simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes

Samuel B Fernandes, Alexander E Lipka

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models.

RESULTS: We illustrate simplePHENOTYPES' ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities.

CONCLUSIONS: simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at .

Original languageEnglish (US)
Article number491
Pages (from-to)491
JournalBMC bioinformatics
Issue number1
StatePublished - Dec 1 2020


  • Computer simulation
  • Correlated traits
  • Multivariate data
  • Quantitative genetics

ASJC Scopus subject areas

  • Applied Mathematics
  • Molecular Biology
  • Structural Biology
  • Biochemistry
  • Computer Science Applications


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