A validated heart-specific model for splice-disrupting variants in childhood heart disease

Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lesurf, Robert [verfasserIn] Breckpot, Jeroen [verfasserIn] Bouwmeester, Jade [verfasserIn] Hanafi, Nour [verfasserIn] Jain, Anjali [verfasserIn] Liang, Yijing [verfasserIn] Papaz, Tanya [verfasserIn] Lougheed, Jane [verfasserIn] Mondal, Tapas [verfasserIn] Alsalehi, Mahmoud [verfasserIn] Altamirano-Diaz, Luis [verfasserIn] Oechslin, Erwin [verfasserIn] Audain, Enrique [verfasserIn] Dombrowsky, Gregor [verfasserIn] Postma, Alex V. [verfasserIn] Woudstra, Odilia I. [verfasserIn] Bouma, Berto J. [verfasserIn] Hitz, Marc-Phillip [verfasserIn] Bezzina, Connie R. [verfasserIn] Blue, Gillian M. [verfasserIn] Winlaw, David S. [verfasserIn] Mital, Seema [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Congenital Heart Disease Genomics RNA splicing Non-canonical Machine Learning

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Genome medicine - BioMed Central, 2009, 16(2024), 1 vom: 15. Okt.
Übergeordnetes Werk:	volume:16 ; year:2024 ; number:1 ; day:15 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s13073-024-01383-8

