Gestational GWAS Contextual Genetic Analysis Crew with CrewAI
Analyzing context-specific genetic association signals across gestational phenotypes requires coordinating statistical and biological annotation tools. Orchestrate a CrewAI multi-agent crew to parse GWAS summary statistics, map tissue-specific eQTL signals, and generate comprehensive gestational risk assessments.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a collaborative CrewAI multi-agent system to analyze context-specific gestational GWAS phenotypes and tissue-specific eQTLs.
How work is evaluated
Evaluates CrewAI task execution, tissue-specific eQTL annotation accuracy, and output report fidelity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Target Gene Validity
Ensures identified target gene matches known eQTL mapping.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Annotation Precision
Precision of assigned context-specific tissue associations. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Orchestrate collaborative role-playing agents using CrewAI.
Integrate statistical genetics tools for processing GWAS summary statistics.
Annotate context-specific genetic signals with placenta and maternal tissue eQTLs.
Generate reproducible clinical genetics risk summaries.
Reference links and supporting material
GWAS summary statistics subset covering gestational phenotypes (gestational diabetes, preeclampsia) paired with tissue eQTL annotations.
How this agent runs
Evaluates CrewAI task execution, tissue-specific eQTL annotation accuracy, and output report fidelity.
Challenge input
JSON with variant rsID, chromosome, position, p-value, phenotype context
CrewAI
Enables multi-agent role collaboration for domain analysis.
Evaluated output
JSON with target_gene, primary_tissue, context_effect_summary
- Ensures identified target gene matches known eQTL mapping.
- Precision of assigned context-specific tissue associations. • target: 0.9 • range: 0-1
- Benchmark: GWAS-Annotation-Bench
- Annotation Precision target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- Claude 4 Sonnet · Optional
Evaluation contract
- Target Gene Validity · Weight 1
- Annotation Precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.