.DFE},
+        )
+    ]
+    neutral = stdpopsim.MutationType()
+    gamma_shape = 0.33  # shape
+    gamma_mean = -3.96e-04  # expected value
+    h = 0.5  # dominance coefficient
+    negative = stdpopsim.MutationType(
+        dominance_coeff=h,
+        distribution_type="g",  # gamma distribution
+        distribution_args=[gamma_mean, gamma_shape],
+    )
+    # p. 2 in supplement says that the total sequence length of synonymous sites LS
+    # related to the total sequence length LNS by LNS = 2.85 * LS
+    # so this is 1 / (1 + 2.85) = 0.2597402597402597
+    prop_synonymous = 0.26
+
+    sel_coeff = 10 ** (-4.801)
+    prop_beneficials = 6.75e-4
+    positive = stdpopsim.MutationType(
+        dominance_coeff=0.5,
+        distribution_type="f",
+        distribution_args=[sel_coeff],
+    )
+
+    return stdpopsim.DFE(
+        id=id,
+        description=description,
+        long_description=long_description,
+        mutation_types=[neutral, negative, positive],
+        proportions=[
+            prop_synonymous,
+            (1 - prop_synonymous) * (1 - prop_beneficials),
+            (1 - prop_synonymous) * prop_beneficials,
+        ],
+        citations=citations,
+    )
+
+
+_species.add_dfe(_ZhenDFE())
+
+
 def _RagsdaleDFE():
     id = "LognormalPlusPositive_R16"
     description = "Deleterious log-normal and beneficial mixed DFE"

stdpopsim/catalog/HomSap/dfes.py

@@ -149,59 +149,6 @@ def _HuberLogNormalDFE():
 _species.add_dfe(_HuberLogNormalDFE())
 
 
-def _KyriazisDFE():
-    id = "Mixed_K23"
-    description = "Deleterious Gamma DFE with additional lethals"
-    long_description = """
-    The DFE estimated from human data recommended in Kyriazis et al.
-    (2023), https://doi.org/10.1086/726736, for general use.
-    This model is similar to the Kim et al. (2017) DFE based on human
-    genetic data, modified to include the dominance distribution from
-    Henn et al. (2016).
-    The model is also augmented with an additional proportion of 0.3% of
-    recessive lethals, based on the analysis of Wade et al. (2023).
-    """
-    citations = [
-        stdpopsim.Citation(
-            author="Kyriazis et al.",
-            year=2023,
-            doi="https://doi.org/10.1086/726736",
-            reasons={stdpopsim.CiteReason.DFE},
-        )
-    ]
-    neutral = stdpopsim.MutationType()
-    gamma_mean = -0.0131
-    gamma_shape = 0.186
-    coefs = [0.45, 0.2, 0.05, 0]
-    breaks = [0.001, 0.01, 0.1]
-    gamma = stdpopsim.MutationType(
-        dominance_coeff_list=coefs,
-        dominance_coeff_breaks=breaks,
-        distribution_type="g",  # gamma distribution
-        distribution_args=[gamma_mean, gamma_shape],
-    )
-    lethal = stdpopsim.MutationType(
-        distribution_type="f",  # fixed value
-        distribution_args=[-1],  # fitness in SLiM for homozygotes is multiiplied by 1+s
-        dominance_coeff=0,
-    )
-    proportion_deleterious = 2.31 / (1 + 2.31)
-    lethal_prop = proportion_deleterious * 0.003  # 0.3% lethals
-    gamma_prop = proportion_deleterious - lethal_prop
-    neutral_prop = 1 - proportion_deleterious
-    return stdpopsim.DFE(
-        id=id,
-        description=description,
-        long_description=long_description,
-        mutation_types=[neutral, gamma, lethal],
-        proportions=[neutral_prop, gamma_prop, lethal_prop],
-        citations=citations,
-    )
-
-
-_species.add_dfe(_KyriazisDFE())
-
-
 def _ZhenDFE():
     id = "GammaPos_Z21"
     description = "Deleterious Gamma DFE with fixed-s beneficials"
@@ -262,3 +209,56 @@ def _ZhenDFE():
 
 
 _species.add_dfe(_ZhenDFE())
+
+
+def _KyriazisDFE():
+    id = "Mixed_K23"
+    description = "Deleterious Gamma DFE with additional lethals"
+    long_description = """
+    The DFE estimated from human data recommended in Kyriazis et al.
+    (2023), https://doi.org/10.1086/726736, for general use.
+    This model is similar to the Kim et al. (2017) DFE based on human
+    genetic data, modified to include the dominance distribution from
+    Henn et al. (2016).
+    The model is also augmented with an additional proportion of 0.3% of
+    recessive lethals, based on the analysis of Wade et al. (2023).
+    """
+    citations = [
+        stdpopsim.Citation(
+            author="Kyriazis et al.",
+            year=2023,
+            doi="https://doi.org/10.1086/726736",
+            reasons={stdpopsim.CiteReason.DFE},
+        )
+    ]
+    neutral = stdpopsim.MutationType()
+    gamma_mean = -0.0131
+    gamma_shape = 0.186
+    coefs = [0.45, 0.2, 0.05, 0]
+    breaks = [0.001, 0.01, 0.1]
+    gamma = stdpopsim.MutationType(
+        dominance_coeff_list=coefs,
+        dominance_coeff_breaks=breaks,
+        distribution_type="g",  # gamma distribution
+        distribution_args=[gamma_mean, gamma_shape],
+    )
+    lethal = stdpopsim.MutationType(
+        distribution_type="f",  # fixed value
+        distribution_args=[-1],  # fitness in SLiM for homozygotes is multiiplied by 1+s
+        dominance_coeff=0,
+    )
+    proportion_deleterious = 2.31 / (1 + 2.31)
+    lethal_prop = proportion_deleterious * 0.003  # 0.3% lethals
+    gamma_prop = proportion_deleterious - lethal_prop
+    neutral_prop = 1 - proportion_deleterious
+    return stdpopsim.DFE(
+        id=id,
+        description=description,
+        long_description=long_description,
+        mutation_types=[neutral, gamma, lethal],
+        proportions=[neutral_prop, gamma_prop, lethal_prop],
+        citations=citations,
+    )
+
+
+_species.add_dfe(_KyriazisDFE())