A validated heart-specific model for splice-disrupting variants in childhood heart disease
Authors:
- Robert LesurfRLRobert Lesurf
- Jeroen BreckpotJBJeroen Breckpot
- Jade BouwmeesterJBJade Bouwmeester
- Nour HanafiNHNour Hanafi
- Anjali JainAJAnjali Jain
- Yijing LiangYLYijing Liang
- Tanya PapazTPTanya Papaz
- Jane LougheedJLJane Lougheed
- Tapas MondalTMTapas Mondal
- Mahmoud AlsalehiMAMahmoud Alsalehi
- Luis Altamirano-DiazLALuis Altamirano-Diaz
- Erwin OechslinEOErwin Oechslin
Preprint
Abstract
Congenital heart disease (CHD) is the most common congenital anomaly. Non-canonical splice-disrupting variants are not routinely evaluated by clinical tests. Algorithms including SpliceAI predict such variants, but are not specific to cardiac-expressed genes. Whole genome (WGS) (n=1083) and myocardial RNA-Sequencing (RNA-Seq) (n=114) of CHD cases was used to identify splice-disrupting variants. Using features of variants confirmed to affect splicing in myocardial RNA, we trained a machine learning model that outperformed SpliceAI for predicting cardiac-specific splice-disrupting variants (AUC 0.92 vs 0.66), and was independently validated in 43 cardiomyopathy probands (AUC 0.88 vs 0.64). Application of this model to 971 CHD WGS samples identified 9% patients with splice-disrupting variants in CHD genes. Forty-one% of predicted splice-disrupting variants were deeply intronic. The burden of variants in CHD genes was higher in cases compared with 2,570 controls. Our model improved genetic yield by identifying splice-disrupting variants that are not evaluated by routine tests.Keywords
All Related Versions
- Published version: Genome Medicine, 16 (1), 1.
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