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Updated: Apr 19, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

False discovery rates for rare variants from sequenced data.

Marinela Capanu¹, Venkatraman E Seshan

¹Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States of America.

Genetic Epidemiology

|January 6, 2015

Summary

This summary is machine-generated.

Detecting rare deleterious variants is difficult due to limited evidence. Accurate false discovery control requires large variant risk and precise hierarchical models, according to simulation studies in statistical genetics.

Keywords:

false discovery rate hierarchical model local false discovery rate rare variants

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Area of Science:

Statistical genetics
Genomics
Computational biology

Background:

Identifying rare deleterious genetic variants is a major challenge in statistical genetics.
Sparse evidence for individual variants complicates distinguishing disease-driving mutations from neutral ones.
Hierarchical modeling and Bayesian model uncertainty show promise for pinpointing rare variants associated with disease.

Purpose of the Study:

To evaluate the false discovery properties of hierarchical modeling and Bayesian model uncertainty in detecting rare deleterious variants.
To understand the conditions under which accurate false discovery control can be achieved in this context.

Main Methods:

Utilized simulation studies to assess the performance of statistical genetics techniques.
Focused on hierarchical modeling and Bayesian model uncertainty.
Analyzed the impact of variant risk magnitude and hierarchical accuracy on false discovery rates.

Main Results:

Accurate false discovery control is not consistently achieved with current methods.
Effective control requires variants with substantial risk.
High accuracy in hierarchical characteristics is crucial for distinguishing deleterious from neutral variants.

Conclusions:

Current frameworks for detecting rare deleterious variants face challenges in false discovery control.
Future research should focus on improving hierarchical model accuracy and considering variant risk.
Optimizing these methods is essential for advancing genetic association studies and understanding disease.