Single-cell data combined with phenotypes improves variant interpretation

Phenotype-specific models that integrate gene expression data with CADD scores show great promise in improving variant prioritisation. In addition to improving diagnostic accuracy, these models offer insights into the underlying biological mechanisms of rare diseases. Enriching existing pathogenicity-related scores with gene expression datasets has the potential to advance personalised medicine through more accurate and interpretable variant prioritisation.

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Publication Details

Journal
BMC Genomics
Published
2025-05-28
DOI
https://doi.org/10.1186/s12864-025-11711-w
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.81

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article

Single-cell data combined with phenotypes improves variant interpretation

Timo Lassmann, Timothy R. Chapman
1 citations
BMC Genomics
Single-cell and spatial transcriptomics
0.81
article

Single-cell data combined with phenotypes improves variant interpretation

Timo Lassmann, Timothy R. Chapman
article en
1 citations

Abstract

Phenotype-specific models that integrate gene expression data with CADD scores show great promise in improving variant prioritisation. In addition to improving diagnostic accuracy, these models offer insights into the underlying biological mechanisms of rare diseases. Enriching existing pathogenicity-related scores with gene expression datasets has the potential to advance personalised medicine through more accurate and interpretable variant prioritisation.

BMC GenomicsVol. 26(1)
The University of Western Australia (AU), Keogh Institute for Medical Research (AU)
Australian Government, Stan Perron Charitable Foundation
Openalex Percentile: Top 28%
Single-cell and spatial transcriptomics
0.81
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