Dataset Information

Contribution of Previously Unrecognized RNA Splice-Altering Variants to Congenital Heart Disease.

ABSTRACT:

Background

Known genetic causes of congenital heart disease (CHD) explain <40% of CHD cases, and interpreting the clinical significance of variants with uncertain functional impact remains challenging. We aim to improve diagnostic classification of variants in patients with CHD by assessing the impact of noncanonical splice region variants on RNA splicing.

Methods

We tested de novo variants from trio studies of 2649 CHD probands and their parents, as well as rare (allele frequency, <2×10^-⁶) variants from 4472 CHD probands in the Pediatric Cardiac Genetics Consortium through a combined computational and in vitro approach.

Results

We identified 53 de novo and 74 rare variants in CHD cases that alter splicing and thus are loss of function. Of these, 77 variants are in known dominant, recessive, and candidate CHD genes, including KMT2D and RBFOX2. In 1 case, we confirmed the variant's predicted impact on RNA splicing in RNA transcripts from the proband's cardiac tissue. Two probands were found to have 2 loss-of-function variants for recessive CHD genes HECTD1 and DYNC2H1. In addition, SpliceAI-a predictive algorithm for altered RNA splicing-has a positive predictive value of ≈93% in our cohort.

Conclusions

Through assessment of RNA splicing, we identified a new loss-of-function variant within a CHD gene in 78 probands, of whom 69 (1.5%; n=4472) did not have a previously established genetic explanation for CHD. Identification of splice-altering variants improves diagnostic classification and genetic diagnoses for CHD.

Registration

URL: https://clinicaltrials.gov; Unique identifier: NCT01196182.

SUBMITTER: Jang MY

PROVIDER: S-EPMC10404383 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Contribution of Previously Unrecognized RNA Splice-Altering Variants to Congenital Heart Disease.

Jang Min Young MY Patel Parth N PN Pereira Alexandre C AC Willcox Jon A L JAL Haghighi Alireza A Tai Angela C AC Ito Kaoru K Morton Sarah U SU Gorham Joshua M JM McKean David M DM DePalma Steven R SR Bernstein Daniel D Brueckner Martina M Chung Wendy K WK Giardini Alessandro A Goldmuntz Elizabeth E Kaltman Jonathan R JR Kim Richard R Newburger Jane W JW Shen Yufeng Y Srivastava Deepak D Tristani-Firouzi Martin M Gelb Bruce D BD Porter George A GA Seidman Christine E CE Seidman Jonathan G JG

Circulation. Genomic and precision medicine 20230511 3

<h4>Background</h4>Known genetic causes of congenital heart disease (CHD) explain <40% of CHD cases, and interpreting the clinical significance of variants with uncertain functional impact remains challenging. We aim to improve diagnostic classification of variants in patients with CHD by assessing the impact of noncanonical splice region variants on RNA splicing.<h4>Methods</h4>We tested de novo variants from trio studies of 2649 CHD probands and their parents, as well as rare (allele frequency ...[more]

PMID: 37165897

Similar Datasets

Project description:BackgroundCongenital 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.MethodsWe 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.ResultsOf 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.ConclusionsA 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.

Dataset Information

Contribution of Previously Unrecognized RNA Splice-Altering Variants to Congenital Heart Disease.

Background

Methods

Results

Conclusions

Registration

Publications

Contribution of Previously Unrecognized RNA Splice-Altering Variants to Congenital Heart Disease.

Similar Datasets

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