Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

Jonathan Rips; Orli Halstuk; Adina Fuchs; Ziv Lang; Tal Sido; Shiri Gershon-Naamat; Bassam Abu-Libdeh; Simon Edvardson; Somaya Salah; Oded Breuer; Mohamad Hadhud; Sharon Eden; Itamar Simon; Mordechai Slae; Nadirah S. Damseh; Abdulsalam Abu-Libdeh; Marina Eskin-Schwartz; Ohad S. Birk; Julia Varga; Ora Schueler-Furman; Chaggai Rosenbluh; Orly Elpeleg; Shira Yanovsky-Dagan; Hagar Mor-Shaked; Tamar Harel

doi:10.1016/j.gim.2024.101068

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

Jonathan Rips, Orli Halstuk, Adina Fuchs, Ziv Lang, Tal Sido, Shiri Gershon-Naamat, Bassam Abu-Libdeh, Simon Edvardson, Somaya Salah, Oded Breuer, Mohamad Hadhud, Sharon Eden, Itamar Simon, Mordechai Slae, Nadirah S. Damseh, Abdulsalam Abu-Libdeh, Marina Eskin-Schwartz, Ohad S. Birk, Julia Varga, Ora Schueler-FurmanChaggai Rosenbluh, Orly Elpeleg, Shira Yanovsky-Dagan, Hagar Mor-Shaked, Tamar Harel

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

3 Scopus citations

Abstract

Purpose: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. Methods: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. Results: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. Conclusion: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

Original language	English
Article number	101068
Journal	Genetics in Medicine
Volume	26
Issue number	4
DOIs	https://doi.org/10.1016/j.gim.2024.101068
State	Published - Apr 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2024 American College of Medical Genetics and Genomics

Funding

The authors thank the families who participated in this study and the referring physicians. No external funding to be declared. Conceptualization of study and methodology: J.R. T.H.; Analysis of exome data: J.R. O.E. H.M.-S. T.H.; Molecular experiments and data analysis: O.H. A.F. S.Y.D. S.E. I.S.; Bioinformatics and computational approaches: Z.L. T.S. S.G.-N.; Clinical and genomic data: B.A.-L. S.E. S.S. O.B. M.H. M.S. N.S.D. A.A. M.E.S. O.S.B.; NGS experiments: C.R.; In silico modeling: J.V. O.S.-F.; Writing-original draft: J.R. T.H.; Writing-review and editing: all authors. The study was conducted in accordance with Hadassah Medical Organization's IRB-approved protocol (No. 0306-10-HMO). All families signed informed consent for genomic testing. Families providing photographs provided additional consent, for photo publication. DNA numbers were coded.

Keywords

Exome sequencing
Genotype matching
HPO terms
KAT2A
Phenotype similarity scores
RPAIN
Variants of uncertain significance

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.gim.2024.101068

Cite this

Rips, J., Halstuk, O., Fuchs, A., Lang, Z., Sido, T., Gershon-Naamat, S., Abu-Libdeh, B., Edvardson, S., Salah, S., Breuer, O., Hadhud, M., Eden, S., Simon, I., Slae, M., Damseh, N. S., Abu-Libdeh, A., Eskin-Schwartz, M., Birk, O. S., Varga, J., ... Harel, T. (2024). Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield. Genetics in Medicine, 26(4), Article 101068. https://doi.org/10.1016/j.gim.2024.101068

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title = "Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield",

abstract = "Purpose: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. Methods: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. Results: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. Conclusion: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.",

keywords = "Exome sequencing, Genotype matching, HPO terms, KAT2A, Phenotype similarity scores, RPAIN, Variants of uncertain significance",

author = "Jonathan Rips and Orli Halstuk and Adina Fuchs and Ziv Lang and Tal Sido and Shiri Gershon-Naamat and Bassam Abu-Libdeh and Simon Edvardson and Somaya Salah and Oded Breuer and Mohamad Hadhud and Sharon Eden and Itamar Simon and Mordechai Slae and Damseh, {Nadirah S.} and Abdulsalam Abu-Libdeh and Marina Eskin-Schwartz and Birk, {Ohad S.} and Julia Varga and Ora Schueler-Furman and Chaggai Rosenbluh and Orly Elpeleg and Shira Yanovsky-Dagan and Hagar Mor-Shaked and Tamar Harel",

note = "Publisher Copyright: {\textcopyright} 2024 American College of Medical Genetics and Genomics",

year = "2024",

month = apr,

doi = "10.1016/j.gim.2024.101068",

language = "אנגלית",

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Rips, J, Halstuk, O, Fuchs, A, Lang, Z, Sido, T, Gershon-Naamat, S, Abu-Libdeh, B, Edvardson, S, Salah, S, Breuer, O, Hadhud, M, Eden, S, Simon, I, Slae, M, Damseh, NS, Abu-Libdeh, A, Eskin-Schwartz, M, Birk, OS, Varga, J, Schueler-Furman, O, Rosenbluh, C, Elpeleg, O, Yanovsky-Dagan, S, Mor-Shaked, H & Harel, T 2024, 'Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield', Genetics in Medicine, vol. 26, no. 4, 101068. https://doi.org/10.1016/j.gim.2024.101068

TY - JOUR

T1 - Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

AU - Rips, Jonathan

AU - Halstuk, Orli

AU - Fuchs, Adina

AU - Lang, Ziv

AU - Sido, Tal

AU - Gershon-Naamat, Shiri

AU - Abu-Libdeh, Bassam

AU - Edvardson, Simon

AU - Salah, Somaya

AU - Breuer, Oded

AU - Hadhud, Mohamad

AU - Eden, Sharon

AU - Simon, Itamar

AU - Slae, Mordechai

AU - Damseh, Nadirah S.

AU - Abu-Libdeh, Abdulsalam

AU - Eskin-Schwartz, Marina

AU - Birk, Ohad S.

AU - Varga, Julia

AU - Schueler-Furman, Ora

AU - Rosenbluh, Chaggai

AU - Elpeleg, Orly

AU - Yanovsky-Dagan, Shira

AU - Mor-Shaked, Hagar

AU - Harel, Tamar

PY - 2024/4

Y1 - 2024/4

N2 - Purpose: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. Methods: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. Results: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. Conclusion: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

AB - Purpose: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. Methods: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. Results: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. Conclusion: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

KW - Exome sequencing

KW - Genotype matching

KW - HPO terms

KW - KAT2A

KW - Phenotype similarity scores

KW - RPAIN

KW - Variants of uncertain significance

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Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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