HSC

Biology

Hardy-Weinberg Scenario Comparison Calculator

Compare two Hardy-Weinberg allele-frequency and population scenarios, expected heterozygotes, expected q-q counts, and explicitly entered planning costs.

Scenario A p frequencyScenario B p frequencyScenario A populationScenario B populationEntered cost per heterozygote screenedEntered cost per q-q caseEntered carrier comparison

Scenario A q-

Scenario B q-

A expected heterozygotes-

B expected heterozygotes-

A expected q-q count-

B expected q-q count-

A entered screening and case cost-

B entered screening and case cost-

B minus A heterozygotes-

B minus A modeled cost-

B heterozygotes minus comparison-

Decision view

Two-population genotype comparison

Two-population genotype comparisonGrouped stacked bars compare expected genotype counts and keep the entered cost layer distinct for scenarios A and B.

Exact scenario comparisonScenario B p frequency changes while all other entered assumptions remain constant.

Scenario B p frequency	Scenario A q	Scenario B q	A expected heterozygotes	B expected heterozygotes	A expected q-q count	B expected q-q count	A entered screening and case cost	B entered screening and case cost	B minus A heterozygotes	B minus A modeled cost	B heterozygotes minus comparison

How to use Hardy-Weinberg Scenario Comparison Calculator

Enter p frequency and population for scenario A.
Enter p frequency and population for scenario B.
Enter optional planning costs and a heterozygote comparison.
Compare the grouped genotype composition and B-minus-A results.

Calculator guide

Understanding Hardy-Weinberg Scenario Comparison Calculator

Comparing two Hardy-Weinberg scenarios requires both allele composition and population scale. A higher heterozygote rate can still produce fewer expected heterozygotes in a smaller population, while entered planning costs can move differently from carrier counts.

Rate and scale Population size can reverse a rate-only impression.

Grouped composition Both genotype structures share one scale.

Costs stay explicit Unit assumptions are visible.

Signs have meaning Differences are consistently B minus A.

Calculation method

How the calculation works

Calculate complete expected genotype counts for two independent p-frequency and population scenarios before applying optional entered planning costs. Planning costs are applied after expected genotype counts so price assumptions remain biologically separate. Calculate q, 2pqN, and q²N independently for each scenario, apply the entered unit costs, then subtract B minus A.

Detailed calculation process

Compare genotype counts and entered costs across two populations

Scenario A uses p = 0.55 and N = 1,000; scenario B uses p = 0.70 and N = 1,200, with $35 per heterozygote and $500 per q-q case.

General formula: for i in {A,B}: q_i = 1-p_iH_i = 2p_iq_iN_iQ_i = q_i^2N_iC_i = c_HH_i + c_QQ_iDeltaH = H_B-H_ADeltaC = C_B-C_A Allele frequencies determine genotype rates, population sizes scale them to counts, and entered unit costs are applied only after the two expected count structures are complete.

What each symbol means

p_i, q_i Scenario i allele frequencies.

N_i Scenario i population size.

H_i Expected p-q heterozygote count.

Q_i Expected q-q count.

c_H, c_Q Entered planning cost per heterozygote and q-q case ($/case).

C_i Scenario modeled cost ($).

DeltaH, DeltaC B-minus-A count and cost differences.

Worked substitution with the default inputs

1. Calculate scenario A frequencies q_A = 1-0.55 = 0.45H_A = 2(0.55)(0.45)(1000) = 495Q_A = 0.45^2(1000) = 202.5 A's p and q are relatively balanced, producing a high heterozygote rate.

2. Calculate scenario B frequencies q_B = 1-0.70 = 0.30H_B = 2(0.70)(0.30)(1200) = 504Q_B = 0.30^2(1200) = 108 B's larger population offsets its lower heterozygote rate.

3. Apply scenario A entered costs C_A = $35(495)+$500(202.5) = $118,575 The q-q term dominates the entered cost model.

4. Apply scenario B entered costs C_B = $35(504)+$500(108) = $71,640 B has slightly more heterozygotes but substantially fewer q-q expectations.

5. Reconcile B-minus-A differences DeltaH = 504-495 = 9DeltaC = $71,640-$118,575 = -$46,935 The signs show nine more B heterozygotes and $46,935 lower modeled B cost.

The defaults give B nine more expected heterozygotes but 94.5 fewer q-q cases and $46,935 less entered modeled cost than A.

Population comparison

Compare genotype composition and expected counts side by side

Grouped stacked bars separate p-p, p-q, and q-q expectations for both populations, with cost references below.

Scenario A p = 0.55 in 1,000.

Scenario B p = 0.70 in 1,200.

Carrier difference B minus A heterozygotes.

Cost difference Entered cost arithmetic, not a recommendation.

Worked situations

Practical examples

Scenario A expects 495 heterozygotes.
Scenario B expects 504 heterozygotes and 108 q-q cases.
The entered cost model is $46,935 lower for B.

Better inputs

Useful tips

Compare both rates and population-scaled counts.
Treat entered costs as explicit scenario assumptions.
Keep the same genotype and cost definitions across scenarios.

Before relying on the result

Limitations and common mistakes

Both scenarios use theoretical Hardy-Weinberg expectations.
Entered costs are not clinical or public-health recommendations.
The comparison excludes sampling uncertainty and all equilibrium violations.

Reference

Key terms

Expected heterozygotes: 2pq multiplied by the entered population.
Expected q-q count: q² multiplied by population.
Modeled cost: Entered unit costs applied to expected counts.
B-minus-A: A signed comparison where positive means B is larger.

Important note

Calculated from the entered values using the displayed biological or statistical model. Study design, sampling, measurement quality, and biological variation affect interpretation.

Frequently asked questions

Why does B have more heterozygotes despite a lower heterozygote rate?

B's population is larger.

Why is B's modeled cost lower?

Its expected q-q count is much lower and that entered unit cost is larger.

Are fractional expected cases valid?

They are mathematical expectations, not literal observed individuals.

Can the cost inputs be zero?

Yes. Genotype comparisons still work without the optional cost layer.