Abstract
Motivation 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 compare different implementations of these analyses. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R 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 a vignette illustrating how to simulate a set of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the numerical equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities.
Conclusions simplePHENOTYPES is a 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 https://github.coin/sainuelbfernandes/siinplePHENOTYPES.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The implementation is better described, as well as new benchmarking has been included.
