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Principles of Pharmacogenetics: Types of Genetic Variants

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Real-world evaluation of deep learning algorithms to classify functional pathogenic germline variants.

Ryan D Chow¹, Ravi B Parikh^2,3,4,5, Katherine L Nathanson^6,7

¹Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Medrxiv : the Preprint Server for Health Sciences

|April 18, 2024

Summary

Deep learning models show promise in predicting hereditary breast cancer risk for BRCA1, BRCA2, and PALB2 variants. However, their clinical utility for variants of uncertain significance remains limited.

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Area of Science:

Genomics
Computational Biology
Oncology

Background:

Deep learning models for variant pathogenicity prediction are effective on curated data but underexplored in real-world disease phenotypes.
Assessing the clinical utility of these models for hereditary cancer risk prediction is crucial.

Approach:

Applied three state-of-the-art deep learning pathogenicity prediction models to classify hereditary breast cancer gene variants.
Utilized data from the UK Biobank for real-world phenotype association.
Explored gene-specific score thresholds to optimize model performance.

Key Points:

Predicted pathogenic variants in BRCA1, BRCA2, and PALB2 were associated with increased breast cancer risk.
No significant association was found for ATM and CHEK2 variants.
Optimizing gene-specific score thresholds improved model performance.

Conclusions:

Deep learning models show potential for identifying pathogenic variants in key breast cancer genes.
The models demonstrated limited clinical utility for classifying variants of uncertain significance.
Further refinement is needed to enhance the real-world clinical application of these predictive tools.