Identifying Mendelian disease genes with the Variant Effect Scoring Tool View Full Text


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Article Info

DATE

2013-05

AUTHORS

Hannah Carter, Christopher Douville, Peter D Stenson, David N Cooper, Rachel Karchin

ABSTRACT

BACKGROUND: Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease. RESULTS: We have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency >1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome. CONCLUSIONS: Our results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us. More... »

PAGES

s3

References to SciGraph publications

  • 2010-01. Exome sequencing identifies the cause of a mendelian disorder in NATURE GENETICS
  • 2010-08. MutationTaster evaluates disease-causing potential of sequence alterations in NATURE METHODS
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2006-05. Mutations in embryonic myosin heavy chain (MYH3) cause Freeman-Sheldon syndrome and Sheldon-Hall syndrome in NATURE GENETICS
  • 2010-06. De novo mutations of SETBP1 cause Schinzel-Giedion syndrome in NATURE GENETICS
  • 2011-03-29. Next-generation association studies for complex traits in NATURE GENETICS
  • 2007-10. The NCBI dbGaP database of genotypes and phenotypes in NATURE GENETICS
  • 2009-09-10. Targeted capture and massively parallel sequencing of 12 human exomes in NATURE
  • 2010-10-28. A map of human genome variation from population-scale sequencing in NATURE
  • 2009-07. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm in NATURE PROTOCOLS
  • 2011-09. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data in NATURE REVIEWS GENETICS
  • 2010-12. Overcoming bias and systematic errors in next generation sequencing data in GENOME MEDICINE
  • 2009-01. The Human Gene Mutation Database: 2008 update in GENOME MEDICINE
  • 2011-05. A framework for variation discovery and genotyping using next-generation DNA sequencing data in NATURE GENETICS
  • 2012-12. An integrative variant analysis suite for whole exome next-generation sequencing data in BMC BIOINFORMATICS
  • 2010-01. Using transcriptomics to identify and validate novel biomarkers of human skeletal muscle cancer cachexia in GENOME MEDICINE
  • 2010-04. A method and server for predicting damaging missense mutations in NATURE METHODS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2164-14-s3-s3

    DOI

    http://dx.doi.org/10.1186/1471-2164-14-s3-s3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023508853

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23819870


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