Hypomorphic alleles pose challenges in rare disease genomic variant interpretation

2021 ◽  
Author(s):  
Daniel K. Nolan ◽  
Bimal Chaudhari ◽  
Samuel J. Franklin ◽  
Saranga Wijeratne ◽  
Ruthann Pfau ◽  
...  
Keyword(s):  
Rare Disease ◽  
Variant Interpretation ◽  
Hypomorphic Alleles ◽  
Genomic Variant

scholarly journals GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

2020 ◽  
Author(s):  
Roozbeh Manshaei ◽  
Sean DeLong ◽  
Veronica Andric ◽  
Esha Joshi ◽  
John B. A. Okello ◽  
...  
Keyword(s):  
Integrated System ◽  
Variant Interpretation ◽  
Expression Data ◽  
Variant Prioritization ◽  
Computational Tools ◽  
Phenotype Data ◽  
Causal Variants ◽  
Interpretation Process ◽  
Genomic Variant ◽  
Speed And Accuracy

ABSTRACTVariant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually scouring through a multitude of independent databases, often with the aid of several and mostly independent computational tools.To streamline the variant interpretation process, we developed GeneTerpret platform that collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in a given genome. The platform assigns quantitative validity scores to genes by query and assembly of the current genotype-phenotype data, sequence homology, molecular interactions, expression data, and animal models. The platform uses the American College of Medical Genetics (ACMG) criteria to categorize variants into five tiers (from benign to pathogenic). The platform then outputs a prioritized list of potentially causal variants/genes in a given genome for a specific case.GeneTerpret is a flexible and free platform designed to streamline the variant interpretation process through a unique interface, with improved ease, speed and accuracy. This unique integrated system provides effective validity and pathogenicity modules to assess genetic variant data and allows the user to decide which output and impact level should be considered in this process. The platform can be accessed and used online at https://geneterpret.com.

scholarly journals Assessing performance of pathogenicity predictors using clinically-relevant variant datasets

2020 ◽  
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Variant Classification ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Diagnostic Panel ◽  
Genomic Variant ◽  
Insight Into ◽  
Panel Sequencing

ABSTRACTPurposePathogenicity predictors are an integral part of genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically-relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an “open” dataset containing variants extracted from publicly-available databases, similar to those commonly applied in previous benchmarking exercises, and a “clinically-representative” dataset containing variants identified through research/diagnostic exome and diagnostic panel sequencing. Using these datasets, we evaluate the performance of three recently developed meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically-representative dataset. Using our clinically-relevant dataset, REVEL performed best with an area under the ROC of 0.81. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as recommended by current variant classification guidelines.

scholarly journals Keeping up with the genomes: scaling genomic variant interpretation

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Heidi L. Rehm ◽  
Douglas M. Fowler
Keyword(s):  
Variant Interpretation ◽  
Genomic Variant

scholarly journals Unmet needs in human genomic variant interpretation

2017 ◽  
Vol 20 (3) ◽  
pp. 376-377 ◽  
Author(s):  
Peter Bauer ◽  
Ellen Karges ◽  
Gabriela Oprea ◽  
Arndt Rolfs
Keyword(s):  
Unmet Needs ◽  
Variant Interpretation ◽  
Human Genomic ◽  
Genomic Variant

scholarly journals DECIPHER: Supporting the interpretation and sharing of rare disease phenotype-linked variant data to advance diagnosis and research

2021 ◽  
Author(s):  
Julia Foreman ◽  
Simon Brent ◽  
Daniel Perrett ◽  
Andrew Bevan ◽  
Sarah Hunt ◽  
...  
Keyword(s):  
Rare Disease ◽  
Tandem Repeats ◽  
Copy Number Variants ◽  
International Standards ◽  
Variant Interpretation ◽  
Phenotypic Data ◽  
Human Phenotype ◽  
The Rich ◽  
Almost All ◽  
Short Tandem

DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymised phenotype-linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualise genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy-number variants and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene-specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing ~40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype-linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this paper, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource.

scholarly journals Assessing performance of pathogenicity predictors using clinically relevant variant datasets

2020 ◽  
pp. jmedgenet-2020-107003
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Operating Characteristic ◽  
Current Practice ◽  
Characteristic Curve ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Genomic Variant ◽  
Insight Into ◽  
In Silico Tools

BackgroundPathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an ‘open’ dataset containing variants extracted from publicly available databases, similar to those commonly applied in previous benchmarking exercises, and a ‘clinically representative’ dataset containing variants identified through research/diagnostic exome and panel sequencing. Using these datasets, we evaluate the performance of three recent meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically representative dataset. Using our clinically relevant dataset, REVEL performed best with an area under the receiver operating characteristic curve of 0.82. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as in current practice.

scholarly journals Precision oncology: separating the wheat from the chaff

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000446 ◽  
Author(s):  
Jordi Remon ◽  
Rodrigo Dienstmann
Keyword(s):  
Clinical Practice ◽  
Next Generation Sequencing ◽  
Large Scale ◽  
Precision Oncology ◽  
Variant Interpretation ◽  
The Real ◽  
Next Generation Sequencing Ngs ◽  
Genomic Variant ◽  
Generation Sequencing

Precision oncology based on next-generation sequencing (NGS) test is growing in daily clinical practice. However, the real impact of this strategy in patients’ outcome on a large scale remains uncertain. In this review, we summarise existing literature on this topic, limitations for broad NGS implementation, bottlenecks in genomic variant interpretation and the role of molecular tumour boards.

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