Imperial College London
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ProfessorJamesWare

Faculty of Medicine, National Heart & Lung Institute

Professor of Cardiovascular and Genomic Medicine
 
 
 
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Contact

 

j.ware Website

 
 
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Assistant

 

Ms Lisa Quinn +44 (0)20 7594 1345

 
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Location

 

3 13GLMS BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cummings:2020:10.1038/s41586-020-2329-2,
author = {Cummings, BB and Karczewski, KJ and Kosmicki, JA and Seaby, EG and Watts, NA and Singer-Berk, M and Mudge, JM and Karjalainen, J and Satterstrom, FK and O'Donnell-Luria, AH and Poterba, T and Seed, C and Solomonson, M and Alföldi, J and Genome, Aggregation Database Production Team and Genome, Aggregation Database Consortium and Daly, MJ and MacArthur, DG},
doi = {10.1038/s41586-020-2329-2},
journal = {Nature},
pages = {452--458},
title = {Transcript expression-aware annotation improves rare variant interpretation.},
url = {http://dx.doi.org/10.1038/s41586-020-2329-2},
volume = {581},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and wil
AU  - Cummings,BB
AU  - Karczewski,KJ
AU  - Kosmicki,JA
AU  - Seaby,EG
AU  - Watts,NA
AU  - Singer-Berk,M
AU  - Mudge,JM
AU  - Karjalainen,J
AU  - Satterstrom,FK
AU  - O'Donnell-Luria,AH
AU  - Poterba,T
AU  - Seed,C
AU  - Solomonson,M
AU  - Alföldi,J
AU  - Genome,Aggregation Database Production Team
AU  - Genome,Aggregation Database Consortium
AU  - Daly,MJ
AU  - MacArthur,DG
DO  - 10.1038/s41586-020-2329-2
EP  - 458
PY  - 2020///
SN  - 0028-0836
SP  - 452
TI  - Transcript expression-aware annotation improves rare variant interpretation.
T2  - Nature
UR  - http://dx.doi.org/10.1038/s41586-020-2329-2
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32461655
UR  - https://www.nature.com/articles/s41586-020-2329-2
UR  - http://hdl.handle.net/10044/1/80027
VL  - 581
ER  -
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