Katalog-ID:	SPR057779708

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520			\|a Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing.
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allfields	10.1186/s13073-024-01383-8 doi (DE-627)SPR057779708 (SPR)s13073-024-01383-8-e DE-627 ger DE-627 rakwb eng 610 VZ Lesurf, Robert verfasserin aut A validated heart-specific model for splice-disrupting variants in childhood heart disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Breckpot, Jeroen verfasserin aut Bouwmeester, Jade verfasserin aut Hanafi, Nour verfasserin aut Jain, Anjali verfasserin aut Liang, Yijing verfasserin aut Papaz, Tanya verfasserin aut Lougheed, Jane verfasserin aut Mondal, Tapas verfasserin aut Alsalehi, Mahmoud verfasserin aut Altamirano-Diaz, Luis verfasserin aut Oechslin, Erwin verfasserin aut Audain, Enrique verfasserin aut Dombrowsky, Gregor verfasserin aut Postma, Alex V. verfasserin aut Woudstra, Odilia I. verfasserin aut Bouma, Berto J. verfasserin aut Hitz, Marc-Phillip verfasserin aut Bezzina, Connie R. verfasserin aut Blue, Gillian M. verfasserin aut Winlaw, David S. verfasserin aut Mital, Seema verfasserin (orcid)0000-0002-7643-4484 aut Enthalten in Genome medicine BioMed Central, 2009 16(2024), 1 vom: 15. Okt. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:15 month:10 https://dx.doi.org/10.1186/s13073-024-01383-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 15 10
spelling	10.1186/s13073-024-01383-8 doi (DE-627)SPR057779708 (SPR)s13073-024-01383-8-e DE-627 ger DE-627 rakwb eng 610 VZ Lesurf, Robert verfasserin aut A validated heart-specific model for splice-disrupting variants in childhood heart disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Breckpot, Jeroen verfasserin aut Bouwmeester, Jade verfasserin aut Hanafi, Nour verfasserin aut Jain, Anjali verfasserin aut Liang, Yijing verfasserin aut Papaz, Tanya verfasserin aut Lougheed, Jane verfasserin aut Mondal, Tapas verfasserin aut Alsalehi, Mahmoud verfasserin aut Altamirano-Diaz, Luis verfasserin aut Oechslin, Erwin verfasserin aut Audain, Enrique verfasserin aut Dombrowsky, Gregor verfasserin aut Postma, Alex V. verfasserin aut Woudstra, Odilia I. verfasserin aut Bouma, Berto J. verfasserin aut Hitz, Marc-Phillip verfasserin aut Bezzina, Connie R. verfasserin aut Blue, Gillian M. verfasserin aut Winlaw, David S. verfasserin aut Mital, Seema verfasserin (orcid)0000-0002-7643-4484 aut Enthalten in Genome medicine BioMed Central, 2009 16(2024), 1 vom: 15. Okt. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:15 month:10 https://dx.doi.org/10.1186/s13073-024-01383-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 15 10
allfields_unstemmed	10.1186/s13073-024-01383-8 doi (DE-627)SPR057779708 (SPR)s13073-024-01383-8-e DE-627 ger DE-627 rakwb eng 610 VZ Lesurf, Robert verfasserin aut A validated heart-specific model for splice-disrupting variants in childhood heart disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Breckpot, Jeroen verfasserin aut Bouwmeester, Jade verfasserin aut Hanafi, Nour verfasserin aut Jain, Anjali verfasserin aut Liang, Yijing verfasserin aut Papaz, Tanya verfasserin aut Lougheed, Jane verfasserin aut Mondal, Tapas verfasserin aut Alsalehi, Mahmoud verfasserin aut Altamirano-Diaz, Luis verfasserin aut Oechslin, Erwin verfasserin aut Audain, Enrique verfasserin aut Dombrowsky, Gregor verfasserin aut Postma, Alex V. verfasserin aut Woudstra, Odilia I. verfasserin aut Bouma, Berto J. verfasserin aut Hitz, Marc-Phillip verfasserin aut Bezzina, Connie R. verfasserin aut Blue, Gillian M. verfasserin aut Winlaw, David S. verfasserin aut Mital, Seema verfasserin (orcid)0000-0002-7643-4484 aut Enthalten in Genome medicine BioMed Central, 2009 16(2024), 1 vom: 15. Okt. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:15 month:10 https://dx.doi.org/10.1186/s13073-024-01383-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 15 10
allfieldsGer	10.1186/s13073-024-01383-8 doi (DE-627)SPR057779708 (SPR)s13073-024-01383-8-e DE-627 ger DE-627 rakwb eng 610 VZ Lesurf, Robert verfasserin aut A validated heart-specific model for splice-disrupting variants in childhood heart disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Breckpot, Jeroen verfasserin aut Bouwmeester, Jade verfasserin aut Hanafi, Nour verfasserin aut Jain, Anjali verfasserin aut Liang, Yijing verfasserin aut Papaz, Tanya verfasserin aut Lougheed, Jane verfasserin aut Mondal, Tapas verfasserin aut Alsalehi, Mahmoud verfasserin aut Altamirano-Diaz, Luis verfasserin aut Oechslin, Erwin verfasserin aut Audain, Enrique verfasserin aut Dombrowsky, Gregor verfasserin aut Postma, Alex V. verfasserin aut Woudstra, Odilia I. verfasserin aut Bouma, Berto J. verfasserin aut Hitz, Marc-Phillip verfasserin aut Bezzina, Connie R. verfasserin aut Blue, Gillian M. verfasserin aut Winlaw, David S. verfasserin aut Mital, Seema verfasserin (orcid)0000-0002-7643-4484 aut Enthalten in Genome medicine BioMed Central, 2009 16(2024), 1 vom: 15. Okt. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:15 month:10 https://dx.doi.org/10.1186/s13073-024-01383-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 15 10
