Modern simulation utilities for genetic analysis

• Main Authors: German, C.A (Author), Ji, S.S (Author), Lange, K. (Author), Sinsheimer, J.S (Author), Sobel, E.M (Author), Zhou, H. (Author), Zhou, J. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: adult; aged; Aged; article; calculation; Cloud based computing; cloud computing; Cloud Computing; Computational efficiency; computer language; computer simulation; Computer Simulation; Efficiency; Generalized linear mixed models; Generalized linear model; genetic analysis; Genetic architecture; genetic model; Genetic programming; genetic screening; Genetic Testing; geneticist; human; Humans; Integrated simulations; memory; Open source software; pedigree; Pedigree; phenotype; Phenotype; pipeline; Pipelines; Power; Realistic genetic models; Reproducible research; Scientific programming; simulation; Statistical genetics; Step-by-step instructions; Trait simulation; velocity
• Online Access: View Fulltext in Publisher

 Background: Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools. Results: We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research. Conclusion: The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses. © 2021, The Author(s).