Problems in variation interpretation guidelines and in their implementation in computational tools
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 p...
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| Format: | Article |
| Language: | English |
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Wiley
2020-09-01
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| Series: | Molecular Genetics & Genomic Medicine |
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| Online Access: | https://doi.org/10.1002/mgg3.1206 |
| _version_ | 1797301122663186432 |
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| author | Mauno Vihinen |
| author_facet | Mauno Vihinen |
| author_sort | Mauno Vihinen |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-03-07T23:16:53Z |
| format | Article |
| id | doaj.art-5867d6f1c99f4f90b17d8c3d420d1970 |
| institution | Directory Open Access Journal |
| issn | 2324-9269 |
| language | English |
| last_indexed | 2024-03-07T23:16:53Z |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Genetics & Genomic Medicine |
| spelling | doaj.art-5867d6f1c99f4f90b17d8c3d420d19702024-02-21T10:24:50ZengWileyMolecular Genetics & Genomic Medicine2324-92692020-09-0189n/an/a10.1002/mgg3.1206Problems in variation interpretation guidelines and in their implementation in computational toolsMauno Vihinen0Department of Experimental Medical Science Lund University Lund SwedenAbstract 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.https://doi.org/10.1002/mgg3.1206ACMG/AMP guidelinesbenchmark datasetscontinuum of diseasemajority votepathogenicity modelpathogenicity prediction |
| spellingShingle | Mauno Vihinen Problems in variation interpretation guidelines and in their implementation in computational tools Molecular Genetics & Genomic Medicine ACMG/AMP guidelines benchmark datasets continuum of disease majority vote pathogenicity model pathogenicity prediction |
| title | Problems in variation interpretation guidelines and in their implementation in computational tools |
| title_full | Problems in variation interpretation guidelines and in their implementation in computational tools |
| title_fullStr | Problems in variation interpretation guidelines and in their implementation in computational tools |
| title_full_unstemmed | Problems in variation interpretation guidelines and in their implementation in computational tools |
| title_short | Problems in variation interpretation guidelines and in their implementation in computational tools |
| title_sort | problems in variation interpretation guidelines and in their implementation in computational tools |
| topic | ACMG/AMP guidelines benchmark datasets continuum of disease majority vote pathogenicity model pathogenicity prediction |
| url | https://doi.org/10.1002/mgg3.1206 |
| work_keys_str_mv | AT maunovihinen problemsinvariationinterpretationguidelinesandintheirimplementationincomputationaltools |