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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations.

Benjamin J Livesey1, Joseph A Marsh1

  • 1MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Molecular Systems Biology
|July 7, 2020
PubMed
Summary

Deep mutational scanning (DMS) experiments outperform computational variant effect predictors (VEPs) in identifying disease mutations. DeepSequence excelled among VEPs, showing strong correlations with DMS data and superior pathogenic variant prediction.

Keywords:
missense mutationsphenotype predictionprotein structuresaturation mutagenesisvariant effect

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Genome and exome sequencing generate numerous protein-coding variants.
  • Computational variant effect predictors (VEPs) assess variant impact.
  • Lack of standardized datasets hinders direct VEP comparison.

Purpose of the Study:

  • Benchmark and compare 46 VEPs using deep mutational scanning (DMS) data.
  • Evaluate DMS and VEPs for discriminating pathogenic from benign missense variants.
  • Identify top-performing VEPs and assess DMS potential for disease mutation discovery.

Main Methods:

  • Utilized 31 independent deep mutational scanning (DMS) experiments.
  • Collected quantitative phenotypic measurements for single amino acid substitutions.
  • Compared performance of 46 computational variant effect predictors (VEPs).

Main Results:

  • Deep mutational scanning (DMS) experiments demonstrated superior performance over leading VEPs.
  • DeepSequence, an unsupervised method, showed the strongest correlation with DMS data and best pathogenic variant prediction.
  • Recommended SNAP2, DEOGEN2, SNPs&GO, SuSPect, and REVEL based on performance.

Conclusions:

  • Deep mutational scanning (DMS) holds significant potential for identifying novel human disease mutations.
  • DeepSequence is a highly effective VEP for predicting pathogenic variants.
  • Specific VEPs show promise for variant interpretation in clinical genomics.