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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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.

Originele taal-2	English
Artikelnummer	2837
Aantal pagina's	13
Tijdschrift	Nature Communications
Volume	10
Nummer van het tijdschrift	1
DOI's	https://doi.org/10.1038/s41467-019-10649-4
Status	Published - 28-jun.-2019

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10.1038/s41467-019-10649-4Licentie: CC BY

Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysisFinal publisher's version, 2,01 MBLicentie: CC BY

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Additional file 10 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-apr.-2023
DOI: 10.6084/m9.figshare.22655426, https://springernature.figshare.com/articles/dataset/Additional_file_10_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655426
Dataset
Additional file 13 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-apr.-2023
DOI: 10.6084/m9.figshare.22655435, https://springernature.figshare.com/articles/dataset/Additional_file_13_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655435
Dataset
Additional file 1 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-apr.-2023
DOI: 10.6084/m9.figshare.22655396, https://springernature.figshare.com/articles/dataset/Additional_file_1_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655396
Dataset

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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), Artikel 2837. https://doi.org/10.1038/s41467-019-10649-4

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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.",

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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",

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

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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, nr. 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

SN - 2041-1723

VL - 10

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 2837

ER -

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

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Additional file 10 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Additional file 13 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Additional file 1 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

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