Problems in variation interpretation guidelines and in their implementation in computational tools
Vihinen, Mauno LU
(2020)
In Molecular Genetics and Genomic Medicine
8(9).
- Abstract
Background: ACMG/AMP and AMP/ASCO/CAP have released guidelines for variation interpretation, and ESHG for diagnostic sequencing. These guidelines contain recommendations including the use of computational prediction methods. The guidelines per se and the way they are implemented cause some problems. Methods: Logical reasoning based on domain knowledge. Results: According to the guidelines, several methods have to be used and they have to agree. This means that the methods with the poorest performance overrule the better ones. The choice of the prediction method(s) should be made by experts based on systematic benchmarking studies reporting all the relevant performance measures. Currently variation interpretation methods have been... (More)
Background: ACMG/AMP and AMP/ASCO/CAP have released guidelines for variation interpretation, and ESHG for diagnostic sequencing. These guidelines contain recommendations including the use of computational prediction methods. The guidelines per se and the way they are implemented cause some problems. Methods: Logical reasoning based on domain knowledge. Results: According to the guidelines, several methods have to be used and they have to agree. This means that the methods with the poorest performance overrule the better ones. The choice of the prediction method(s) should be made by experts based on systematic benchmarking studies reporting all the relevant performance measures. Currently variation interpretation methods have been applied mainly to amino acid substitutions and splice site variants; however, predictors for some other types of variations are available and there will be tools for new application areas in the near future. Common problems in prediction method usage are discussed. The number of features used for method training or the number of variation types predicted by a tool are not indicators of method performance. Many published gene, protein or disease-specific benchmark studies suffer from too small dataset rendering the results useless. In the case of binary predictors, equal number of positive and negative cases is beneficial for training, the imbalance has to be corrected for performance assessment. Predictors cannot be better than the data they are based on and used for training and testing. Minor allele frequency (MAF) can help to detect likely benign cases, but the recommended MAF threshold is apparently too high. The fact that many rare variants are disease-causing or -related does not mean that rare variants in general would be harmful. How large a portion of the tested variants a tool can predict (coverage) is not a quality measure. Conclusion: Methods used for variation interpretation have to be carefully selected. It should be possible to use only one predictor, with proven good performance or a limited number of complementary predictors with state-of-the-art performance. Bear in mind that diseases and pathogenicity have a continuum and variants are not dichotomic i.e. either pathogenic or benign, either.
(Less)
- author
- Vihinen, Mauno
LU
- organization
- publishing date
- 2020-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ACMG/AMP guidelines, benchmark datasets, continuum of disease, majority vote, pathogenicity model, pathogenicity prediction, prediction methods, variation interpretation
- in
- Molecular Genetics and Genomic Medicine
- volume
- 8
- issue
- 9
- article number
- e1206
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:32160417
- scopus:85081313896
- ISSN
- 2324-9269
- DOI
- 10.1002/mgg3.1206
- language
- English
- LU publication?
- yes
- id
- 69d7b580-a619-45be-ad7b-ac5fa45269e2
- date added to LUP
- 2020-04-10 10:23:12
- date last changed
- 2024-03-20 07:20:26
@article{69d7b580-a619-45be-ad7b-ac5fa45269e2,
abstract = {{<p>Background: ACMG/AMP and AMP/ASCO/CAP have released guidelines for variation interpretation, and ESHG for diagnostic sequencing. These guidelines contain recommendations including the use of computational prediction methods. The guidelines per se and the way they are implemented cause some problems. Methods: Logical reasoning based on domain knowledge. Results: According to the guidelines, several methods have to be used and they have to agree. This means that the methods with the poorest performance overrule the better ones. The choice of the prediction method(s) should be made by experts based on systematic benchmarking studies reporting all the relevant performance measures. Currently variation interpretation methods have been applied mainly to amino acid substitutions and splice site variants; however, predictors for some other types of variations are available and there will be tools for new application areas in the near future. Common problems in prediction method usage are discussed. The number of features used for method training or the number of variation types predicted by a tool are not indicators of method performance. Many published gene, protein or disease-specific benchmark studies suffer from too small dataset rendering the results useless. In the case of binary predictors, equal number of positive and negative cases is beneficial for training, the imbalance has to be corrected for performance assessment. Predictors cannot be better than the data they are based on and used for training and testing. Minor allele frequency (MAF) can help to detect likely benign cases, but the recommended MAF threshold is apparently too high. The fact that many rare variants are disease-causing or -related does not mean that rare variants in general would be harmful. How large a portion of the tested variants a tool can predict (coverage) is not a quality measure. Conclusion: Methods used for variation interpretation have to be carefully selected. It should be possible to use only one predictor, with proven good performance or a limited number of complementary predictors with state-of-the-art performance. Bear in mind that diseases and pathogenicity have a continuum and variants are not dichotomic i.e. either pathogenic or benign, either.</p>}},
author = {{Vihinen, Mauno}},
issn = {{2324-9269}},
keywords = {{ACMG/AMP guidelines; benchmark datasets; continuum of disease; majority vote; pathogenicity model; pathogenicity prediction; prediction methods; variation interpretation}},
language = {{eng}},
number = {{9}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Molecular Genetics and Genomic Medicine}},
title = {{Problems in variation interpretation guidelines and in their implementation in computational tools}},
url = {{http://dx.doi.org/10.1002/mgg3.1206}},
doi = {{10.1002/mgg3.1206}},
volume = {{8}},
year = {{2020}},
}