Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation

Truty, Rebecca; Ouyang, Karen; Rojahn, Susan; García, Sarah; Colavin, Alexandre; Hamlington, Barbara; Freivogel, Mary E.; Nussbaum, Robert L.; Nykamp, Keith; Aradhya, Swaroop

doi:10.1016/j.ajhg.2021.03.006

Cited by 49 publications

(51 citation statements)

References 52 publications

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“…In principle, both coding and intronic variants within a gene have the potential to affect splicing (Cooper et al, 2009;Scotti and Swanson, 2016;Anna and Monika, 2018;Truty et al, 2021). Genetic variants occurring within the 5 splice site GT dinucleotide, whenever found in disease-causing or disease-predisposing genes, have generally been classified as pathogenic (Mount et al, 2019;Stenson et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Splicing Outcomes of 5′ Splice Site GT>GC Variants That Generate Wild-Type Transcripts Differ Significantly Between Full-Length and Minigene Splicing Assays

Lin

Zou

et al. 2021

Front. Genet.

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Combining data derived from a meta-analysis of human disease-associated 5′ splice site GT>GC (i.e., +2T>C) variants and a cell culture-based full-length gene splicing assay (FLGSA) of forward engineered +2T>C substitutions, we recently estimated that ∼15–18% of +2T>C variants can generate up to 84% wild-type transcripts relative to their wild-type counterparts. Herein, we analyzed the splicing outcomes of 20 +2T>C variants that generate some wild-type transcripts in two minigene assays. We found a high discordance rate in terms of the generation of wild-type transcripts, not only between FLGSA and the minigene assays but also between the different minigene assays. In the pET01 context, all 20 wild-type minigene constructs generated the expected wild-type transcripts; of the 20 corresponding variant minigene constructs, 14 (70%) generated wild-type transcripts. In the pSPL3 context, only 18 of the 20 wild-type minigene constructs generated the expected wild-type transcripts whereas 8 of the 18 (44%) corresponding variant minigene constructs generated wild-type transcripts. Thus, in the context of a particular type of variant, we raise awareness of the limitations of minigene splicing assays and emphasize the importance of sequence context in regulating splicing. Whether or not our findings apply to other types of splice-altering variant remains to be investigated.

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Section: Introductionmentioning

confidence: 99%

Splicing Outcomes of 5′ Splice Site GT>GC Variants That Generate Wild-Type Transcripts Differ Significantly Between Full-Length and Minigene Splicing Assays

Lin

Zou

et al. 2021

Front. Genet.

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“…Similarly, a recent study by Invitae aimed to exemplify the utility of RNA analysis for reclassifying splicing VUS (Truty et al, 2021). The investigators analyzed a significantly large sample consisting of nearly 700k patients from a clinical cohort plus individuals from two large public datasets (i.e.…”

Section: Hereditary Cancermentioning

confidence: 99%

“…The investigators analyzed a significantly large sample consisting of nearly 700k patients from a clinical cohort plus individuals from two large public datasets (i.e. ClinVar and Genome Aggregation Database/gnomAD ) (Truty et al, 2021). In their clinical cohort, Invitae found that 5.4% of individuals had at least one splicing VUS (most of which were identified outside of essential splice sites), and that splicing variants represented 13% of all variants classified as (likely) pathogenic or VUS.…”

Section: Hereditary Cancermentioning

confidence: 99%

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Transcriptome analysis provides critical answers to the “variants of uncertain significance” conundrum

Postel

Culver

Ricker

et al. 2022

Preprint

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The vast volume of data that has been generated as a result of the next-generation sequencing revolution is overwhelming to sift through and interpret. Parsing functional vs. non-functional and benign vs. pathogenic variants continues to be a challenge. Out of three billion bases, the genomes of two given individuals will only differ by about 3 million variants (0.1%). Furthermore, only a small fraction of these are biologically-relevant and, of those that are functional, only a handful actually drive disease pathology. While whole genome and exome sequencing have transformed our collective understanding of the role that genetics plays in disease pathogenesis, there are certain conditions and populations for whom DNA-level data has failed to produce a molecular diagnosis. Patients of non-White race/non-European ancestry are disproportionately affected by “variants of unknown/uncertain significance” (VUS). This limits the scope of precision medicine for minority patients and perpetuates health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret in DNA alone. RNA analysis is capable of illuminating the consequences of VUS thereby allowing for their reclassification as pathogenic vs. benign. Here we review the critical role, going forward, of transcriptome analysis for clarifying VUS in both neoplastic and non-neoplastic diseases.

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“…While the accuracy of in silico algorithms in predicting whether a variant will cause mis-splicing has been comprehensively assessed [6][7][8][9] , there is currently no reliable means to predict which mis-splicing event(s) may occur in response to a variant that activates mis-splicing. As a result of this and other factors, the vast majority of splice site variants are classified as variants of uncertain significance (VUS); a non-actionable diagnostic endpoint in genomic medicine 10 .…”

Section: Introductionmentioning

confidence: 99%

Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

Dawes

Joshi

Cooper

2021

Preprint

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Predicting which cryptic-donors may be activated by a genetic variant is notoriously difficult. Through analysis of 5,145 cryptic-donors activated by 4,811 variants (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the authentic-donor appear important determinants of cryptic-donor activation. However, other factors such as auxiliary splicing elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequent mis-splicing events at each exon-intron junction, mined from 40,233 RNA-sequencing samples, predict with remarkable accuracy which cryptic-donor will be activated in rare disease. Aggregate RNA-Sequencing splice-junction data provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

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Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation

Cited by 49 publications

References 52 publications

Splicing Outcomes of 5′ Splice Site GT>GC Variants That Generate Wild-Type Transcripts Differ Significantly Between Full-Length and Minigene Splicing Assays

Splicing Outcomes of 5′ Splice Site GT>GC Variants That Generate Wild-Type Transcripts Differ Significantly Between Full-Length and Minigene Splicing Assays

Transcriptome analysis provides critical answers to the “variants of uncertain significance” conundrum

Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

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