allfieldsSound	10.1186/s13073-024-01383-8 doi (DE-627)SPR057779708 (SPR)s13073-024-01383-8-e DE-627 ger DE-627 rakwb eng 610 VZ Lesurf, Robert verfasserin aut A validated heart-specific model for splice-disrupting variants in childhood heart disease 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Breckpot, Jeroen verfasserin aut Bouwmeester, Jade verfasserin aut Hanafi, Nour verfasserin aut Jain, Anjali verfasserin aut Liang, Yijing verfasserin aut Papaz, Tanya verfasserin aut Lougheed, Jane verfasserin aut Mondal, Tapas verfasserin aut Alsalehi, Mahmoud verfasserin aut Altamirano-Diaz, Luis verfasserin aut Oechslin, Erwin verfasserin aut Audain, Enrique verfasserin aut Dombrowsky, Gregor verfasserin aut Postma, Alex V. verfasserin aut Woudstra, Odilia I. verfasserin aut Bouma, Berto J. verfasserin aut Hitz, Marc-Phillip verfasserin aut Bezzina, Connie R. verfasserin aut Blue, Gillian M. verfasserin aut Winlaw, David S. verfasserin aut Mital, Seema verfasserin (orcid)0000-0002-7643-4484 aut Enthalten in Genome medicine BioMed Central, 2009 16(2024), 1 vom: 15. Okt. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:15 month:10 https://dx.doi.org/10.1186/s13073-024-01383-8 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 15 10
language	English
source	Enthalten in Genome medicine 16(2024), 1 vom: 15. Okt. volume:16 year:2024 number:1 day:15 month:10
sourceStr	Enthalten in Genome medicine 16(2024), 1 vom: 15. Okt. volume:16 year:2024 number:1 day:15 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Congenital Heart Disease Genomics RNA splicing Non-canonical Machine Learning
dewey-raw	610
isfreeaccess_bool	true
container_title	Genome medicine
authorswithroles_txt_mv	Lesurf, Robert @@aut@@ Breckpot, Jeroen @@aut@@ Bouwmeester, Jade @@aut@@ Hanafi, Nour @@aut@@ Jain, Anjali @@aut@@ Liang, Yijing @@aut@@ Papaz, Tanya @@aut@@ Lougheed, Jane @@aut@@ Mondal, Tapas @@aut@@ Alsalehi, Mahmoud @@aut@@ Altamirano-Diaz, Luis @@aut@@ Oechslin, Erwin @@aut@@ Audain, Enrique @@aut@@ Dombrowsky, Gregor @@aut@@ Postma, Alex V. @@aut@@ Woudstra, Odilia I. @@aut@@ Bouma, Berto J. @@aut@@ Hitz, Marc-Phillip @@aut@@ Bezzina, Connie R. @@aut@@ Blue, Gillian M. @@aut@@ Winlaw, David S. @@aut@@ Mital, Seema @@aut@@
publishDateDaySort_date	2024-10-15T00:00:00Z
hierarchy_top_id	594424275
dewey-sort	3610
id	SPR057779708
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057779708</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241015064728.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241015s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-024-01383-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057779708</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13073-024-01383-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lesurf, Robert</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A validated heart-specific model for splice-disrupting variants in childhood heart disease</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. 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author	Lesurf, Robert
spellingShingle	Lesurf, Robert ddc 610 misc Congenital Heart Disease misc Genomics misc RNA splicing misc Non-canonical misc Machine Learning A validated heart-specific model for splice-disrupting variants in childhood heart disease
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topic_title	610 VZ A validated heart-specific model for splice-disrupting variants in childhood heart disease Congenital Heart Disease (dpeaa)DE-He213 Genomics (dpeaa)DE-He213 RNA splicing (dpeaa)DE-He213 Non-canonical (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213
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title	A validated heart-specific model for splice-disrupting variants in childhood heart disease
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title_full	A validated heart-specific model for splice-disrupting variants in childhood heart disease
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author_browse	Lesurf, Robert Breckpot, Jeroen Bouwmeester, Jade Hanafi, Nour Jain, Anjali Liang, Yijing Papaz, Tanya Lougheed, Jane Mondal, Tapas Alsalehi, Mahmoud Altamirano-Diaz, Luis Oechslin, Erwin Audain, Enrique Dombrowsky, Gregor Postma, Alex V. Woudstra, Odilia I. Bouma, Berto J. Hitz, Marc-Phillip Bezzina, Connie R. Blue, Gillian M. Winlaw, David S. Mital, Seema
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title_sort	a validated heart-specific model for splice-disrupting variants in childhood heart disease
title_auth	A validated heart-specific model for splice-disrupting variants in childhood heart disease
abstract	Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. © The Author(s) 2024
abstractGer	Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. © The Author(s) 2024
abstract_unstemmed	Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing. © The Author(s) 2024
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title_short	A validated heart-specific model for splice-disrupting variants in childhood heart disease
url	https://dx.doi.org/10.1186/s13073-024-01383-8
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author2	Breckpot, Jeroen Bouwmeester, Jade Hanafi, Nour Jain, Anjali Liang, Yijing Papaz, Tanya Lougheed, Jane Mondal, Tapas Alsalehi, Mahmoud Altamirano-Diaz, Luis Oechslin, Erwin Audain, Enrique Dombrowsky, Gregor Postma, Alex V. Woudstra, Odilia I. Bouma, Berto J. Hitz, Marc-Phillip Bezzina, Connie R. Blue, Gillian M. Winlaw, David S. Mital, Seema
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up_date	2024-10-15T04:49:30.583Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057779708</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241015064728.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241015s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-024-01383-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057779708</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13073-024-01383-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lesurf, Robert</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A validated heart-specific model for splice-disrupting variants in childhood heart disease</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. 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A validated heart-specific model for splice-disrupting variants in childhood heart disease

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