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Schuetz Dissertation (OhioLINK_v2).pdf (8.55 MB)

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From Data to Diagnosis: Leveraging Algorithms to Identify Clinically Significant Variation in Rare Genetic Disease

Schuetz, Robert J
http://orcid.org/0000-0002-8370-5270
http://rave.ohiolink.edu/etdc/view?acc_num=osu1720887217339216

Abstract Details

2024, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
This dissertation addresses the critical need for scalable variant interpretation in the diagnosis of rare genetic diseases (RGDs) by developing and validating novel computational methods for the interpretation of genome sequencing (GS) data. We introduce Clinical Assessment of Variant Likelihood Ratios (CAVaLRi), a robust algorithm that uses a modified likelihood ratio framework to prioritize diagnostic germline variants. CAVaLRi effectively integrates phenotypic data with variant impact predictions and family genotype information to achieve superior performance over existing prioritization tools in multiple clinical cohorts. CAVaLRi-informed reanalysis was able to uncover eight diagnoses in a cohort of RGD patients who had previously received non-diagnostic GS test results, demonstrating utility in ending diagnostic odysseys. Complementing CAVaLRi, we developed CNVoyant, an advanced tool designed to classify and prioritize copy number variants (CNVs) by incorporating machine learning techniques with genomic features to identify disease causal CNVs. CNVoyant's integration into the CAVaLRi framework allows for a unified approach to handle multiple variant types, thus providing a comprehensive solution for genetic diagnostics. The combined utility of CAVaLRi and CNVoyant offers significant improvements in diagnostic yield and accuracy, facilitating timely and precise genetic diagnosis in clinical settings. These tools represent a scalable approach to meet the growing demands of GS testing, thereby expediting the diagnostic process for patients with undiagnosed RGDs and supporting the broader application of genomics in personalized medicine.
Peter White (Advisor)
Bimal Chaudhari (Advisor)
Elaine Mardis (Committee Member)
Alex Wagner (Committee Member)
James Blachly (Committee Member)
241 p.
Bioinformatics; Biomedical Research; Genetics
Genetic rare disease algorithm data science machine learning artificial intelligence

Recommended Citations

Citations

  • Schuetz, R. J. (2024). From Data to Diagnosis: Leveraging Algorithms to Identify Clinically Significant Variation in Rare Genetic Disease [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1720887217339216

    APA Style (7th edition)

  • Schuetz, Robert. From Data to Diagnosis: Leveraging Algorithms to Identify Clinically Significant Variation in Rare Genetic Disease. 2024. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1720887217339216.

    MLA Style (8th edition)

  • Schuetz, Robert. "From Data to Diagnosis: Leveraging Algorithms to Identify Clinically Significant Variation in Rare Genetic Disease." Doctoral dissertation, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1720887217339216

    Chicago Manual of Style (17th edition)

osu1720887217339216
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© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.
Release 3.2.12