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GenRisk: a tool for comprehensive genetic risk modeling.

Rana Aldisi1, Emadeldin Hassanin1, Sugirthan Sivalingam1,2,3

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Summary

GenRisk integrates rare deleterious variant burden and common variant polygenic risk scores for complex trait analysis. This Python package enables association tests and phenotype prediction models, improving genetic architecture understanding.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex traits are influenced by common regulatory variants and rare coding variants.
  • Current analyses often treat these genetic contributions separately.
  • Integrating both variant types can provide a more comprehensive view of genetic architecture.

Purpose of the Study:

  • Introduce GenRisk, a Python package for genetic analysis.
  • Enable computation and integration of rare variant burden scores and polygenic risk scores.
  • Facilitate association testing and phenotype prediction models.

Main Methods:

  • Developed GenRisk, a Python package compatible with VCF files.
  • Implemented methods for calculating rare variant burden scores.
  • Integrated polygenic risk scores from common variants.
  • Included functionalities for association tests and machine learning models for phenotype prediction.

Main Results:

  • GenRisk provides a unified framework for analyzing diverse genetic contributions.
  • The package supports VCF input for seamless data integration.
  • Enables robust association testing and phenotype prediction.

Conclusions:

  • GenRisk offers a novel approach to integrate common and rare genetic variants.
  • The package enhances the study of complex trait genetic architecture.
  • Provides a versatile tool for genetic association and prediction studies.