Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities
Dukda, Sonam, Kumar, Manoharan, Calcino, Andrew, Schmitz, Ulf, and Field, Matt A. (2025) Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities. Human Genomics, 19 (1). 97.
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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.
| Item ID: | 87717 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1479-7364 |
| Copyright Information: | © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 29 Jan 2026 02:19 |
| FoR Codes: | 32 BIOMEDICAL AND CLINICAL SCIENCES > 3207 Medical microbiology > 320702 Medical infection agents (incl. prions) @ 100% |
| SEO Codes: | 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100% |
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