Manual clinical DNA variant classification is the bottleneck of every clinical and research rare disease workflow. The process typically requires a curator to assemble evidence from numerous databases, weigh 28 criteria, reconcile competing evidence, and produce a defensible case for the final classification. Additionally, the framework used to assess variants is not static and successive addenda have revised individual criteria. The most complete and current evidence aggregators available are commercial platforms, which can limit researcher access. We present HeartVar, an open-source web tool that automates the evidence-gathering and interpretation steps of variant classification associated with cardiovascular disease. Given a gene, a variant, and clinical context, HeartVar queries 20 public databases in parallel and assigns ACMG/AMP criteria through a hybrid rule-based/large language model (LLM) approach. Criteria that can be resolved from structured data are computed programmatically, and only those requiring interpretation of unstructured evidence are passed to the LLM. HeartVar returns a classification, point score, per-criterion breakdown, clinical-narrative summary, and database annotations. Benchmarking of 106 expert-curated ClinGen variants showed HeartVar outperformed other curation tools, assigning the correct ACMG tier in 72% of cases. HeartVar demonstrates that an LLM constrained by a domain-specific prompt and grounded in structured evidence can produce variant interpretations of first-pass quality for a cardiovascular disease cohort; however, it is not intended to replace manual assessment by a qualified variant curator. The tool is freely available to use and hosted at www.heartvar.victorchang.edu.au.
Jamie-Lee M. Thompson, Debjani Das, S. Dunwoodie et al.· bioRxiv· 0 citations
Recent medical advances have significantly improved the life expectancy of individuals with congenital heart disease (CHD); however, these children remain at increased risk of co‐occurring neurodevelopmental disorders (NDD), such as attention‐deficit/hyperactivity disorder and autism spectrum disorder. Although prenatal environmental factors, including placental dysfunction and altered oxygen levels in utero, as well as postnatal events such as cardiac surgery, may contribute to this increased risk, shared genetic factors may also underlie both conditions. Therefore, this study is aimed at investigating genomic, transcriptomic and epigenomic findings in patients with NDD and/or CHD using an integrative multiomic approach. A cohort of 14 trios and one duo was recruited: Two probands had both NDD and CHD, two had CHD only and 11 had NDD only. Blood samples were analysed using whole‐genome sequencing, RNA sequencing and DNA methylation profiling. We identified one large deletion (~2.5 Mb) and seven likely pathogenic/pathogenic (LP/P) variants, including two in autosomal dominant genes relevant to the patients′ phenotypes and five in autosomal recessive genes consistent with carrier status. This included a likely pathogenic de novo splice‐disrupting variant in the chromatin remodelling gene ARID1B, validated by RNA sequencing. DNA methylation analysis revealed epigenetic differences between CHD and NDD patients, with CHD patients showing elevated biological age acceleration. Comparison with a reference cohort of 178 controls identified four probands with extreme methylation dysregulation, including the individual with the ARID1B variant. Overall, these findings highlight the diagnostic and mechanistic value of integrative multiomic profiling in paediatric developmental disease cohorts.
Jamie-Lee M. Thompson, Yun-Kai Gao, Eri Iwasawa et al.· Human Mutation· 0 citations
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