Paul Avillach | Search PubMed

PubMed2026

Prothrombin G20210A and Factor V Leiden Variants Are Not Associated With Thrombotic Events in Congenital Heart Disease: An Observational Trial.

BACKGROUND: Thrombotic events, including acute ischemic stroke, are more common in individuals with congenital heart disease (CHD). Whether common thrombophilia variants contribute to thrombotic risk in this population remains unclear. We evaluated whether prothrombin G20210A (F2 c.97G>A) and factor V Leiden (F5 c.1601G>A; p.Arg534Gln) are associated with thrombotic events in CHD. METHODS: Participants in the Pediatric Cardiac Genomics Consortium with exome sequencing and electronic medical record data were identified. Individuals were stratified by prothrombin G20210A and factor V Leiden genotypes, ventricular physiology, and antithrombotic therapy. The primary outcome was the presence of International Classification of Diseases (ICD) or Phecodes (phenotype codes) for thrombotic events. RESULTS: Among 4008 participants (median age, 11.4 [interquartile range, 5.1-17.9] years; 44.4% boys), thrombotic events occurred in 737 (18%), including 93 (13%) with acute ischemic stroke. Compared with the Genome Aggregation Database, the CHD cohort had a lower prevalence of heterozygous prothrombin G20210A and factor V Leiden variants. Variant prevalence did not differ between participants with and without thrombotic events. Single-ventricle CHD was associated with higher thrombosis frequency than biventricular CHD (35% versus 16%, P≤0.0001), without differences in variant prevalence. CONCLUSIONS: In this multicenter CHD cohort, prothrombin G20210A and factor V Leiden were not significantly associated with thrombotic outcomes, supporting recommendations against routine screening. Given low variant prevalence, the study was powered to exclude only large associations. Reduced variant frequency suggests survivorship bias beginning in fetal life. Larger integrated clinical-genomic studies are needed to refine thrombotic risk stratification in CHD. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT03347214.

Humans↗

PubMed2025

VarPPUD: Pinpointing diagnostic variants from sets of prioritized, strong candidate variants.

Rare and ultra-rare genetic conditions are estimated to impact nearly 1 in 17 people worldwide, yet accurately pinpointing the diagnostic variants underlying each of these conditions remains a formidable challenge. Because comprehensive, in vivo functional assessment of all possible genetic variants is infeasible, clinicians instead consider in silico variant pathogenicity predictions to distinguish plausibly disease-causing from benign variants across the genome. However, in the most difficult undiagnosed cases, such as those accepted to the Undiagnosed Diseases Network (UDN), existing pathogenicity predictions cannot reliably discern true etiological variant(s) from other deleterious candidate variants that were prioritized through case- or family-level analyses. Pinpointing the disease-causing variant from a small pool of plausible candidates remains a largely manual effort requiring extensive clinical workups, functional and experimental assays, and eventual identification of genotype- and phenotype-matched individuals. Here, we introduce VarPPUD, a tool trained on prioritized variants from UDN cases, that leverages gene-, amino acid-, and nucleotide-level features to discern pathogenic (disease causative) variants from other damaging or deleterious variants that are unlikely to be confirmed as relevant to the disease. VarPPUD achieves a cross-validated accuracy of 79.3% and precision of 77.5% on a held-out subset of uniquely challenging UDN cases, respectively representing an average 18.6% and 23.4% improvement over nine existing state-of-the-art pathogenicity prediction tools on this task. We validate VarPPUD's ability to discriminate likely from unlikely pathogenic variants using both synthetic data generated via a GAN-based framework and a temporally held-out set of UDN patients evaluated between 2022 and 2024. The model was trained exclusively on data available through 2021 and applied without retraining to the post-2021 cohort, demonstrating strong generalizability to newly accrued cases. Finally, we show how VarPPUD can be probed to evaluate each input feature's importance and contribution toward prediction-an essential step toward understanding the distinct characteristics of newly-uncovered disease-causing variants.

Humans↗