Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

Katherine S Josephs; Angharad M Roberts; Pantazis Theotokis; Roddy Walsh; Philip J Ostrowski; Matthew Edwards; Andrew Fleming; Courtney Thaxton; Jason D Roberts; Melanie Care; Wojciech Zareba; Arnon Adler; Amy C Sturm; Rafik Tadros; Valeria Novelli; Emma Owens; Lucas Bronicki; Olga Jarinova; Bert Callewaert; Stacey Peters; Tom Lumbers; Elizabeth Jordan; Babken Asatryan; Neesha Krishnan; Ray E Hershberger; C Anwar A Chahal; Andrew P Landstrom; Cynthia James; Elizabeth M McNally; Daniel P Judge; Peter van Tintelen; Arthur Wilde; Michael Gollob; Jodie Ingles; James S Ware

doi:10.1186/s13073-023-01246-8

Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

Katherine S Josephs, Angharad M Roberts, Pantazis Theotokis, Roddy Walsh, Philip J Ostrowski, Matthew Edwards, Andrew Fleming, Courtney Thaxton, Jason D Roberts, Melanie Care, Wojciech Zareba, Arnon Adler, Amy C Sturm, Rafik Tadros, Valeria Novelli, Emma Owens, Lucas Bronicki, Olga Jarinova, Bert Callewaert, Stacey PetersTom Lumbers, Elizabeth Jordan, Babken Asatryan, Neesha Krishnan, Ray E Hershberger, C Anwar A Chahal, Andrew P Landstrom, Cynthia James, Elizabeth M McNally, Daniel P Judge, Peter van Tintelen, Arthur Wilde, Michael Gollob, Jodie Ingles, James S Ware^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

BACKGROUND: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.

METHODS: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.

RESULTS: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.

CONCLUSIONS: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

Original language	English
Article number	86
Number of pages	15
Journal	Genome Medicine
Volume	15
Issue number	1
DOIs	https://doi.org/10.1186/s13073-023-01246-8
Publication status	Published - Dec 2023

Keywords

Databases, Genetic
Genetic Testing
Genetic Variation
Genomics
Humans
Inheritance Patterns
Inherited cardiac conditions
Inheritance
Variant classification
Allelic requirement
Disease mechanism
Genomic variant filtering
Gene curation
Variant interpretation

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Josephs, K. S., Roberts, A. M., Theotokis, P., Walsh, R., Ostrowski, P. J., Edwards, M., Fleming, A., Thaxton, C., Roberts, J. D., Care, M., Zareba, W., Adler, A., Sturm, A. C., Tadros, R., Novelli, V., Owens, E., Bronicki, L., Jarinova, O., Callewaert, B., ... Ware, J. S. (2023). Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions. Genome Medicine, 15(1), Article 86. https://doi.org/10.1186/s13073-023-01246-8

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title = "Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions",

abstract = "BACKGROUND: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.METHODS: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.RESULTS: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.CONCLUSIONS: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.",

keywords = "Databases, Genetic, Genetic Testing, Genetic Variation, Genomics, Humans, Inheritance Patterns, Inherited cardiac conditions, Inheritance, Variant classification, Allelic requirement, Disease mechanism, Genomic variant filtering, Gene curation, Variant interpretation",

author = "Josephs, {Katherine S} and Roberts, {Angharad M} and Pantazis Theotokis and Roddy Walsh and Ostrowski, {Philip J} and Matthew Edwards and Andrew Fleming and Courtney Thaxton and Roberts, {Jason D} and Melanie Care and Wojciech Zareba and Arnon Adler and Sturm, {Amy C} and Rafik Tadros and Valeria Novelli and Emma Owens and Lucas Bronicki and Olga Jarinova and Bert Callewaert and Stacey Peters and Tom Lumbers and Elizabeth Jordan and Babken Asatryan and Neesha Krishnan and Hershberger, {Ray E} and Chahal, {C Anwar A} and Landstrom, {Andrew P} and Cynthia James and McNally, {Elizabeth M} and Judge, {Daniel P} and {van Tintelen}, Peter and Arthur Wilde and Michael Gollob and Jodie Ingles and Ware, {James S}",

note = "Publisher Copyright: {\textcopyright} 2023, BioMed Central Ltd., part of Springer Nature.",

year = "2023",

month = dec,

doi = "10.1186/s13073-023-01246-8",

language = "English",

volume = "15",

journal = "Genome Medicine",

issn = "1756-994x",

publisher = "BioMed Central",

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Josephs, KS, Roberts, AM, Theotokis, P, Walsh, R, Ostrowski, PJ, Edwards, M, Fleming, A, Thaxton, C, Roberts, JD, Care, M, Zareba, W, Adler, A, Sturm, AC, Tadros, R, Novelli, V, Owens, E, Bronicki, L, Jarinova, O, Callewaert, B, Peters, S, Lumbers, T, Jordan, E, Asatryan, B, Krishnan, N, Hershberger, RE, Chahal, CAA, Landstrom, AP, James, C, McNally, EM, Judge, DP, van Tintelen, P, Wilde, A, Gollob, M, Ingles, J & Ware, JS 2023, 'Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions', Genome Medicine, vol. 15, no. 1, 86. https://doi.org/10.1186/s13073-023-01246-8

Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions. / Josephs, Katherine S; Roberts, Angharad M; Theotokis, Pantazis et al.
In: Genome Medicine, Vol. 15, No. 1, 86, 12.2023.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Beyond gene-disease validity

T2 - capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

AU - Josephs, Katherine S

AU - Roberts, Angharad M

AU - Theotokis, Pantazis

AU - Walsh, Roddy

AU - Ostrowski, Philip J

AU - Edwards, Matthew

AU - Fleming, Andrew

AU - Thaxton, Courtney

AU - Roberts, Jason D

AU - Care, Melanie

AU - Zareba, Wojciech

AU - Adler, Arnon

AU - Sturm, Amy C

AU - Tadros, Rafik

AU - Novelli, Valeria

AU - Owens, Emma

AU - Bronicki, Lucas

AU - Jarinova, Olga

AU - Callewaert, Bert

AU - Peters, Stacey

AU - Lumbers, Tom

AU - Jordan, Elizabeth

AU - Asatryan, Babken

AU - Krishnan, Neesha

AU - Hershberger, Ray E

AU - Chahal, C Anwar A

AU - Landstrom, Andrew P

AU - James, Cynthia

AU - McNally, Elizabeth M

AU - Judge, Daniel P

AU - van Tintelen, Peter

AU - Wilde, Arthur

AU - Gollob, Michael

AU - Ingles, Jodie

AU - Ware, James S

PY - 2023/12

Y1 - 2023/12

N2 - BACKGROUND: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.METHODS: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.RESULTS: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.CONCLUSIONS: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

AB - BACKGROUND: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.METHODS: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.RESULTS: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.CONCLUSIONS: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

KW - Databases, Genetic

KW - Genetic Testing

KW - Genetic Variation

KW - Genomics

KW - Humans

KW - Inheritance Patterns

KW - Inherited cardiac conditions

KW - Inheritance

KW - Variant classification

KW - Allelic requirement

KW - Disease mechanism

KW - Genomic variant filtering

KW - Gene curation

KW - Variant interpretation

UR - http://www.scopus.com/inward/record.url?scp=85174828629&partnerID=8YFLogxK

U2 - 10.1186/s13073-023-01246-8

DO - 10.1186/s13073-023-01246-8

M3 - Article

C2 - 37872640

SN - 1756-994x

VL - 15

JO - Genome Medicine

JF - Genome Medicine

IS - 1

M1 - 86

ER -

Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

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