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How good are pathogenicity predictors in detecting benign variants?

Niroula, Abhishek LU and Vihinen, Mauno LU (2019) In PLoS Computational Biology 15(2).
Abstract

Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to... (More)

Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS Computational Biology
volume
15
issue
2
article number
e1006481
publisher
Public Library of Science
external identifiers
  • scopus:85062016592
  • pmid:30742610
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1006481
language
English
LU publication?
yes
id
28b54977-6bbb-4845-937d-f74783db83e6
date added to LUP
2019-03-06 13:02:50
date last changed
2020-02-12 09:55:34
@article{28b54977-6bbb-4845-937d-f74783db83e6,
  abstract     = {<p>Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and &lt;25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.</p>},
  author       = {Niroula, Abhishek and Vihinen, Mauno},
  issn         = {1553-7358},
  language     = {eng},
  month        = {02},
  number       = {2},
  publisher    = {Public Library of Science},
  series       = {PLoS Computational Biology},
  title        = {How good are pathogenicity predictors in detecting benign variants?},
  url          = {http://dx.doi.org/10.1371/journal.pcbi.1006481},
  doi          = {10.1371/journal.pcbi.1006481},
  volume       = {15},
  year         = {2019},
}