generate_phenodata: Functions to generate phenotype data.
In CJAMP: Copula-Based Joint Analysis of Multiple Phenotypes
Description
Usage
Arguments
Details
Value
Examples
Description
Functions to generate standard normal or binary phenotypes based on provided genetic
data, for specified effect sizes.
The functions generate_phenodata_1_simple and
generate_phenodata_1 generate one phenotype Y conditional on
single nucleotide variants (SNVs) and two covariates.
generate_phenodata_2_bvn as well as generate_phenodata_2_copula
generate two phenotypes Y1, Y2 with dependence Kendall's tau conditional on
the provided SNVs and two covariates.
Usage
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generate_phenodata_1_simple(genodata = NULL, type = "quantitative",
b = 0, a = c(0, 0.5, 0.5))
generate_phenodata_1(genodata = NULL, type = "quantitative", b = 0.6,
a = c(0, 0.5, 0.5), MAF_cutoff = 1, prop_causal = 0.1,
direction = "a")
generate_phenodata_2_bvn(genodata = NULL, tau = NULL, b1 = 0,
b2 = 0, a1 = c(0, 0.5, 0.5), a2 = c(0, 0.5, 0.5))
generate_phenodata_2_copula(genodata = NULL, phi = NULL, tau = 0.5,
b1 = 0.6, b2 = 0.6, a1 = c(0, 0.5, 0.5), a2 = c(0, 0.5, 0.5),
MAF_cutoff = 1, prop_causal = 0.1, direction = "a")
Arguments
genodata
Numeric input vector or dataframe containing the genetic
variant(s) in columns. Must be in allelic coding 0, 1, 2.
type
String with value "quantitative" or "binary"
specifying whether normally-distributed or binary phenotypes
are generated.
b
Integer or vector specifying the genetic effect size(s) of
the provided SNVs (genodata) in the data generation.
a
Numeric vector specifying the effect sizes of the covariates X1, X2
in the data generation.
MAF_cutoff
Integer specifying a minor allele frequency cutoff to
determine among which SNVs the causal SNVs are
sampled for the phenotype generation.
prop_causal
Integer specifying the desired percentage of causal SNVs
among all SNVs.
direction
String with value "a", "b", or "c"
specifying whether all causal SNVs have a positive effect on
the phenotypes ("a"), 20% of the causal SNVs have a
negative effect and 80% a positive effect on the phenotypes
("b"), or 50% of the causal SNVs have a negative
effect and 50% a positive effect on the phenotypes ("c").
tau
Integer specifying Kendall's tau, which determines the
dependence between the two generated phenotypes.
b1
Integer or vector specifying the genetic effect size(s) of
the provided SNVs (genodata) on the first phenotype
in the data generation.
b2
Integer or vector specifying the genetic effect size(s) of
the provided SNVs (genodata) on the second phenotype
in the data generation.
a1
Numeric vector specifying the effect sizes of the covariates X1, X2
on the first phenotype in the data generation.
a2
Numeric vector specifying the effect sizes of the covariates X1, X2
on the second phenotype in the data generation.
phi
Integer specifying the parameter φ for
the dependence between the two generated phenotypes.
Details
In more detail, the function generate_phenodata_1_simple
generates a quantitative or binary phenotype Y with n observations,
conditional on the specified SNVs with given effect sizes and conditional
on one binary and one standard normally-distributed covariate with
specified effect sizes. n is given through the provided SNVs.
generate_phenodata_1 provides an extension of
generate_phenodata_1_simple and allows to further select
the percentage of causal SNVs, a minor allele frequency cutoff on the
causal SNVs, and varying effect directions. n is given through the
provided SNVs.
The function generate_phenodata_2_bvn generates
two quantitative phenotypes Y1, Y2 conditional on one binary and one
standard normally-distributed covariate X1, X2 from the bivariate
normal distribution so that they have have dependence τ given
by Kendall's tau.
The function generate_phenodata_2_copula generates
two quantitative phenotypes Y1, Y2 conditional on one binary and one
standard normally-distributed covariate X1, X2 from the Clayton copula
so that Y1, Y2 are marginally normally distributed and have dependence
Kendall's tau specified by tau or phi, using the function
generate_clayton_copula.
The genetic effect sizes are the specified numeric values b and
b1, b2, respectively, in the functions generate_phenodata_1_simple
and generate_phenodata_2_bvn. In
generate_phenodata_1 and generate_phenodata_2_copula,
the genetic effect sizes are computed by multiplying b or b1, b2,
respectively, with the absolute value of the log10-transformed
minor allele frequencies, so that rarer variants have larger effect sizes.
