Gene variant
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Gene Variant Interpretation and Analysis: Key Tools and Methods
Gene-Aware Variant Interpretation (GAVIN)
GAVIN is a novel method designed to classify genetic variants for clinical diagnostics with high accuracy. It utilizes gene-specific calibrations of allele frequencies from the ExAC database, variant impact predictions from SnpEff, and deleteriousness estimates from CADD scores for over 3,000 genes. GAVIN achieves a sensitivity of 91.4% and a specificity of 76.9%, outperforming 12 other tools in benchmark tests on 18 clinical gene sets. This tool is available as an online service and an open-source executable, making it accessible for bioinformatic pipelines1.
Large-Scale Genetic Variation Analysis
The Exome Aggregation Consortium (ExAC) has aggregated high-quality exome sequence data from 60,706 individuals of diverse ancestries. This extensive dataset reveals an average of one variant every eight bases of the exome, highlighting widespread mutational recurrence. The data is instrumental in calculating pathogenicity metrics for sequence variants and identifying genes under strong selection against mutations. Notably, 3,230 genes show near-complete depletion of protein-truncating variants, with 72% lacking an established human disease phenotype. This resource is crucial for filtering candidate disease-causing variants and discovering human 'knockout' variants2.
ANNOVAR: Functional Annotation of Genetic Variants
ANNOVAR is a tool developed to annotate single nucleotide variants (SNVs) and insertions/deletions from high-throughput sequencing data. It assesses the functional consequences of variants on genes, reports functional importance scores, and identifies variants in conserved regions. ANNOVAR can handle large datasets efficiently, making it practical for analyzing hundreds of human genomes daily. It has been successfully used to identify causal mutations for diseases like Miller syndrome by reducing the number of candidate variants through a stepwise exclusion process3.
Mutation Significance Cutoff for Variant Predictions
The Mutation Significance Cutoff method addresses the challenge of interpreting the pathogenicity of variants identified through next-generation sequencing (NGS). Traditional variant-level methods like PolyPhen-2, SIFT, and CADD use fixed cutoffs, which may not be accurate across all genes due to genetic diversity. This method combines gene-level and variant-level cutoffs to improve the accuracy of pathogenicity predictions, considering the medical and population genetic features of human genes4.
Trans-Acting Variants and Gene Expression
Trans-acting variants, which affect gene expression from different chromosomes, are a significant source of heritable gene expression variation. A study using the CRISPR-Swap strategy in yeast identified three such variants, each with distinct molecular mechanisms and evolutionary histories. These findings underscore the complexity of predicting which natural genetic variants will impact gene expression, highlighting the need for advanced mapping and validation techniques5.
Functional Interpretation of Genetic Variants
Understanding the biological mechanisms by which genetic variants influence phenotypes is a primary challenge in human genetics. New methods for functional variant interpretation, using patient tissue samples and in vitro models, are being applied to dissect variant mechanisms across various cell types and environments. These approaches are increasingly used in clinical settings to improve disease diagnosis, risk prediction, and therapy development6.
Predicting Disease-Causing Variant Combinations
The Variant Combinations Pathogenicity Predictor (VarCoPP) is a machine-learning method designed to predict the pathogenicity of variant combinations in gene pairs. This tool is particularly useful for identifying genetic causes of rare diseases by assessing the pathogenicity of digenic or bilocus variant combinations. VarCoPP provides high accuracy and explicit decision-making processes, aiding geneticists and clinicians in patient counseling and diagnosis7.
Tools for Mendelian Disease Gene Identification
The PhenoDB Variant Analysis Module and GeneMatcher are tools developed to streamline the identification of causative variants from whole-exome or whole-genome sequencing data. PhenoDB integrates phenotype data with variant analysis, while GeneMatcher connects clinicians and researchers interested in the same genes, facilitating the discovery of gene-disease associations and biological evidence for causality8.
Variant Effect Scoring Tool (VEST)
The Variant Effect Scoring Tool (VEST) is a machine-learning classifier that prioritizes rare missense variants likely involved in human disease. VEST outperforms other methods like PolyPhen-2 and SIFT in benchmarking experiments. It aggregates variant scores into gene-level scores, enhancing the identification of candidate Mendelian disease genes from exome sequencing data. VEST is available as a stand-alone software and through the CRAVAT web server9.
Genome Aggregation Database (gnomAD)
The Genome Aggregation Database (gnomAD) aggregates exome and genome data from 141,456 individuals, identifying 443,769 high-confidence predicted loss-of-function (pLoF) variants. This database classifies human protein-coding genes based on their tolerance to inactivation, validated through model organisms and engineered human cells. gnomAD improves gene discovery power for both common and rare diseases by providing a comprehensive resource for genetic variant analysis10.
Conclusion
The landscape of genetic variant interpretation and analysis is rapidly evolving with the development of advanced tools and large-scale datasets. Methods like GAVIN, ANNOVAR, and VEST, along with resources like ExAC and gnomAD, are enhancing our ability to identify and understand the pathogenicity of genetic variants. These advancements are crucial for improving disease diagnosis, risk prediction, and the development of targeted therapies.
Sources and full results
Most relevant research papers on this topic
GAVIN: Gene-Aware Variant INterpretation for medical sequencing
GAVIN accurately classifies variants for clinical diagnostic purposes, with a sensitivity of 91.4% and specificity of 76.9%, outperforming 12 other tools in a benchmark.
Rigorous JournalAnalysis of protein-coding genetic variation in 60,706 humans
This large-scale exome data set reveals widespread mutational recurrence and can efficiently filter disease-causing variants and discover human 'knockout' variants in protein-coding genes.
Rigorous Journal
Highly CitedANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
ANNOVAR is a free tool that efficiently annotates genetic variants from high-throughput sequencing data, enabling functional analysis of genes and their consequences.
Highly CitedEstimation statistics should replace significance testing
A uniform gene-level cutoff is unlikely to be accurate across all protein-coding genes due to the diversity of medical and population genetic features between human genes and populations.
Rigorous JournalHighly CitedDNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories
Three single DNA base changes in baker's yeast significantly influence gene expression, highlighting the complexity of predicting natural genetic variants that affect gene expression.
From variant to function in human disease genetics
Better methods for understanding the biological mechanisms of genetic variants can revolutionize disease diagnosis, risk prediction, and therapy development.
Highly CitedPredicting disease-causing variant combinations
The Variant Combinations Pathogenicity Predictor (VarCoPP) accurately predicts disease-causing variant combinations in gene pairs, aiding geneticists and clinicians in better understanding rare diseases and improving patient care.
Highly CitedNew Tools for Mendelian Disease Gene Identification: PhenoDB Variant Analysis Module; and GeneMatcher, a Web‐Based Tool for Linking Investigators with an Interest in the Same Gene
The Variant Analysis Module and GeneMatcher tools help identify causative variants in mendelian diseases and connect clinicians and researchers with shared interests in the same gene.
Rigorous JournalHighly CitedIdentifying Mendelian disease genes with the Variant Effect Scoring Tool
The Variant Effect Scoring Tool (VEST) effectively prioritizes rare missense variants in human diseases, identifying potential Mendelian disease genes in large-scale exome sequencing studies.
Rigorous JournalHighly CitedThe mutational constraint spectrum quantified from variation in 141,456 humans
This study identified 443,769 high-confidence predicted loss-of-function variants in human protein-coding genes, improving gene discovery power for both common and rare diseases.
Rigorous JournalHighly Cited