Biomedical subjects

Vikas Pejaver

Publications and source records attributed to Vikas Pejaver.

3 recordsLinked to original sources

PubMed2026

A Phenotype Risk Score-Triggered E-Visit Pathway to Capture TTR V142I in a Heart Failure Population.

BACKGROUND: Variant transthyretin amyloidosis (ATTRv) is an underdiagnosed cause of heart failure (HF), typically identified at later disease stages. The TTR p.Val142Ile (V142I) variant, found in ∼4% of African American (AA) individuals, is the most common cause of ATTRv in the United States and delayed diagnoses contribute to health disparities. PROJECT RATIONALE: Genetic testing is a guideline-directed step in diagnosing ATTRv but is performed in only ∼6% of patients. Improving identification of at-risk patients through genetic testing should accelerate diagnosis. PROJECT SUMMARY: A novel phenotype risk score (PheRS) that identifies patients at increased likelihood of harboring V142I is implemented in a health system. Using data from the electronic health records of AA patients ≥60 years old with HF, high-risk PheRS scores trigger clinician alerts recommending genetic testing through a new asynchronous, message-only (E-visit) pathway. TAKE-HOME MESSAGE: A disease-specific PheRS paired with clinician prompts and a digital genetics care pathway can expedite genetic diagnoses in patients with HF at risk for ATTRv.

cardiomyopathy↗

PubMed2025

Extracting and calibrating evidence of variant pathogenicity from population biobank data.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

Humans↗

PubMed2025

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.

PURPOSE: We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches has since been released, and these methods must be independently calibrated for clinical application. METHODS: Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength. RESULTS: All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions. CONCLUSION: At calibrated thresholds, 3 new computational predictors provided evidence for variant pathogenicity at similar strength to the 4 previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

Humans↗