Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis

Patrick Deelen; Sipko van Dam; Johanna C Herkert; Juha M Karjalainen; Harm Brugge; Kristin M Abbott; Cleo C van Diemen; Paul A van der Zwaag; Erica H Gerkes; Evelien Zonneveld-Huijssoon; Jelkje J Boer-Bergsma; Pytrik Folkertsma; Tessa Gillett; K Joeri van der Velde; Roan Kanninga; Peter C van den Akker; Sabrina Z Jan; Edgar T Hoorntje; Wouter P Te Rijdt; Yvonne J Vos; Jan D H Jongbloed; Conny M A van Ravenswaaij-Arts; Richard Sinke; Birgit Sikkema-Raddatz; Wilhelmina S Kerstjens-Frederikse; Morris A Swertz; Lude Franke

doi:10.1038/s41467-019-10649-4

Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis

Patrick Deelen, Sipko van Dam, Johanna C Herkert, Juha M Karjalainen, Harm Brugge, Kristin M Abbott, Cleo C van Diemen, Paul A van der Zwaag, Erica H Gerkes, Evelien Zonneveld-Huijssoon, Jelkje J Boer-Bergsma, Pytrik Folkertsma, Tessa Gillett, K Joeri van der Velde, Roan Kanninga, Peter C van den Akker, Sabrina Z Jan, Edgar T Hoorntje, Wouter P Te Rijdt, Yvonne J VosJan D H Jongbloed, Conny M A van Ravenswaaij-Arts, Richard Sinke, Birgit Sikkema-Raddatz, Wilhelmina S Kerstjens-Frederikse, Morris A Swertz, Lude Franke

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Abstract

The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.

Original language	English
Article number	2837
Number of pages	13
Journal	Nature Communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-10649-4
Publication status	Published - 28-Jun-2019

Keywords

DISEASE
IDENTIFICATION
DISCOVERY
PRIORITIZATION
MUTATIONS
GENOTYPES
VARIANTS

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Deelen, P., van Dam, S., Herkert, J. C., Karjalainen, J. M., Brugge, H., Abbott, K. M., van Diemen, C. C., van der Zwaag, P. A., Gerkes, E. H., Zonneveld-Huijssoon, E., Boer-Bergsma, J. J., Folkertsma, P., Gillett, T., van der Velde, K. J., Kanninga, R., van den Akker, P. C., Jan, S. Z., Hoorntje, E. T., Te Rijdt, W. P., ... Franke, L. (2019). Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis. Nature Communications, 10(1), [2837]. https://doi.org/10.1038/s41467-019-10649-4

@article{091ea4932d5347cc9e4577316de01c4d,

title = "Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis",

abstract = "The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.",

keywords = "DISEASE, IDENTIFICATION, DISCOVERY, PRIORITIZATION, MUTATIONS, GENOTYPES, VARIANTS",

author = "Patrick Deelen and {van Dam}, Sipko and Herkert, {Johanna C} and Karjalainen, {Juha M} and Harm Brugge and Abbott, {Kristin M} and {van Diemen}, {Cleo C} and {van der Zwaag}, {Paul A} and Gerkes, {Erica H} and Evelien Zonneveld-Huijssoon and Boer-Bergsma, {Jelkje J} and Pytrik Folkertsma and Tessa Gillett and {van der Velde}, {K Joeri} and Roan Kanninga and {van den Akker}, {Peter C} and Jan, {Sabrina Z} and Hoorntje, {Edgar T} and {Te Rijdt}, {Wouter P} and Vos, {Yvonne J} and Jongbloed, {Jan D H} and {van Ravenswaaij-Arts}, {Conny M A} and Richard Sinke and Birgit Sikkema-Raddatz and Kerstjens-Frederikse, {Wilhelmina S} and Swertz, {Morris A} and Lude Franke",

year = "2019",

month = jun,

day = "28",

doi = "10.1038/s41467-019-10649-4",

language = "English",

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journal = "Nature Communications",

issn = "2041-1723",

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Deelen, P, van Dam, S, Herkert, JC, Karjalainen, JM, Brugge, H , Abbott, KM , van Diemen, CC , van der Zwaag, PA , Gerkes, EH , Zonneveld-Huijssoon, E, Boer-Bergsma, JJ, Folkertsma, P , Gillett, T , van der Velde, KJ, Kanninga, R, van den Akker, PC, Jan, SZ, Hoorntje, ET, Te Rijdt, WP, Vos, YJ, Jongbloed, JDH , van Ravenswaaij-Arts, CMA , Sinke, R , Sikkema-Raddatz, B , Kerstjens-Frederikse, WS , Swertz, MA & Franke, L 2019, 'Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis', Nature Communications, vol. 10, no. 1, 2837. https://doi.org/10.1038/s41467-019-10649-4

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T1 - Improving the diagnostic yield of exome-sequencing by predicting gene-phenotype associations using large-scale gene expression analysis

AU - Deelen, Patrick

AU - van Dam, Sipko

AU - Herkert, Johanna C

AU - Karjalainen, Juha M

AU - Brugge, Harm

AU - Abbott, Kristin M

AU - van Diemen, Cleo C

AU - van der Zwaag, Paul A

AU - Gerkes, Erica H

AU - Zonneveld-Huijssoon, Evelien

AU - Boer-Bergsma, Jelkje J

AU - Folkertsma, Pytrik

AU - Gillett, Tessa

AU - van der Velde, K Joeri

AU - Kanninga, Roan

AU - van den Akker, Peter C

AU - Jan, Sabrina Z

AU - Hoorntje, Edgar T

AU - Te Rijdt, Wouter P

AU - Vos, Yvonne J

AU - Jongbloed, Jan D H

AU - van Ravenswaaij-Arts, Conny M A

AU - Sinke, Richard

AU - Sikkema-Raddatz, Birgit

AU - Kerstjens-Frederikse, Wilhelmina S

AU - Swertz, Morris A

AU - Franke, Lude

PY - 2019/6/28

Y1 - 2019/6/28

N2 - The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.

AB - The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.

KW - DISEASE

KW - IDENTIFICATION

KW - DISCOVERY

KW - PRIORITIZATION

KW - MUTATIONS

KW - GENOTYPES

KW - VARIANTS

U2 - 10.1038/s41467-019-10649-4

DO - 10.1038/s41467-019-10649-4

M3 - Article

C2 - 31253775

VL - 10

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 2837

ER -