simulate_phenotype {martini} | R Documentation

## Simulate phenotype

### Description

Simulates a phenotype from a GWAS experiment and a specified set of causal SNPs. If the data is qualitative, only controls are used.

### Usage

    simulate_phenotype(
      gwas,
      snps,
      h2,
      model = "additive",
      effectSize = rnorm(length(snps)),
      qualitative = FALSE,
      ncases,
      ncontrols,
      prevalence
    )

### Arguments

`gwas` | A SnpMatrix object with the GWAS information.
`snps` | Character vector with the SNP ids of the causal SNPs. Must match SNPs in gwas[["map"]][["snp.names"]].
`h2` | Heritability of the phenotype (between 0 and 1).
`model` | String specifying the genetic model under the phenotype. Accepted values: "additive".
`effectSize` | Numeric vector with the same lenght as the number of causal SNPs. It indicates the effect size of each of the SNPs; if absent, they are sampled fron a normal distribution.
`qualitative` | Bool indicating if the phenotype is qualitative or not (quantitative).
`ncases` | Integer specifying the number of cases to simulate in a qualitative phenotype. Required if qualitative = TRUE.
`ncontrols` | Integer specifying the number of controls to simulate in a qualitative phenotype. Required if qualitative = TRUE.
`prevalence` | Value between 0 and 1 specifying the population prevalence of the disease. Note that ncases cannot be greater than prevalence * number of samples. Required if qualitative = TRUE.

### Value

A copy of the GWAS experiment with the new phenotypes in `gwas[["fam"]][["affected"]]`.

### References

Inspired from GCTA simulation tool: http://cnsgenomics.com/software/gcta/Simu.html.

### Examples

    gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
    causal <- simulate_causal_snps(gi, ngenes = 2)
    simulate_phenotype(minigwas, causal, h2 = 1)

[Package martini version 1.10.0 Index]