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STAAR workflow: a cloud-based workflow for scalable and reproducible rare variant analysis.

Sheila M Gaynor1, Kenneth E Westerman2,3,4, Lea L Ackovic5

  • 1Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.

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Summary
This summary is machine-generated.

We developed a workflow for analyzing rare variants in whole genome sequencing data. This tool enhances rare variant analysis power by incorporating functional annotations and is optimized for cloud platforms.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole genome sequencing (WGS) studies generate vast amounts of data, posing challenges for rare variant analysis.
  • Identifying the contribution of rare variants to complex diseases requires robust and efficient analytical tools.

Purpose of the Study:

  • To introduce a novel workflow description language (WDL) workflow for rare variant association testing.
  • To facilitate the integration of functional annotation information to improve the power of rare variant analyses.
  • To provide a user-friendly and cloud-optimized tool for WGS data analysis.

Main Methods:

  • Development of the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow using WDL.
  • Implementation of gene-centric and genetic region approaches for rare variant testing.
  • Incorporation of functional annotations to enhance analytical power.
  • Optimization for cloud-based platforms like BioData Catalyst Powered by Terra.

Main Results:

  • The STAAR workflow enables genome-wide, candidate, and conditional rare variant analyses.
  • The workflow effectively integrates functional annotations to boost analytical power.
  • It is optimized for efficient implementation on cloud computing environments.

Conclusions:

  • The STAAR WDL workflow provides a powerful and accessible tool for rare variant analysis in WGS studies.
  • This workflow can be seamlessly integrated into comprehensive WGS analysis pipelines.
  • The tool enhances the ability to detect associations involving rare variants by leveraging functional information.