LA-MARRVEL: A Knowledge-Grounded and Language-Aware LLM Reranker for AI-MARRVEL in Rare Disease Diagnosis

Abstract: Diagnosing rare diseases often requires connecting variant-bearing genes to evidence that is written as unstructured clinical prose, which the current established pipelines still leave for clinicians to reconcile manually. To this end, we introduce LA-MARRVEL, a knowledge-grounded and language-aware reranking layer that operates on top of AI-MARRVEL: it supplies expert-engineered context, queries a LLM multiple times, and aggregates the resulting partial rankings with a ranked voting method to produce a stable, explainable gene ranking. Evaluated on three real-world cohorts (BG, DDD, UDN), LA-MARRVEL consistently improves Recall@K over AI-MARRVEL and established phenotype-driven tools such as Exomiser and LIRICAL, with especially large gains on cases where the first-stage ranker placed the causal gene lower. Each ranked gene is accompanied by LLM-generated reasoning that integrates phenotypic, inheritance, and variant-level evidence, thereby making the output more interpretable and facilitating clinical review.