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Related Experiment Video

Updated: Dec 20, 2025

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

Transcript expression-aware annotation improves rare variant interpretation.

Beryl B Cummings^1,2,3, Konrad J Karczewski^1,2, Jack A Kosmicki^1,2,4

¹Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

|May 29, 2020

Summary

This summary is machine-generated.

Interpreting rare genetic variants is challenging. A new method using exon expression levels improves the accuracy of identifying disease-causing variants, especially in dosage-sensitive genes.

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

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Published on: April 4, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Accelerated DNA sequencing generates vast human genetic variation data.
Interpreting rare genetic variants, particularly disruptive ones in dosage-sensitive genes, poses a significant challenge.
Alternative splicing of mRNA leads to variable exon expression across cell types, complicating variant interpretation.

Purpose of the Study:

To develop a novel transcript-level annotation metric to quantify isoform expression for variants.
To assess the utility of this metric in differentiating functional exon importance.
To improve the filtering of falsely annotated putative loss-of-function (pLoF) variants in disease genes.

Main Methods:

Manual curation of pLoF variants in haploinsufficient disease genes within the Genome Aggregation Database (gnomAD).
Development and calculation of the 'proportion expressed across transcripts' metric using Genotype Tissue Expression (GTEx) project data.
Application of the expression filter to analyze de novo variants in autism spectrum disorder and developmental disorder cohorts.

Main Results:

The 'proportion expressed across transcripts' metric differentiates between weakly and highly evolutionarily conserved exons.
Expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants in haploinsufficient disease genes, with minimal removal of pathogenic variants.
pLoF variants in highly expressed exons are significantly enriched in cases of autism spectrum disorder and developmental disorders.

Conclusions:

The developed annotation metric provides a valuable tool for interpreting variants by incorporating isoform expression data.
This approach enhances the genetic diagnosis of rare diseases and the analysis of rare variant burden in complex disorders.
The annotation is fast, flexible, and generalizable, aiding variant curation and prioritization in various genetic studies.