Value
A dataframe containing n observations of the phenotype Y or phenotypes
Y1, Y2 and of the covariates X1, X2.
Examples
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# Generate genetic data:
set.seed(10)
genodata <- generate_genodata(n_SNV = 20, n_ind = 1000)
compute_MAF(genodata)
# Generate different phenotype data:
phenodata1 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "quantitative", b = 0)
phenodata2 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "quantitative", b = 2)
phenodata3 <- generate_phenodata_1_simple(genodata = genodata,
type = "quantitative", b = 2)
phenodata4 <- generate_phenodata_1_simple(genodata = genodata,
type = "quantitative",
b = seq(0.1, 2, 0.1))
phenodata5 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "binary", b = 0)
phenodata6 <- generate_phenodata_1(genodata = genodata[,1],
type = "quantitative", b = 0,
MAF_cutoff = 1, prop_causal = 0.1,
direction = "a")
phenodata7 <- generate_phenodata_1(genodata = genodata,
type = "quantitative", b = 0.6,
MAF_cutoff = 0.1, prop_causal = 0.05,
direction = "a")
phenodata8 <- generate_phenodata_1(genodata = genodata,
type = "quantitative",
b = seq(0.1, 2, 0.1),
MAF_cutoff = 0.1, prop_causal = 0.05,
direction = "a")
phenodata9 <- generate_phenodata_2_bvn(genodata = genodata[,1],
tau = 0.5, b1 = 0, b2 = 0)
phenodata10 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 0, b2 = 0)
phenodata11 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 1,
b2 = seq(0.1,2,0.1))
phenodata12 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 1, b2 = 2)
par(mfrow = c(3, 1))
hist(phenodata12$Y1)
hist(phenodata12$Y2)
plot(phenodata12$Y1, phenodata12$Y2)
phenodata13 <- generate_phenodata_2_copula(genodata = genodata[,1],
MAF_cutoff = 1, prop_causal = 1,
tau = 0.5, b1 = 0, b2 = 0)
phenodata14 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.5, b1 = 0, b2 = 0)
phenodata15 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.5, b1 = 0, b2 = 0)
phenodata16 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.2, b1 = 0.3,
b2 = seq(0.1, 2, 0.1))
phenodata17 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.2, b1 = 0.3, b2 = 0.3)
par(mfrow = c(3, 1))
hist(phenodata17$Y1)
hist(phenodata17$Y2)
plot(phenodata17$Y1, phenodata17$Y2)
CJAMP documentation built on May 1, 2019, 9:15 p.m.
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Description Usage Arguments Details Value Examples
Description
Functions to generate standard normal or binary phenotypes based on provided genetic
data, for specified effect sizes.
The functions generate_phenodata_1_simple and
generate_phenodata_1 generate one phenotype Y conditional on
single nucleotide variants (SNVs) and two covariates.
generate_phenodata_2_bvn as well as generate_phenodata_2_copula
generate two phenotypes Y1, Y2 with dependence Kendall's tau conditional on
the provided SNVs and two covariates.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 | generate_phenodata_1_simple(genodata = NULL, type = "quantitative",
b = 0, a = c(0, 0.5, 0.5))
generate_phenodata_1(genodata = NULL, type = "quantitative", b = 0.6,
a = c(0, 0.5, 0.5), MAF_cutoff = 1, prop_causal = 0.1,
direction = "a")
generate_phenodata_2_bvn(genodata = NULL, tau = NULL, b1 = 0,
b2 = 0, a1 = c(0, 0.5, 0.5), a2 = c(0, 0.5, 0.5))
generate_phenodata_2_copula(genodata = NULL, phi = NULL, tau = 0.5,
b1 = 0.6, b2 = 0.6, a1 = c(0, 0.5, 0.5), a2 = c(0, 0.5, 0.5),
MAF_cutoff = 1, prop_causal = 0.1, direction = "a")
|
Arguments
genodata |
Numeric input vector or dataframe containing the genetic variant(s) in columns. Must be in allelic coding 0, 1, 2. |
type |
String with value |
b |
Integer or vector specifying the genetic effect size(s) of
the provided SNVs ( |
a |
Numeric vector specifying the effect sizes of the covariates X1, X2 in the data generation. |
MAF_cutoff |
Integer specifying a minor allele frequency cutoff to determine among which SNVs the causal SNVs are sampled for the phenotype generation. |
prop_causal |
Integer specifying the desired percentage of causal SNVs among all SNVs. |
direction |
String with value |
tau |
Integer specifying Kendall's tau, which determines the dependence between the two generated phenotypes. |
b1 |
Integer or vector specifying the genetic effect size(s) of
the provided SNVs ( |
b2 |
Integer or vector specifying the genetic effect size(s) of
the provided SNVs ( |
a1 |
Numeric vector specifying the effect sizes of the covariates X1, X2 on the first phenotype in the data generation. |
a2 |
Numeric vector specifying the effect sizes of the covariates X1, X2 on the second phenotype in the data generation. |
phi |
Integer specifying the parameter φ for the dependence between the two generated phenotypes. |
Details
In more detail, the function generate_phenodata_1_simple
generates a quantitative or binary phenotype Y with n observations,
conditional on the specified SNVs with given effect sizes and conditional
on one binary and one standard normally-distributed covariate with
specified effect sizes. n is given through the provided SNVs.
