Reproducible simulations of realistic samples for next-generation sequencing studies using variant simulation tools

Bo Peng

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.

Original languageEnglish (US)
Pages (from-to)45-52
Number of pages8
JournalGenetic epidemiology
Volume39
Issue number1
DOIs
StatePublished - Jan 1 2015

Keywords

  • Genetic variants
  • Next-gen sequencing
  • Rare variant association analysis
  • Reproducible study
  • Simulation

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

MD Anderson CCSG core facilities

  • Bioinformatics Shared Resource

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