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A novel PS4 criterion approach based on symptoms of rare diseases and in-house frequency data in a Bayesian framework

You Kyung Cho, Dhong-gun Won, Changwon Keum, Beom Hee Lee, Go Hun Seo, Byung-Chul Lee
doi: https://doi.org/10.1101/2020.07.22.215426
You Kyung Cho
13billion Inc., Seoul, South Korea
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Dhong-gun Won
13billion Inc., Seoul, South Korea
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Changwon Keum
13billion Inc., Seoul, South Korea
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Beom Hee Lee
2Medical Genetics Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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Go Hun Seo
13billion Inc., Seoul, South Korea
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Byung-Chul Lee
13billion Inc., Seoul, South Korea
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  • For correspondence: bclee@3billion.io
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Abstract

The American College of Medical Genetics (ACMG) and Genomics/Association for Molecular Pathology (AMP) previously reported standardized guidance for the assessment of genetic variants. One of the criteria regarding the prevalence in a case-control study, PS4, is important due to its evidence of pathogenicity. Despite recent studies approaching gene- and disease-specific probands, interpretation of a variant to PS4 still has certain limitations for rare variants. Here, we suggest a generalized method, Bayesian odds ratio (BayesianOR), applicable to PS4 via decomposing a disease to its symptoms and applying a Bayesian framework. Using this approach, we demonstrate reproducibility of the calculation of the original odds ratio from well-studied epilepsy data and verify the applicability to in-house frequencies for various rare diseases. In addition, BayesianOR showed a significant difference in tendency with different ClinVar pathogenicity, using in-house data. Thus, the novel method described here should provide an improved interpretation of sequence variants. Furthermore, we anticipate that it will enhance the diagnosis of patients with rare diseases.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 24, 2020.
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A novel PS4 criterion approach based on symptoms of rare diseases and in-house frequency data in a Bayesian framework
You Kyung Cho, Dhong-gun Won, Changwon Keum, Beom Hee Lee, Go Hun Seo, Byung-Chul Lee
bioRxiv 2020.07.22.215426; doi: https://doi.org/10.1101/2020.07.22.215426
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A novel PS4 criterion approach based on symptoms of rare diseases and in-house frequency data in a Bayesian framework
You Kyung Cho, Dhong-gun Won, Changwon Keum, Beom Hee Lee, Go Hun Seo, Byung-Chul Lee
bioRxiv 2020.07.22.215426; doi: https://doi.org/10.1101/2020.07.22.215426

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