Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities
Abstract The accurate diagnosis of pathogenic variants is essential for effective clinical decision making within precision medicine programs. Despite significant advances in both the quality and quantity of molecular patient data, diagnostic rates remain suboptimal for many inherited diseases. As s...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | Human Genomics |
| Online Access: | https://doi.org/10.1186/s40246-025-00811-z |
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| author | Sonam Dukda Manoharan Kumar Andrew Calcino Ulf Schmitz Matt A. Field |
| author_facet | Sonam Dukda Manoharan Kumar Andrew Calcino Ulf Schmitz Matt A. Field |
| author_sort | Sonam Dukda |
| collection | DOAJ |
| description | Abstract The accurate diagnosis of pathogenic variants is essential for effective clinical decision making within precision medicine programs. Despite significant advances in both the quality and quantity of molecular patient data, diagnostic rates remain suboptimal for many inherited diseases. As such, prioritisation and identification of pathogenic disease-causing variants remains a complex and rapidly evolving field. This review explores the latest technological and computational options being used to increase genetic diagnosis rates in precision medicine programs. While interpreting genetic variation via standards such as ACMG guidelines is increasingly being recognized as a gold standard approach, the underlying datasets and algorithms recommended are often slow to incorporate additional data types and methodologies. For example, new technological developments, particularly in single-cell and long-read sequencing, offer great opportunity to improve genetic diagnosis rates, however, how to best interpret and integrate increasingly complex multi-omics patient data remains unclear. Further, advances in artificial intelligence and machine learning applications in biomedical research offer enormous potential, however they require careful consideration and benchmarking given the clinical nature of the data. This review covers the current state of the art in available sequencing technologies, software methodologies for variant annotation/prioritisation, pedigree-based strategies and the potential role of machine learning applications. We describe a key set of design principles required for a modern multi-omic precision medicine framework that is robust, modular, secure, flexible, and scalable. Creating a next generation framework will ensure we realise the full potential of precision medicine into the future. |
| format | Article |
| id | doaj-art-27a0421f1bb1463fa51b9c54bcb17fe9 |
| institution | Kabale University |
| issn | 1479-7364 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj-art-27a0421f1bb1463fa51b9c54bcb17fe92025-08-24T11:39:55ZengBMCHuman Genomics1479-73642025-08-0119111610.1186/s40246-025-00811-zIncreasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunitiesSonam Dukda0Manoharan Kumar1Andrew Calcino2Ulf Schmitz3Matt A. Field4Centre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook UniversityCentre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook UniversityCentre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook UniversityCentre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook UniversityCentre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook UniversityAbstract The accurate diagnosis of pathogenic variants is essential for effective clinical decision making within precision medicine programs. Despite significant advances in both the quality and quantity of molecular patient data, diagnostic rates remain suboptimal for many inherited diseases. As such, prioritisation and identification of pathogenic disease-causing variants remains a complex and rapidly evolving field. This review explores the latest technological and computational options being used to increase genetic diagnosis rates in precision medicine programs. While interpreting genetic variation via standards such as ACMG guidelines is increasingly being recognized as a gold standard approach, the underlying datasets and algorithms recommended are often slow to incorporate additional data types and methodologies. For example, new technological developments, particularly in single-cell and long-read sequencing, offer great opportunity to improve genetic diagnosis rates, however, how to best interpret and integrate increasingly complex multi-omics patient data remains unclear. Further, advances in artificial intelligence and machine learning applications in biomedical research offer enormous potential, however they require careful consideration and benchmarking given the clinical nature of the data. This review covers the current state of the art in available sequencing technologies, software methodologies for variant annotation/prioritisation, pedigree-based strategies and the potential role of machine learning applications. We describe a key set of design principles required for a modern multi-omic precision medicine framework that is robust, modular, secure, flexible, and scalable. Creating a next generation framework will ensure we realise the full potential of precision medicine into the future.https://doi.org/10.1186/s40246-025-00811-z |
| spellingShingle | Sonam Dukda Manoharan Kumar Andrew Calcino Ulf Schmitz Matt A. Field Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities Human Genomics |
| title | Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities |
| title_full | Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities |
| title_fullStr | Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities |
| title_full_unstemmed | Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities |
| title_short | Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities |
| title_sort | increasing pathogenic germline variant diagnosis rates in precision medicine current best practices and future opportunities |
| url | https://doi.org/10.1186/s40246-025-00811-z |
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