EyeG2P: an automated variant filtering approach improves efficiency of diagnostic genomic testing for inherited ophthalmic disorders.
Eye Diseases
Genetic Variation
Genomics
Journal
Journal of medical genetics
ISSN: 1468-6244
Titre abrégé: J Med Genet
Pays: England
ID NLM: 2985087R
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
07
05
2022
accepted:
16
12
2022
medline:
15
8
2023
pubmed:
21
1
2023
entrez:
20
1
2023
Statut:
ppublish
Résumé
Genomic variant prioritisation is one of the most significant bottlenecks to mainstream genomic testing in healthcare. Tools to improve precision while ensuring high recall are critical to successful mainstream clinical genomic testing, in particular for whole genome sequencing where millions of variants must be considered for each patient. We developed EyeG2P, a publicly available database and web application using the Ensembl Variant Effect Predictor. EyeG2P is tailored for efficient variant prioritisation for individuals with inherited ophthalmic conditions. We assessed the sensitivity of EyeG2P in 1234 individuals with a broad range of eye conditions who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we assessed the precision of EyeG2P in comparison with routine diagnostic approaches. For 10 additional individuals, we assessed the utility of EyeG2P for whole genome analysis. EyeG2P had 99.5% sensitivity for genomic variants previously identified as clinically relevant through routine diagnostic analysis (n=1234 individuals). Prospectively, EyeG2P enabled a significant increase in precision (35% on average) in comparison with routine testing strategies (p<0.001). We demonstrate that incorporation of EyeG2P into whole genome sequencing analysis strategies can reduce the number of variants for analysis to six variants, on average, while maintaining high diagnostic yield. Automated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.
Sections du résumé
BACKGROUND
Genomic variant prioritisation is one of the most significant bottlenecks to mainstream genomic testing in healthcare. Tools to improve precision while ensuring high recall are critical to successful mainstream clinical genomic testing, in particular for whole genome sequencing where millions of variants must be considered for each patient.
METHODS
We developed EyeG2P, a publicly available database and web application using the Ensembl Variant Effect Predictor. EyeG2P is tailored for efficient variant prioritisation for individuals with inherited ophthalmic conditions. We assessed the sensitivity of EyeG2P in 1234 individuals with a broad range of eye conditions who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we assessed the precision of EyeG2P in comparison with routine diagnostic approaches. For 10 additional individuals, we assessed the utility of EyeG2P for whole genome analysis.
RESULTS
EyeG2P had 99.5% sensitivity for genomic variants previously identified as clinically relevant through routine diagnostic analysis (n=1234 individuals). Prospectively, EyeG2P enabled a significant increase in precision (35% on average) in comparison with routine testing strategies (p<0.001). We demonstrate that incorporation of EyeG2P into whole genome sequencing analysis strategies can reduce the number of variants for analysis to six variants, on average, while maintaining high diagnostic yield.
CONCLUSION
Automated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.
Identifiants
pubmed: 36669873
pii: jmg-2022-108618
doi: 10.1136/jmg-2022-108618
pmc: PMC10423522
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
810-818Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT095908
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT098051
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT108749/Z/15/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT200990/Z/16/Z
Pays : United Kingdom
Organisme : Department of Health
ID : IS‐BRC‐1215‐20007
Pays : United Kingdom
Organisme : Cancer Research UK
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Investigateurs
J C Ambrose
(JC)
P Arumugam
(P)
R Bevers
(R)
M Bleda
(M)
F Boardman-Pretty
(F)
C R Boustred
(CR)
H Brittain
(H)
M A Brown
(MA)
M J Caulfield
(MJ)
G C Chan
(GC)
A Giess
(A)
J N Griffin
(JN)
A Hamblin
(A)
S Henderson
(S)
T J P Hubbard
(TJP)
R Jackson
(R)
L J Jones
(LJ)
D Kasperaviciute
(D)
M Kayikci
(M)
A Kousathanas
(A)
L Lahnstein
(L)
A Lakey
(A)
S E A Leigh
(SEA)
I U S Leong
(IUS)
F J Lopez
(FJ)
F Maleady-Crowe
(F)
M McEntagart
(M)
F Minneci
(F)
J Mitchell
(J)
L Moutsianas
(L)
M Mueller
(M)
N Murugaesu
(N)
A C Need
(AC)
P O'Donovan
(P)
C A Odhams
(CA)
C Patch
(C)
D Perez-Gil
(D)
M B Pereira
(MB)
J Pullinger
(J)
T Rahim
(T)
A Rendon
(A)
T Rogers
(T)
K Savage
(K)
K Sawant
(K)
R H Scott
(RH)
A Siddiq
(A)
A Sieghart
(A)
S C Smith
(SC)
A Sosinsky
(A)
A Stuckey
(A)
M Tanguy
(M)
A L Taylor Tavares
(AL)
E R A Thomas
(ERA)
S R Thompson
(SR)
A Tucci
(A)
M J Welland
(MJ)
E Williams
(E)
K Witkowska
(K)
S M Wood
(SM)
M Zarowiecki
(M)
Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
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