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Benchmark of computational methods to detect digenism in sequencing data.

Marie-Sophie C Ogloblinsky1, Donald F Conrad2, Anaïs Baudot3

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Digenic inheritance, involving two genes, can cause rare diseases. This study benchmarks methods for detecting it in sequencing data, finding DiGePred, ARBOCK, and DIEP offer different strengths for identifying digenic disease patterns.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Digenic inheritance, where two gene alterations cause disease, is a key factor in many undiagnosed rare genetic disorders.
  • Next-generation sequencing (NGS) facilitates digenic inheritance detection, but challenges remain due to the lack of a gold standard method.
  • Identifying digenic inheritance patterns is crucial for diagnosing rare diseases and understanding complex genetic etiologies.

Purpose of the Study:

  • To provide a comprehensive overview and classification of methods for detecting digenic inheritance in sequencing data.
  • To benchmark the performance of different digenic inheritance detection methods, focusing on rare and heterogeneous diseases.
  • To guide researchers and clinicians in selecting appropriate methodologies for digenic inheritance analysis.

Main Methods:

  • Classification of digenic inheritance detection methods into cohort-based and individual-based approaches.
  • Evaluation of methods using real-life scenarios with known digenic and neutral gene pairs.
  • Benchmarking focused on individual-based methods for applicability to rare diseases.

Main Results:

  • DiGePred demonstrated the lowest false positive rate, indicating high specificity.
  • ARBOCK identified the highest number of true positives, suggesting superior sensitivity.
  • DIEP offered the best overall balance between true and false positives, providing robust performance.

Conclusions:

  • The benchmark provides valuable insights into the practical utility and performance of digenic inheritance detection methods.
  • Individual-based methods are particularly suitable for rare diseases, especially when phenotypic data is limited.
  • This work serves as a critical resource for selecting effective tools for digenic inheritance analysis in diverse genetic disorders.