Gennari, JohnChen, Yile2026-02-052026-02-052026-02-052025Chen_washington_0250E_29139.pdfhttps://hdl.handle.net/1773/55114Thesis (Ph.D.)--University of Washington, 2025The rapid growth of clinical sequencing has led to an accelerating number of Variants of Uncertain Significance (VUS), now comprising a substantial fraction of reported germline findings. While functional assays and computational Variant Effect Predictors (VEPs) contribute valuable evidence, current frameworks often treat genes uniformly, overlook gene-level heterogeneity in pathogenicity prevalence, and rely on uncalibrated or globally calibrated predictor scores. These gaps limit the consistency, accuracy, and clinical actionability of variant interpretation under ACMG/AMP guidelines. There is a pressing need for approaches that incorporate gene-specific context, integrate diverse evidence sources, and improve the calibration of computational evidence to strengthen variant classification. This dissertation introduces gene-specific informatics frameworks to improve functional assay prioritization, pathogenicity prior estimation, and the calibration of Variant Effect Predictors (VEPs), with the goal of reducing the burden of VUS in genomic medicine. By integrating statistical modeling, positive–unlabeled learning, domain-aware clustering, and adaptive calibration strategies, the work strengthens the ACMG/AMP Bayesian framework for context-aware variant interpretation. First, a gene prioritization model identifies genes where functional assays would yield the greatest clinical impact by jointly optimizing VUS “movability,” correction of potential misclassifications, and gains from computational predictors, highlighting high-value genes such as \textit{TSC2}. Second, gene-specific pathogenicity priors are estimated using a refined PU-learning method (DistCurve), supported by a complementary domain-based clustering approach for genes with limited labels. Third, a gene-aware calibration framework converts raw VEP scores into calibrated PP3/BP4 evidence strengths through a dynamic decision-tree workflow that selects the optimal strategy per gene. This gene-specific approach outperforms global calibration and, together with a per-gene mixed-predictor selection strategy, improves the accuracy and consistency of variant classification. Together, these contributions establish a context-aware decision-support ecosystem that better directs functional assay investment, provides robust statistical foundations for Bayesian interpretation, and improves the reliability of computational evidence. The resulting framework enhances the accuracy, consistency, and clinical actionability of genomic variant classification.application/pdfen-USnoneBioinformaticsTo Be AssignedThesis