The University of Southampton
University of Southampton Institutional Repository

Targeting de novo loss of function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project

Targeting de novo loss of function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project
Targeting de novo loss of function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project
Whole genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP); however, data analysis was time and resource intensive with 3 million variants found per patient. Consequently, analysis was restricted to predefined gene panels associated with the patient’s phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnostic variants outside of the panel(s) applied. We propose a complementary method to rapidly identify diagnostic variants, including those missed by 100KGP methods.
The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, single nucleotide loss-of-function variants in OMIM disease genes with a LOEUF score <0.2. We conducted our analysis prospectively in 2019 and compared our findings with the corresponding diagnostic reports as returned in 2019 and again in 2021.
324/336 (96%) of the variants identified through DeNovoLOEUF were classified as diagnostic or partially diagnostic. We identified 39 diagnoses that were “missed” by 100KGP standard analyses, which are now being returned to patients. We have demonstrated a highly specific and rapid method with a 96% positive predictive value that has good concordance with standard analysis, low false positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.
Seaby, Eleanor G.
ec948f42-007c-4bd8-9dff-bb86278bf03f
Thomas, N. Simon
6aac3e46-688d-4ad8-81ed-b7a667f4f20d
Webb, Amy
eb493497-92af-4a28-86d5-05b323678235
Brittain, Helen
19411fbb-588d-49c3-93e8-f2926c8541e3
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Rehm, Heidi L.
0e4087b7-519d-4b95-84ac-412d1c0173d1
O’Donnell-Luria, Anne
2c660818-57d6-4eb4-9e70-908bdbefadb3
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Seaby, Eleanor G.
ec948f42-007c-4bd8-9dff-bb86278bf03f
Thomas, N. Simon
6aac3e46-688d-4ad8-81ed-b7a667f4f20d
Webb, Amy
eb493497-92af-4a28-86d5-05b323678235
Brittain, Helen
19411fbb-588d-49c3-93e8-f2926c8541e3
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Rehm, Heidi L.
0e4087b7-519d-4b95-84ac-412d1c0173d1
O’Donnell-Luria, Anne
2c660818-57d6-4eb4-9e70-908bdbefadb3
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Whole genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP); however, data analysis was time and resource intensive with 3 million variants found per patient. Consequently, analysis was restricted to predefined gene panels associated with the patient’s phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnostic variants outside of the panel(s) applied. We propose a complementary method to rapidly identify diagnostic variants, including those missed by 100KGP methods.
The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, single nucleotide loss-of-function variants in OMIM disease genes with a LOEUF score <0.2. We conducted our analysis prospectively in 2019 and compared our findings with the corresponding diagnostic reports as returned in 2019 and again in 2021.
324/336 (96%) of the variants identified through DeNovoLOEUF were classified as diagnostic or partially diagnostic. We identified 39 diagnoses that were “missed” by 100KGP standard analyses, which are now being returned to patients. We have demonstrated a highly specific and rapid method with a 96% positive predictive value that has good concordance with standard analysis, low false positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

Text
2022.05.18.22275260v2.full - Author's Original
Available under License Creative Commons Attribution.
Download (1MB)

More information

Published date: 24 May 2022

Identifiers

Local EPrints ID: 468291
URI: http://eprints.soton.ac.uk/id/eprint/468291
PURE UUID: 304df448-818f-48f2-8194-87c162e996f5
ORCID for Eleanor G. Seaby: ORCID iD orcid.org/0000-0002-6814-8648
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833
ORCID for Sarah Ennis: ORCID iD orcid.org/0000-0003-2648-0869

Catalogue record

Date deposited: 09 Aug 2022 16:57
Last modified: 10 Aug 2024 02:00

Export record

Altmetrics

Contributors

Author: Eleanor G. Seaby ORCID iD
Author: N. Simon Thomas
Author: Amy Webb
Author: Helen Brittain
Author: Ana Lisa Taylor Tavares
Author: Diana Baralle ORCID iD
Author: Heidi L. Rehm
Author: Anne O’Donnell-Luria
Author: Sarah Ennis ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×