generate_phenodata_1 provides an extension of
generate_phenodata_1_simple and allows to further select
the percentage of causal SNVs, a minor allele frequency cutoff on the
causal SNVs, and varying effect directions. n is given through the
provided SNVs.
The function generate_phenodata_2_bvn generates
two quantitative phenotypes Y1, Y2 conditional on one binary and one
standard normally-distributed covariate X1, X2 from the bivariate
normal distribution so that they have have dependence τ given
by Kendall's tau.
The function generate_phenodata_2_copula generates
two quantitative phenotypes Y1, Y2 conditional on one binary and one
standard normally-distributed covariate X1, X2 from the Clayton copula
so that Y1, Y2 are marginally normally distributed and have dependence
Kendall's tau specified by tau or phi, using the function
generate_clayton_copula.
The genetic effect sizes are the specified numeric values b and
b1, b2, respectively, in the functions generate_phenodata_1_simple
and generate_phenodata_2_bvn. In
generate_phenodata_1 and generate_phenodata_2_copula,
the genetic effect sizes are computed by multiplying b or b1, b2,
respectively, with the absolute value of the log10-transformed
minor allele frequencies, so that rarer variants have larger effect sizes.
Value
A dataframe containing n observations of the phenotype Y or phenotypes Y1, Y2 and of the covariates X1, X2.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # Generate genetic data:
set.seed(10)
genodata <- generate_genodata(n_SNV = 20, n_ind = 1000)
compute_MAF(genodata)
# Generate different phenotype data:
phenodata1 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "quantitative", b = 0)
phenodata2 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "quantitative", b = 2)
phenodata3 <- generate_phenodata_1_simple(genodata = genodata,
type = "quantitative", b = 2)
phenodata4 <- generate_phenodata_1_simple(genodata = genodata,
type = "quantitative",
b = seq(0.1, 2, 0.1))
phenodata5 <- generate_phenodata_1_simple(genodata = genodata[,1],
type = "binary", b = 0)
phenodata6 <- generate_phenodata_1(genodata = genodata[,1],
type = "quantitative", b = 0,
MAF_cutoff = 1, prop_causal = 0.1,
direction = "a")
phenodata7 <- generate_phenodata_1(genodata = genodata,
type = "quantitative", b = 0.6,
MAF_cutoff = 0.1, prop_causal = 0.05,
direction = "a")
phenodata8 <- generate_phenodata_1(genodata = genodata,
type = "quantitative",
b = seq(0.1, 2, 0.1),
MAF_cutoff = 0.1, prop_causal = 0.05,
direction = "a")
phenodata9 <- generate_phenodata_2_bvn(genodata = genodata[,1],
tau = 0.5, b1 = 0, b2 = 0)
phenodata10 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 0, b2 = 0)
phenodata11 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 1,
b2 = seq(0.1,2,0.1))
phenodata12 <- generate_phenodata_2_bvn(genodata = genodata,
tau = 0.5, b1 = 1, b2 = 2)
par(mfrow = c(3, 1))
hist(phenodata12$Y1)
hist(phenodata12$Y2)
plot(phenodata12$Y1, phenodata12$Y2)
phenodata13 <- generate_phenodata_2_copula(genodata = genodata[,1],
MAF_cutoff = 1, prop_causal = 1,
tau = 0.5, b1 = 0, b2 = 0)
phenodata14 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.5, b1 = 0, b2 = 0)
phenodata15 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.5, b1 = 0, b2 = 0)
phenodata16 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.2, b1 = 0.3,
b2 = seq(0.1, 2, 0.1))
phenodata17 <- generate_phenodata_2_copula(genodata = genodata,
MAF_cutoff = 1, prop_causal = 0.5,
tau = 0.2, b1 = 0.3, b2 = 0.3)
par(mfrow = c(3, 1))
hist(phenodata17$Y1)
hist(phenodata17$Y2)
plot(phenodata17$Y1, phenodata17$Y2)
|
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