💞 GenomeMatch

Purpose

Score genetic compatibility between all male-female pairings in a Genomebook generation. The engine evaluates heterozygosity advantage, disease carrier risk, and trait complementarity to rank optimal mating pairs for the next generation.

How It Works

1. Load genomes for a target generation from GENOMEBOOK/DATA/GENOMES/.
2. Compute pairwise compatibility for every M x F combination:
   • Heterozygosity score (40%): fraction of loci where offspring would be heterozygous (genetic diversity advantage).
   • Trait complementarity (40%): reward balanced trait combinations and high average trait values across the pair.
   • Disease risk penalty (20%): flag pairs where both parents carry recessive disease alleles (25% affected offspring risk per flagged condition).
3. Rank all pairings by composite score (0.0 to 1.0).
4. Select non-overlapping mating pairs via greedy selection from the top of the ranked list (each individual mates at most once per generation).

Input

• GENOMEBOOK/DATA/GENOMES/*.genome.json
• GENOMEBOOK/DATA/disease_registry.json

Output

• Ranked compatibility table (all M x F pairings)
• Selected mating pairs for the next generation

CLI Usage

# Score all pairings for generation 0
python skills/genome-match/genome_match.py

# Score a specific generation
python skills/genome-match/genome_match.py --generation 1

# Demo mode
python skills/genome-match/genome_match.py --demo

# Limit output to top N pairings
python skills/genome-match/genome_match.py --top 10

Output Format

Rank          Male x Female              Score   Het   Comp   Risk  Flags
   1      einstein-g0 x curie-g0         0.8234  0.650  0.821  0.000  --
   2      darwin-g0   x franklin-g0      0.7891  0.600  0.790  0.000  --
...

SELECTED MATING PAIRS (generation 0 -> 1):
  Albert Einstein x Marie Curie  (compat: 0.8234)
  Charles Darwin x Rosalind Franklin  (compat: 0.7891)