Optimising Genetic Diagnostics: The Role of AION Database in Enhancing Efficiency and Precision

Welcome to another blog post!

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At Nostos Genomics, we are continuously improving the AION platform, driven by user feedback, to enhance the precision and efficiency of their clinical and research workflows. Our latest update introduces a significant enhancement: the AION Database (AION DB). This new feature acts as a laboratory knowledge base that enables users to harness insights from every case and data point added by anyone within their institution or team. Consequently, once a variant is analysed, its data can be effectively reused for future cases, streamlining processes and conserving resources by eliminating the need for repetitive analyses and decision-making.

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Here, we will delve deeper into AION DB, exploring its role as an essential internal database for various organisations and discussing how this feature can substantially improve our users' diagnostic accuracy and workflow efficiency.

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Why an Internal Database Feature is Important: Use Case

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Identifying and interpreting genetic variants accurately is crucial for research and clinical applications¹. Public databases, like the Genome Aggregation Database (gnomAD), provide valuable frequency data on genetic variants across diverse populations, aiding in the assessment of variant rarity and potential significance². However, relying solely on public databases may not fully capture the nuances of specific populations or experimental conditions.

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For example, a research team might be investigating a cohort of patients with a rare disease. They identify a genetic variant with a very low minor allele frequency (MAF) in the gnomAD database, which suggests rarity and potential pathogenicity. However, further evaluation on an internal database shows that the cohort's variant frequency is unexpectedly high (>50%). This discrepancy indicates that the variant may be a technical artefact or a benign polymorphism prevalent in this specific group rather than a disease-related mutation³. Based on these insights, the team can refine their variant filtering strategy to automatically filter out common and cohort-specific non-pathogenic variants, reducing the number of false positives. 

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In such a case, integrating an internal database like AION DB will likely dramatically reduce the number of variants requiring detailed investigation. This streamlining allows for more focused and faster analysis, accelerating the pace of research and increasing the likelihood of identifying truly significant variants.

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Understanding AION DB

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AION DB is designed to centralise the storage of variants within an organisational database. The database can be shared across an entire organisation or dedicated to specific projects, facilitating seamless collaboration among colleagues. Additionally, AION DB supports the integration of historical databases from other sources, allowing users to maintain continuity by adding existing data into the new system. Once the database has been set up and historical data onboarded, new cases run on AION are automatically added to the database in real-time, and statistics and classification can be performed using AION DB’s variant statistics and variant classification features. 

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• Variant statistics: AION DB will remember all the variants it sees and provide users with valuable insights into related variant statistics, such as internal variant frequencies and allele counts, which can be used for filtering while analysing a case.

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• ‍Variant classification: Users can record a final decision by adding classifications and comments about the variant’s pathogenicity. These decisions are institutional and updated in real-time to AION DB. Then, in future cases, these decisions can be leveraged to support more efficient, consistent classifications.

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Key Benefits for Users

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Easier Collaboration and Consistent Interpretation

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AION DB allows all team members to easily see previous decisions and notes related to a variant’s pathogenicity, which can be reviewed and reapplied to new cases. This also allows researchers or clinicians to find instances where a reanalysis might be relevant if the interpretation changes. Ultimately, the ability to quickly review and apply previously made decisions facilitates collaborative decision-making and consistent interpretations across an organisation.

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Classification and Frequency-Powered Artefact Filtering

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Lab-specific factors such as sequencing platforms, sample preparation techniques, and data analysis pipelines can influence the types of artefacts encountered in the data⁴. By utilising AION DB for variant classifications, users can label commonly seen variants as artefacts and disregard them from analysis. Moreover, when using AION DB for variant statistics, users can customise their filtering strategies based on frequency, leading to faster, more precise results.

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Increased Accuracy and Diagnostic Yield

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While publicly available databases, like gnomAD, can be helpful for variant frequency analysis, they may not always reflect the unique genetic makeup of specific cohorts³. Conversely, an internal clinical repository, like AION DB, is built based on the lab's data, often includes information from a particular population or disease cohort, and provides a unique information source tailored to the characteristics of the lab's samples and testing methods. This enhances diagnostic precision and accuracy and ultimately supports a higher diagnostic yield. 

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Improved Efficiency and Turnaround Time

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Ultimately, all of the benefits mentioned in this section lead to improved efficiency, productivity, and turnaround times. By allowing users to apply decisions made previously by them or their colleagues, time and resources are not wasted making the same decision again. Moreover, the ability to classify and filter based on cohort-specific factors saves time investigating artefacts or non-pathogenic variants, and the improved diagnostic accuracy means that turnaround times for diagnosis are enhanced, which is particularly important in cases involving neonatal patients where early diagnosis can have a significant impact on treatment and outcomes^5,6. 

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Summary

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In a recent update, Nostos Genomics has integrated the AION Database feature into the AION platform, enhancing data management for genomic analysis. This feature allows users to store, recall, and apply variant data to new cases, refining diagnostic accuracy and improving efficiency by reducing redundant analyses. This streamlined process accelerates research and improves diagnostic outcomes, particularly important in time-sensitive settings like neonatal care.

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To learn more about how the AION variant interpretation platform, now enhanced with the AION DB feature, can support your research and clinical workflows, get in touch with one of our experts today!

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References:

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1. Marian AJ. Clinical Interpretation and Management of Genetic Variants. JACC Basic Transl Sci. 2020;5(10):1029-1042. doi:10.1016/j.jacbts.2020.05.013

2. Dewey FE, Grove ME, Pan C, et al. Clinical Interpretation and Implications of Whole-Genome Sequencing. JAMA. 2014;311(10):1035. doi:10.1001/jama.2014.1717

3. Maffucci P, Bigio B, Rapaport F, et al. Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis. Proc Natl Acad Sci. 2019;116(3):950-959. doi:10.1073/pnas.1808403116

4. Singh RR. Next-Generation Sequencing in High-Sensitive Detection of Mutations in Tumors. J Mol Diagn. 2020;22(8):994-1007. doi:10.1016/j.jmoldx.2020.04.213

5. Morton G, Thomas S, Roberts P, Clark V, Imrie J, Morrison A. The importance of early diagnosis and views on newborn screening in metachromatic leukodystrophy: results of a Caregiver Survey in the UK and Republic of Ireland. Orphanet J Rare Dis. 2022;17(1):403. doi:10.1186/s13023-022-02550-z

6. Varghese SE, El Otol RHM, Al Olama FS, Elbadawi SAM. The Importance of Early Detection of Genetic Diseases. Dubai Med J. 2021;4(2):133-141. doi:10.1159/000514215

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