Intronic position +9 and −9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data

Zhang, Li; Shen, Meikun; Shu, Xianhong; Zhou, Jingmin; Ding, Jing; Zhong, Chunjiu; Pan, Baishen; Wang, Beili; Cui, Zhenling; Wei, Guo

doi:10.1186/s12920-023-01542-7

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…Application of the model to 971 CHD WGS samples, in addition to the confirmed splice variants assessed in patient myocardium of 114 cases, identified 3% of CHD cases that harbor non-canonical splice-disrupting variants in Tier 1 CHD genes. As nearly half (44%) of these variants were intronic, our combined use of RNA-Seq and WGS enabled the identification of putatively pathogenic variants that would not be detected by panel or exome sequencing, as intronic variants beyond ∼50bp are not reliably found with these methods 12,13 .…”

Section: Discussionmentioning

confidence: 99%

“…In addition, in a case-control burden analysis, an enrichment for rare splice region variants predicted to result in the loss of nearby existing splice junctions among CHD genes was observed. Unfortunately, exome sequencing is unable to detect deeply intronic splice-disrupting variants 12,13 , and minigene assays alone have technical limitations since they are not cardiac specific, cannot test variants in repetitive regions, and often provide indeterminate results 27,28 .…”

Section: Discussionmentioning

confidence: 99%

“…In addition to identifying canonical splice-disrupting variants in known CHD-related genes in 0.5% cases, this approach identified putatively damaging non-canonical splice-disrupting variants in 8% of isolated CHD, with deeply intronic variants representing 41% of all high-confidence splice-disrupting variants in CHD genes. WGS was critical for the identification of variants that would not be captured by routine clinical genetic tests including exome sequencing 12,13 , while cardiac RNA sequencing (RNA-Seq) allowed for high specificity in the interpretation of splice-disrupting effects. This splice-disrupting variant discovery framework, coupled with a case-control burden analysis, provides a practical strategy for increasing the yield of pathogenic variants in known CHD genes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lesurf,

Breckpot,

Bouwmeester

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Lesurf,

Breckpot,

Bouwmeester

et al. 2023

Preprint

View full text Add to dashboard Cite

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The Clinical and Genetic Diversity of Thyroid Hormone Resistance: Four Clinical Vignettes

Deeb,

Kadam,

El-Kebbi

2024

Horm Res Paediatr

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Introduction: Resistance to thyroid hormones (RTH) is a rare but an important genetic cause of decreased peripheral tissue responses to the actions of thyroxine. Most RTH cases are caused by mutations in thyroid hormone receptor β (TRβ, THRB), while a few are caused by mutations in thyroid hormone receptor α (TRα, THRA). RTH is clinically heterogeneous, and the biochemical features are often confusing, resulting in misdiagnoses, mismanagement, and life-long consequences for affected individuals. An awareness of the clinical and genetic spectrum of RTH is therefore essential to avoid misdiagnosis and to ensure timely referral for definitive management. Case presentation: Here we present four clinical vignettes describing three children and one adult with RTH encountered in our “real-world” tertiary pediatric endocrinology practice. We describe a novel THRA (NM_199334.3:c.-298+5G>A) missense mutation in the first intron in the 5’ untranslated region (UTR) of THRA, with causal variant prediction with CADD placing the mutation in the top 1% most deleterious variants (scaled C-score 21.7). We speculate that this mutation causes an exon skipping event affecting the 5’UTR and protein-coding region, thereby resulting in abnormal or absent TRα1, although supporting clinical, genetic, and/or functional analyses are required to upgrade the pathogenicity classification from uncertain significance to pathogenic/likely pathogenic. The three cases describing “classical” RTH caused by THRB mutations showcase the consequences of misdiagnosis, with two patients prescribed medications that could exacerbate symptoms and one child presenting with behavioral problems that might benefit from tailored management with hormone therapies. Conclusion: This report not only highlights the importance of a high index of suspicion for RTH to prompt the genetic diagnosis but also contributes to a growing appreciation of the pathogenic role of non-coding variants in rare diseases.

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

Lesurf,

Breckpot,

Bouwmeester

et al. 2024

Genome Med

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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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Intronic position +9 and −9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data

Cited by 3 publications

References 46 publications

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

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

The Clinical and Genetic Diversity of Thyroid Hormone Resistance: Four Clinical Vignettes

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

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