CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Li, Shuang; Velde, K. Joeri van der; Ridder, Dick de; Dijk, Aalt D.J. van; Soudis, Dimitrios; Zwerwer, Leslie R.; Deelen, Patrick; Hendriksen, Dennis; Charbon, Bart; Gijn, Mariëlle van; Abbott, Kristin M.; Sikkema‐Raddatz, Birgit; Diemen, Cleo C. van; Kerstjens-Frederikse, Wilhelmina S.; Sinke, Richard J.; Swertz, Morris A.

doi:10.1101/19012229

Cited by 3 publications

(4 citation statements)

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“…One reason for the low diagnostic yield are variants of uncertain significance that may contribute to pathogenicity, but for which we lack sufficient evidence to determine their role. Databases of variant-tophenotype links like ClinVar [53] and variant prediction tools like CADD [54], Capice [55], and ClinPred [56] can help to annotate variants. Extending the list of disease genes to physiologically relevant enhancers and identifying their target genes will facilitate evaluating the clinical relevance of de novo mutations and their classification as benign or pathogenic.…”

Section: Interpreting Mutations Located In Enhancersmentioning

confidence: 99%

Enhancers in disease: molecular basis and emerging treatment strategies

Claringbould

Zaugg

2021

Trends in Molecular Medicine

108

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Enhancers are genomic sequences that play a key role in regulating tissue-specific gene expression levels. An increasing number of diseases are linked to impaired enhancer function through chromosomal rearrangement, genetic variation within enhancers, or epigenetic modulation. Here, we review how these enhancer disruptions have recently been implicated in congenital disorders, cancers, and common complex diseases and address the implications for diagnosis and treatment. Although further fundamental research into enhancer function, target genes, and context is required, enhancer-targeting drugs and gene editing approaches show great therapeutic promise for a range of diseases. HighlightsEnhancer disruption is increasingly implicated as a disease-driving mechanism. Chromosomal rearrangements can cause an enhancer to drive aberrant gene expression, genetic variants in enhancers can impact a transcription factor binding site, and disease-associated epigenetic changes are enriched in enhancer regions.The three big challenges in enhancer research focus on systematically identifying functional enhancers, their target genes, and the context in which they are active.Bromo-and extra-terminal (BET) inhibitors are a new class of drugs that target enhancers and inhibit gene expression. They are under investigation as treatment for cancer and other diseases.Gene editing techniques elucidate enhancer function and are being used to selectively regulate or mutate disturbed enhancers.

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Section: Interpreting Mutations Located In Enhancersmentioning

confidence: 99%

Enhancers in disease: molecular basis and emerging treatment strategies

Claringbould

Zaugg

2021

Trends in Molecular Medicine

108

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“…Thus, this data cannot be shared due to patient privacy concerns. Training and testing data with label and predictions from CAPICE and tested predictors and the pre-computed scores for all possible SNVs and InDels are available online at Zenodo [51]: https:// zenodo.org/record/3928295 and at GitHub: https://github.com/molgenis/ capice.…”

Section: Availability and Requirementsmentioning

confidence: 99%

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Velde

Ridder

et al. 2020

Genome Med

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Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/ capice.

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“…To examine the clinical utility of KidneyNetwork, we applied GADO 6 to data from 13 patients with a suspected hereditary kidney disease but no genetic diagnosis, using KidneyNetwork as the input matrix. For each patient, we identified which genes prioritized by GADO with KidneyNetwork overlap with genes containing potentially pathogenic variants predicted by CAPICE 22 . The resulting gene lists contained 1−4 candidate genes for 9 of the 13 patients (supplementary table 6) .…”

Section: Resultsmentioning

confidence: 99%

“…Based on their phenotype, HPO terms were assigned to these cases by two physicians from the genetics department. For each patient, the complete exome sequencing data were reanalyzed using CAPICE 22 to identify potentially pathogenic variants. Genes containing variants with a minor allele frequency (MAF) < 0.005 and a recall ≥ 99%, corresponding with a mild CAPICE cut-off of ≥ 0.0027, were considered interesting candidates.…”

Section: Methodsmentioning

confidence: 99%

KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

Boulogne

Claus

Wiersma

et al. 2021

Preprint

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Background: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions. Methods: KidneyNetwork is a co-expression network built upon a combination of 878 kidney RNA-sequencing samples and a multi-tissue dataset of 31,499 samples. It uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis. Results: KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to identify a promising candidate gene for kidney and liver cysts: ALG6. Conclusion: We present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes in order to better understand kidney physiology and pathophysiology.

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CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Cited by 3 publications

References 50 publications

Enhancers in disease: molecular basis and emerging treatment strategies

Enhancers in disease: molecular basis and emerging treatment strategies

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

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