PyEpiSIM User Manual
1
Using PyEpiSIM
1.1
Null simulation
s (1.1.1,Figure
1). Five of general parameters, including Cases, Controls,
SNPs, Upper bound of MAFs and Lower bound of MAFs, should
be specified by users freely. The parameter of Models is set to be the
default value 0.
Figure 1 Interface for setting general parameters
s (1.1.2, Figure 2). The parameter of Average
of Adjacent LD should be specified as the default value 0. Other parameters
in Figure 2 need not to be set.
Figure 2 - Interface for setting LD parameters
s (1.1.3, Figure 3). Six output
parameters, including FileName of
chromosome data, FileName of SNP data,
chromosome data (.txt), chromosome data (.mat), SNP data
(.txt), and SNP data (.mat) should be specified by users freely. Repeat
simulation number is set to be default value 1.
Figure 3 - Interface for setting output parameters
s Clicking simulate button for null simulation.
1.2 Generation of LD patterns and haplotype
blocks
s (1.2.1, Figure 1). This step is the
same as the one (1.1.1, Figure 1).
s (1.2.2, Figure 4). The parameter of Average
of Adjacent LD should be specified freely by users in the range of (0,1) . Other
parameters (i.e., Position of the first SNP, Position of the second
SNP, MAF of the first SNP, MAF of the second SNP, and the
LD level) can be set one by one by users. These settings will be accepted
while clicking the Specify button.
Figure 4 Interface for setting LD parameters
s (1.2.3, Figure 3). This step is the
same as the one (1.1.3, Figure 3).
s Clicking simulate button for the
generation of LD patterns and haplotype block.
1.3 Computation of existing public models
s (1.3.1, Figure 1). Five of general
parameters, including Cases, Controls, SNPs, Upper
bound of MAFs and Lower bound of MAFs, need not to be set by users.
The parameter of Models should be set as another value, not the default
value 0. This value is the number of models which will be computed or embedded
into the simulation data later.
s (1.3.2, Figure 5). There are five
steps for the computation of public models. First, selecting the order of a
public model (1-SNP to 4-SNP) in the drop-down box. Second, selecting a public
model in the list box below the drop-down box. The parametric description of
the selected public model is then displayed in the bottom list box. Third,
clicking Add button for the computation of the public model. If the
information Add OK is shown in the right window, the computation is
successful. If the information Add Error is shown in the right window,
the computation is failed. Fifth, the successful computation public model can
be obtained while the simulate button is clicked.
Figure 5 - Interface of the computation of public models
1.4 Computation of Low-order eME Model
PyEpiSIM provides two
strategies for the computation of low-order eME
models given MAFk and the population
prevalence p(D): one is based on
the heritability h2, and another is based on the marginal effect
size l .
The one based on
heritability h2 , MAFk and the population
prevalence p(D) are shown in
Figure 6.
s (1.4.1_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.4.1_2, Figure 6). There are five
steps for the computation of eME models. First,
selecting the order of an eME model (1-SNP to 2-SNP)
in the drop-down box. Second, selecting an eME model
in the list box below the drop-down box. The parametric description of the
selected eME model is then displayed in the bottom
list box. Third, setting parameters, including The
proportion of cases, Heritability of the Model, MAFs of the Model.
Fourth, clicking Add button for the computation of the eME model. If the information Add OK is shown in the
right window, the 4 computation is successful. If the information Add Error is
shown in the right window, the computation is failed. Fifth, the successful
computation eME model can be obtained while the simulate
button is clicked.
Figure 6 - Interface of the first strategy for the computation of low-order eME models
The one based on
the marginal effect size l , MAFk and the population
prevalence p (D) are shown in
Figure 7.
s (1.4.2_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.4.2_2, Figure 7). There are five
steps for the computation of low-order eME models. First, selecting the order of a low-order eME model (1-SNP to
2-SNP) in the drop-down box. Second, selecting a low-order eME model in the list
box below the drop-down box. The parametric description of the selected low-order eME model is then
displayed in the bottom list box. Third, setting parameters, including The proportion of cases, Marginal effect
size of the first SNP ( AA - Aa ), MAFs of the Model. Fourth,
clicking Add button for the computation of the low-order eME model. If the
information Add OK is shown in the right window, the computation is
successful. If the information Add Error is shown in the right window,
the computation is failed. Fifth, the successful computation low-order eME model can be
obtained while the simulate button is clicked.
Figure7 - Interface of the second strategy for the computation of low-order eME models
1.5 Computation of Higher-order
eME Model
PyEpiSIM uses one
constraint of h2 and p(D) to maximize the other constraint, allowing
users to define their own heritability or morbidity without having to find a
combination of heritability and morbidity.
The result of
maximizing p(D) by constraining h2 is shown in Figure 8.
s (1.5.1_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.5.1_2, Figure 8). There are five
steps for the computation of higher-order eME models. First, selecting the order of an higher-order eME model (1-SNP to 4-SNP) in the drop-down box. Second, selecting an higher-order eME model in the list box below the drop-down box. The
parametric description of the selected higher-order eME model is then
displayed in the bottom list box. Third, setting parameters, including The
proportion of cases, Marginal effect size of the first SNP ( AA - Aa
), MAFs of the Model. Fourth, clicking Add button for the
computation of the higher-order eME model. If the information Add OK is shown in the
right window, the computation is successful. If the information Add Error is
shown in the right window, the computation is failed. Fifth, the successful
computation higher-order eME model can be obtained while the simulate button
is clicked.
