mendelian-pedigree-sim — 멘델 유전 가상 가계도Lab
Punnett square + Monte Carlo (n=10K) + LOD score (Morton 1955) + 6 inheritance patterns (autosomal D/R, X-linked D/R, Y, mitochondrial) drag-and-drop pedigree (zero external libs, ko/en/ja).
Two carriers Aa × Aa → 25% AA, 50% Aa, 25% aa offspring. Compare dominant vs recessive expression.
PEDIGREE CANVAS (drag nodes, click ! to toggle affected, × to remove)
☐ male · ◯ female · filled = affected · Bennett 1995 NSGC notation. Select a node first to add spouse/child.
LOD SCORE ANALYSIS (Morton 1955)
LOD ≥ 3 = significant linkage (1000:1 odds). BRCA scenario (θ=0.1, n=20, r=2) → LOD = 3.197 (정정값).
Tool Guide
Definition
mendelian-pedigree-sim is a free learning tool that lets users drag-and-drop pedigree nodes, toggle 6 inheritance patterns (autosomal dominant/recessive, X-linked dominant/recessive, Y-linked, mitochondrial), auto-compute Punnett squares, run Monte Carlo (n=10,000), and calculate LOD scores (Morton 1955 AJHG 7:277) — all in-house React + SVG. Five learning scenarios (dominant vs recessive / X-linked family / consanguineous marriage risk / BRCA-positive LOD / mitochondrial maternal inheritance) baked in full (Strachan & Read / Morton 1955 / OMIM HBOC, zero npm).
Purpose
(1) Undergraduate genetics pedigree static figures → drag-and-drop interactive (2) Compare 6 inheritance patterns intuitively (dominant/recessive/X-linked/Y-linked/mitochondrial) (3) Learn Punnett + Monte Carlo law-of-large-numbers ±2% verification (4) LOD score (Morton 1955) clinical genome linkage baseline (5) 5 baked scenarios: consanguineous marriage risk, BRCA positive, mitochondrial maternal, etc. (6) Zero external libs — replace d3-hierarchy / d3-tree with in-house SVG drag
How to Use
① Pedigree canvas — drag-and-drop nodes (○ female / □ male) + connect children/spouses ② Toggle phenotype (affected ■ ● / carrier / normal) ③ Pick inheritance pattern (6) — autosomal D/R, X-linked D/R, Y-linked, mitochondrial ④ Punnett square auto-computed (selected parent pair → offspring genotype probabilities) ⑤ Monte Carlo n=10,000 → next-generation phenotype distribution (law of large numbers ±2%) ⑥ LOD score — enter n/r recombinants → LOD = r·log₁₀(θ) + (n−r)·log₁₀(1−θ) + n·log₁₀(2) ⑦ Quick scenarios — dominant vs recessive / X-linked family / consanguineous / BRCA / mitochondrial ⑧ Hover 16 academic terms (dominant/recessive / autosomal / X-linked / penetrance / expressivity / LOD score / recombination / centimorgan / Punnett, etc.)
Examples
Example 1) Aa × Aa Punnett → {AA: 0.25, Aa: 0.5, aa: 0.25} Example 2) Monte Carlo n=10,000 (Aa × Aa) → AA ≈ 2500 ±200 / Aa ≈ 5000 ±200 / aa ≈ 2500 ±200 (law of large numbers) Example 3) LOD Morton 1955 (θ=0.1, n=20, r=2) → LOD ≈ 3.197 ±0.01 (strong linkage evidence) Example 4) X-linked recessive (carrier mother × normal father) → 50% affected sons / 0% affected daughters, 50% carriers Example 5) BRCA-positive (autosomal dominant, penetrance 80%) → LOD + 60% next-gen risk Example 6) Mitochondrial maternal → affected mother → all children affected; affected father → 0