Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Identifying Mendelian disease genes with the Variant Effect Scoring Tool

Carter, Hannah, Douville, Christopher, Stenson, Peter D., Cooper, David Neil and Karchin, Rachel 2013. Identifying Mendelian disease genes with the Variant Effect Scoring Tool. BMC Genomics 14 (S3) , S3. 10.1186/1471-2164-14-S3-S3

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Publisher: BioMed Central
ISSN: 1471-2164
Date of First Compliant Deposit: 22 August 2018
Last Modified: 22 Aug 2018 13:01
URI: http://orca.cf.ac.uk/id/eprint/84044

Citation Data

Cited 61 times in Google Scholar. View in Google Scholar

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics