GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Velde, K. Joeri van der; Boer, Eddy N. de; Diemen, Cleo C. van; Sikkema‐Raddatz, Birgit; Abbott, Kristin M.; Knopperts, Alain; Franke, Lude; Sijmons, Rolf H.; Koning, Tom J. de; Wijmenga, Cisca; Sinke, Richard J.; Swertz, Morris A.

doi:10.1101/072330

Cited by 4 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, other methods such as GAVIN suggest that, for PTEN, CADD Phred scores above 29.3 predict that the variant is damaging, and below 17.33 as benign. Using these criteria, only 13 of the 97 variants were assessed as damaging by this criteria, a significant under-representation of the variants found to be damaging according the wing and HEK assays [76] (Fig 7A). PolyPhen-2 uses a naïve Bayes model whose output is binary, and the scores are derived from the posterior probability.…”

Section: Comparison Of Wing Size Scores To Computational Predictionsmentioning

confidence: 99%

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

et al. 2021

View full text Add to dashboard Cite

Gene variant discovery is becoming routine, but it remains difficult to usefully interpret the functional consequence or disease relevance of most variants. To fill this interpretation gap, experimental assays of variant function are becoming common place. Yet, it remains challenging to make these assays reproducible, scalable to high numbers of variants, and capable of assessing defined gene-disease mechanism for clinical interpretation aligned to the ClinGen Sequence Variant Interpretation (SVI) Working Group guidelines for ‘well-established assays’. Drosophila melanogaster offers great potential as an assay platform, but was untested for high numbers of human variants adherent to these guidelines. Here, we wished to test the utility of Drosophila as a platform for scalable well-established assays. We took a genetic interaction approach to test the function of ~100 human PTEN variants in cancer-relevant suppression of PI3K/AKT signaling in cellular growth and proliferation. We validated the assay using biochemically characterized PTEN mutants as well as 23 total known pathogenic and benign PTEN variants, all of which the assay correctly assigned into predicted functional categories. Additionally, function calls for these variants correlated very well with our recent published data from a human cell line. Finally, using these pathogenic and benign variants to calibrate the assay, we could set readout thresholds for clinical interpretation of the pathogenicity of 70 other PTEN variants. Overall, we demonstrate that Drosophila offers a powerful assay platform for clinical variant interpretation, that can be used in conjunction with other well-established assays, to increase confidence in the accurate assessment of variant function and pathogenicity.

show abstract

Section: Comparison Of Wing Size Scores To Computational Predictionsmentioning

confidence: 99%

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We first observed that the CADD and SNAP2 scores showed a continuous range and correlated only modestly with MCF10A scores (Pearson's r ¼ 0.4483 and 0.4533, respectively). CADD proposes (but does not recommend) a genome-wide cutoff of 15, above which, a variant is likely to be deleterious (7), although alternative methods such as MSC (38) and GAVIN (39) give a PTENspecific cutoff of 18.97 and 17.33, respectively. Using a lower cutoff of 18.5, only two variants were considered benign according to CADD.…”

Section: Using Lof Scores and Machine Learning To Predict Pathogenicity Of Pten Variantsmentioning

confidence: 99%

A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants

et al. 2020

View full text Add to dashboard Cite

As sequencing becomes more economical, we are identifying sequence variations in the population faster than ever. For diseaseassociated genes, it is imperative that we differentiate a sequence variant as either benign or pathogenic, such that the appropriate therapeutic interventions or surveillance can be implemented. PTEN is a frequently mutated tumor suppressor that has been linked to the PTEN hamartoma tumor syndrome. Although the domain structure of PTEN and the functional impact of a number of its most common tumor-linked mutations have been characterized, there is a lack of information about many recently identified clinical variants. To address this challenge, we developed a cell-based assay that utilizes a premalignant phenotype of normal mammary epithelial cells lacking PTEN. We measured the ability of PTEN variants to rescue the spheroid formation phenotype of PTEN À/À MCF10A cells maintained in suspension. As proof of concept, we functionalized 47 missense variants using this assay, only 19 of which have clear classifications in ClinVar. We utilized a machine learning model trained with annotated genotypic data to classify variants as benign or pathogenic based on our functional scores. Our model predicted with high accuracy that loss of PTEN function was indicative of pathogenicity. We also determined that the pathogenicity of certain variants may have arisen from reduced stability of the protein product. Overall, this assay outperformed computational predictions, was scalable, and had a short run time, serving as an ideal alternative for annotating the clinical significance of cancer-associated PTEN variants.

show abstract

“…SNPEff (Geoffroy et al, 2015;van der Velde et al, 2017), an annotation tool that can predict the effects due to variations in the genetic code that might result in amino acid changes, was used to enhance the annotations in the VCF files. Additionally, vcfanno (Pedersen, Layer, & Quinlan, 2016) was used to annotate VCF file with extensive available data resources like the Genome Aggregation Database (gnomAD) (Lek et al, 2016), 1,000 genome (Clarke et al, 2017), Combined Annotation-Dependent Depletion (CADD) (Rentzsch, Witten, Cooper, Shendure, & Kircher, 2018), and GERP (Paila, Chapman, Kirchner, & Quinlan, 2013).…”

Section: Incidence Of Pndmmentioning

confidence: 99%

The clinical and genetic characteristics of permanent neonatal diabetes (PNDM) in the state of Qatar

Al‐Khawaga

Mohammed

Saraswathi

et al. 2019

Molec Gen & Gen Med

View full text Add to dashboard Cite

BackgroundNeonatal diabetes mellitus (NDM) is a rare condition that occurs within the first six months of life. Permanent NDM (PNDM) is caused by mutations in specific genes that are known for their expression at early and/or late stages of pancreatic beta‐ cell development, and are either involved in beta‐cell survival, insulin processing, regulation, and release. The native population in Qatar continues to practice consanguineous marriages that lead to a high level of homozygosity. To our knowledge, there is no previous report on the genomics of NDM among the Qatari population. The aims of the current study are to identify patients with NDM diagnosed between 2001 and 2016, and examine their clinical and genetic characteristics.MethodsTo calculate the incidence of PNDM, all patients with PNDM diagnosed between 2001 and 2016 were compared to the total number of live births over the 16‐year‐period. Whole Genome Sequencing (WGS) was used to investigate the genetic etiology in the PNDM cohort.ResultsPNDM was diagnosed in nine (n = 9) patients with an estimated incidence rate of 1:22,938 live births among the indigenous Qatari. Seven different mutations in six genes (PTF1A, GCK, SLC2A2, EIF2AK3, INS, and HNF1B) were identified. In the majority of cases, the genetic etiology was part of a previously identified autosomal recessive disorder. Two novel de novo mutations were identified in INS and HNF1B.ConclusionQatar has the second highest reported incidence of PNDM worldwide. A majority of PNDM cases present as rare familial autosomal recessive disorders. Pancreas associated transcription factor 1a (PTF1A) enhancer deletions are the most common cause of PNDM in Qatar, with only a few previous cases reported in the literature.

show abstract

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Cited by 4 publications

References 38 publications

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants

The clinical and genetic characteristics of permanent neonatal diabetes (PNDM) in the state of Qatar

Contact Info

Product

Resources

About