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recombinator

Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluation

Published by ClawBio ↗ClawBio/ClawBio

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recombinator

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Use recombinator to simulate and clinically evaluate an offspring genome from [male genome file] and [female genome file], using the required trait and disease registries, and return the inferred traits and health-score summary.

Required inputs

  • [male genome file]
  • [female genome file]

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SKILL.md

name: recombinatordescription: Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluationlicense: MITmetadata:   version: 0.1.0  author: Manuel Corpas  tags:   - genomebook  - recombination  - meiosis  - mutation  - offspring  - clinical-genetics  openclaw:     requires:       bins:       - python3    always: false    emoji: 🧪    homepage: https://github.com/ClawBio/ClawBio    os:     - darwin    - linux    trigger_keywords:     - recombinator    - recombination    - offspring    - breed    - meiosis    - genomebook breed    - next generation

🧪 Recombinator

Purpose

Produce offspring genomes from selected parent pairs via simulated meiotic recombination. Models Mendelian segregation, de novo mutation, sex determination, and clinical evaluation against a disease registry.

How It Works

  1. Mendelian segregation: one allele inherited from each parent per locus (random selection simulating independent assortment).
  2. De novo mutation: configurable rate per locus (default 0.1%), with hotspot multipliers for cognitive, immune, and metabolic loci. Mutations are classified as disease-risk, protective, or neutral.
  3. Sex determination: 50/50 coin flip (XY or XX).
  4. Trait inference: reverse-map offspring genotype back to trait scores using the trait registry, accounting for dominance models.
  5. Clinical evaluation: check offspring genotype against disease registry for penetrance, onset probability, and fitness cost.
  6. Health score: computed from cumulative fitness costs of clinical conditions.

Input

  • Two parent .genome.json files (one Male, one Female)
  • GENOMEBOOK/DATA/trait_registry.json
  • GENOMEBOOK/DATA/disease_registry.json

Output

  • Offspring .genome.json with:
    • Inherited loci and alleles
    • Mutation log
    • Inferred trait scores
    • Clinical history
    • Health score (0.0 to 1.0)

CLI Usage

# Demo: breed Einstein x Anning, produce 3 offspring
python skills/recombinator/recombinator.py --demo

# Breed specific parents
python skills/recombinator/recombinator.py \
  --father einstein-g0 --mother anning-g0 --offspring 3

# Custom generation number
python skills/recombinator/recombinator.py \
  --father einstein-g0 --mother curie-g0 --offspring 2 --generation 1

Output Format

ID:     g1-001-a3f2c1
Sex:    Female (XX)
Health: 0.9500
Mutations: 1
  - COMT_Val158Met: G->A (neutral, from mother)
Conditions: 0
Top traits:
  - curiosity: 0.92
  - analytical_thinking: 0.88
  - persistence: 0.85