Developing a phenotype risk score for TTR V142I to capture undiagnosed variant transthyretin amyloidosis in health systems
Year: 2026
Type: preprint
Abstract: ABSTRACT Background Phenotype Risk Scores (PheRS) leverage electronic health record (EHR) data to identify individuals at risk for Mendelian disorders, but their performance remains untested for diseases with common and/or non-specific features such as variant transthyretin amyloidosis (ATTRv), often presenting with heart failure (HF), atrial fibrillation, polyneuropathy, and other prevalent diagnoses. We optimized a PheRS for the most common form of ATTRv by integrating genomic and clinical data in Mount Sinai’s Bio Me biobank, focusing on expert-driven phenotype definitions for the TTR variant p.Val142Ile (V142I), which is prevalent in African American (AA) populations (4%). Methods We developed and evaluated a customized PheRS for ATTRv that incorporated 21 expert-curated phenotypic features including 292 ICD-9 and ICD-10 diagnosis codes on a biobank cohort of V142I+ cases (n=383) and controls without any pathogenic/likely pathogenic TTR variants (n=30,642). We compared its performance with the standard automated PheRS approach using different metrics. To account for age-dependent penetrance and high lifelong risk of HF, we further tested the customized PheRS for V142I in a subset of individuals of age ≥ 60 with self-reported Black or AA race/ethnicity and at least one occurrence of HF in their EHRs. Results The expert-curated PheRS outperformed the standard PheRS as measured by improved precision-at- k (0.05 vs. 0.00; k =100), a demonstrably, clinically relevant metric. In the subcohort enriched for anticipated penetrance (older, Black/AA HF patients), the expert-curated PheRS identified more V142I+ individuals (6.0%) among the top 100-scoring individuals than a strategy that randomly sampled from the population (3.6%). Conclusion This work demonstrates that standard PheRS methods are insufficient for common, adult-onset cardiovascular genetic diseases such as V142I-related ATTRv, but when redesigned with disease biology, ancestry, age, and clinical context in mind, PheRS become clinically actionable tools for precision cardiology.
Source: medRxiv
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Big Omics Data Engine 2 Supercomputer, COVID and Translational Science supercomputer (CATS), Conduits: Mount Sinai Health System Translational Science Hub, Uncovering early signals of hereditary TTR amyloidosis in minority populations at high genetic risk, Integrative data science approaches for rare disease discovery in health records
OpenAlex ID: W7118600356
