# Functions to generate phenotype data. 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. ```r 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 $\phi$ for the dependence between the two generated phenotypes. ## Returns A dataframe containing n observations of the phenotype Y or phenotypes $Y1$, $Y2$ and of the covariates $X1$, $X2$. ## 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 $\tau$ 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. ## Examples ```r # 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) ```

generate_phenodata function