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Source note3 min read

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

Source

arXiv

arxiv.org/abs/2606.24779v1

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Source

Plain-Language Brief

DeepBD is a workflow developed by researchers to help diagnose genetic birth defects using exome and genome sequencing data. The workflow uses a language model (LLM) to organize the interpretation process, and it includes an evidence engine, specialist modules, and a grounded diagnostic review layer. DeepBD was tested on an in-house dataset of 18,622 cases and outperformed other methods on a held-out benchmark.

What the Document Says

  • DeepBD is a workflow for variant prioritization and diagnostic interpretation of genetic birth defects
  • The workflow includes LLM-assisted case structuring, a pretrained evidence engine, specialist evidence modules, and a grounded diagnostic review layer
  • The evidence engine learns patient-specific scores from structured rule evidence, sequence and variant-effect representations, and phenotype-conditioned biological context
  • Specialist modules and the agentic layer provide tool-based refinement, candidate-pool review, and diagnosis-oriented synthesis
  • DeepBD was developed using an in-house fetal and infant cohort comprising 18,622 cases
  • On an internal held-out solved-case benchmark, DeepBD achieved Recall@1/3/5/10 of 0.658/0.882/0.912/0.929
  • DeepBD outperformed standalone Exomiser, DeepRare, and prompted LLM reranking baselines evaluated on Exomiser-derived top-20 candidate variants
  • Ablation and overlap analyses show that rule evidence, mechanistic context, and specialist refinement provide complementary signals

Key Concepts Mentioned

  • DeepBD
  • workflow
  • variant prioritization
  • diagnostic interpretation
  • genetic birth defects
  • exome sequencing
  • genome sequencing
  • LLM
  • evidence engine
  • specialist modules
  • grounded diagnostic review layer
  • recall
  • Exomiser
  • DeepRare

Claims Made by the Document

  • DeepBD outperforms other methods on a held-out benchmark

Evidence Used

  • DeepBD achieved Recall@1/3/5/10 of 0.658/0.882/0.912/0.929 and outperformed standalone Exomiser, DeepRare, and prompted LLM reranking baselines

Methods / Approach

  • LLM-assisted case structuring
  • pretrained evidence engine
  • specialist evidence modules
  • grounded diagnostic review layer

Limitations Stated by the Authors

  • Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study did not compare DeepBD to other methods on the same dataset, which could affect the results

Open Questions

  • Are there other datasets available for comparison?
  • What is the long-term performance of DeepBD?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The study only evaluates DeepBD on an internal held-out solved-case benchmark, and it is unclear whether the same dataset was used to evaluate other methods

Raw Abstract / Excerpt

Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved many cases from variant detection to post-sequencing interpretation: clinicians must rank patient-specific candidate variants under incomplete fetal or infant phenotypes and heterogeneous evidence from population genetics, variant-effect prediction, gene-disease validity, phenotype ontologies, cellular and pathway context, protein structure and clinical literature. We present DeepBD, a grounded agentic workflow for variant prioritization and diagnostic interpretation of genetic birth defects. DeepBD organizes the workflow into LLM-assisted case structuring, a pretrained evidence engine, specialist evidence modules and a grounded diagnostic review layer. The evidence engine learns patient-specific variant scores from structured rule evidence, sequence and variant-effect representations and phenotype-conditioned biological context, whereas specialist modules and the agentic layer provide tool-based refinement, candidate-pool review and diagnosis-oriented synthesis from ranked candidates. Developed using an in-house fetal and infant cohort comprising 18,622 cases, DeepBD achieved Recall@1/3/5/10 of 0.658/0.882/0.912/0.929 on an internal held-out solved-case benchmark, outperforming standalone Exomiser, DeepRare and prompted LLM reranking baselines evaluated on Exomiser-derived top-20 candidate variants. Ablation and overlap analyses show that rule evidence, mechanistic context, and specialist refinement provide complementary signals. These findings support a grounded agentic workflow that separates evidence integration, tool-based refinement, and LLM-assisted diagnostic review for retrospective variant prioritization in genetic birth defects.