Hardy-Weinberg p²

freq AA = p².
Created by
Renato Passos, Eng. de Software
Reviewed by
Renato Passos, Eng. de Software

Last updated: Apr 18, 2026

0,3600

About this calculator

The Hardy-Weinberg p² calculator determines the frequency of the homozygous dominant genotype (AA) in a population under genetic equilibrium. Based on the Hardy-Weinberg equation (p² + 2pq + q² = 1), users input the dominant allele frequency (p), and the result is p². This tool is fundamental in population genetics for predicting genotype distributions in idealized populations.

To use the calculator, enter the value of p, representing the dominant allele frequency. The p² calculation applies under Hardy-Weinberg equilibrium assumptions (no selection, mutation, migration, or genetic drift). It is commonly used in studies of hereditary diseases, genetic conservation, and evolutionary biology.

Apply this tool when estimating the proportion of AA genotype individuals in a theoretical population. Examples include analyzing recessive disease-linked genes, such as cystic fibrosis, or monitoring genetic diversity in reserves. Avoid use in populations with active migration or selective pressures.

Note: Results depend on the validity of Hardy-Weinberg assumptions. In real populations, factors like non-random mating or mutations can invalidate calculations. Always ensure the sample is representative and that p falls within 0 < p < 1.

Frequently asked questions

How to differentiate AA and Aa genotypes with this calculator?

The calculator only computes AA frequency (p²). For Aa, calculate 2pq, where q = 1 - p.

Why is Hardy-Weinberg equilibrium required?

The model assumes ideal populations with random mating and no evolutionary factors. Deviations indicate real biological pressures.

What if the p frequency is less than 0.1?

Results may be imprecise due to rounding errors. Use values with at least 3 decimal places.

Can it be used for recessive genes?

Not directly. For recessive (aa), calculate q², where q = 1 - p.

How to validate results in a real population?

Compare observed frequencies with expected values using statistical tests like chi-square.

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