Figure 8 - Interface of the first strategy for the computation of
higher-order eME models
The result of
maximizing h2 with constraint p (D) is shown in Figure 9.
s (1.5.2_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.5.2_2, Figure 9). There are five
steps for the computation of higher-order eME models. First, selecting the order of a higher-order eME model (1-SNP to 4-SNP) in the
drop-down box. Second, selecting a higher-order eME model in the list box below the drop-down box. The
parametric description of the selected higher-order eME model is then
displayed in the bottom list box. Third, setting parameters, including The
proportion of cases, Marginal effect size of the first SNP ( AA - Aa
), MAFs of the Model. Fourth, clicking Add button for the
computation of the higher-order eME model. If the information Add OK is shown in the
right window, the computation is successful. If the information Add Error is
shown in the right window, the computation is failed. Fifth, the successful
computation higher-order eME model can be obtained while the simulate button
is clicked.
Figure 9 - Interface of the second strategy for the computation of higher-order eME models
1.6 Search of Low-order eNME models
s (1.6.1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.6.2, Figure 10). There are four
steps for the computation of low-order eME models.
First, setting parameters of the 2-SNP eNME model,
including Search veracity, The proportion of cases, Heritability
of the Model and MAFs by users freely. Second, clicking Search button
for the search of the low-order eNME model. If there
are one or more models displaying in the right-top list box, the search is
successful. If there is no model displaying in the right-top list box, the
search is failed. Third, selecting a low-order eNME
model in the right-top list box. The parametric description of the selected
low-order eNME model is then displayed in the
right-bottom list box. Fourth, clicking Add button to add the low-order eNME model into the simulation data. Fifth, the successful
computation higher-order eME model can be obtained while the simulate button
is clicked.
Figure 10 - Interface for the search of low-order eNME models
1.7 Computation of Higher-order eNME models
PyEpiSIM uses two
computational strategies to calculate higher-order eNME
models.
The strategy of
not using heritability but only using prevalence, is shown in Figure11.
s (1.7.1_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.7.1_2, Figure 11). The calculation
of higher-order eME models involves four steps.
Firstly, set the parameters of the higher-order eNME
model, including Choosing the order, The proportion of cases, Heritability
of the Model(Can only be 0), and MAFs of the Model. Secondly, click the Count
button to search for higher-order eNME models. If one
or more penetrance values are calculated in the list box in the upper right
corner, the calculation is successful. If no value is displayed in the list box
in the upper right corner, the search fails. Thirdly, click the Add
button to add the explicit rate value to the simulation data. Fourth, the
successful computation higher-order eME model can be obtained while the simulate button
is clicked.
Figure 11 - Interface of the first strategy for the computation of
higher-order eNME models
The strategy of
calculating both heritability and prevalence is shown in Figure 12.
s (1.7.2_1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.7.2_2, Figure 12). The calculation
of higher-order eME models involves four steps.
Firstly, set the parameters of the higher-order eNME
model, including Choosing the order, The proportion of cases, Heritability
of the Model, and MAFs of the Model. Secondly, click the Count
button to search for higher-order eNME models. If one
or more penetrance values are calculated in the list box in the upper right
corner, the calculation is successful. If no value is displayed in the list box
in the upper right corner, the search fails. Thirdly, click the Add
button to add the explicit rate value to the simulation data. Fourth, the
successful computation higher-order eME model can be obtained while the simulate button
is clicked.
Figure 12 - Interface of the second strategy for the computation
of higher-order eNME models
1.8 Embedding multiple
epistasis models
s (1.8.1, Figure 1). This step is the
same as the one (1.3.1, Figure 1).
s (1.8.2, Figure 2). This step is the
same as the one (1.1.2, Figure 2).
s (1.8.3, Figure 3). This step is the
same as the one (1.1.3, Figure 3).
s (1.8.4). Embedding epistasis models
into the simulation data until satisfying the number of the parameter Models.
There are four strategies for embedding epistasis models.
(1) This step is
the same as the one computation of existing public models: (1.3.2, Figure 5).
(2) This step is
the same as the one computation of low-order eME
models: (1.4.1_2, Figure 6).
(3) This step is
the same as the one computation of low-order eME
models: (1.4.2_2, Figure 7).
(4) This step is
the same as the one computation of higher-order eME
models: (1.5.1_2, Figure 8).
(5) This step is
the same as the one computation of higher-order eME
models: (1.5.2_2, Figure 9).
(6) This step is
the same as the one search of low-order eNME models:
(1.6.2, Figure 10).
(7) This step is
the same as the one computation of higher-order eNME models: (1.7.1_2, Figure 11).
(8) This step is
the same as the one computation of
higher-order eNME models: (1.7.2_2, Figure 12).
s Clicking simulate button for embedding
multiple epistasis models.