Genotype Frequency Calculator

The genotype frequency calculator converts the raw counts of three genotypes at a single biallelic locus into observed genotype frequencies, derives the underlying allele frequencies (p and q), and then predicts the genotype proportions you would expect under Hardy-Weinberg equilibrium. You simply enter how many homozygous dominant (AA), heterozygous (Aa), and homozygous recessive (aa) individuals you counted, and the tool does the rest. It is one of the most useful starting points in population genetics, because the gap between observed and expected genotype frequencies is exactly what reveals whether a population is evolving.

A genotype frequency is nothing more than the proportion of individuals in a population that carry a particular pair of alleles. Because every diploid individual has exactly one genotype at the locus, the three genotype frequencies always sum to 1. The calculator reports each frequency as a decimal to four places and as a percentage, alongside the total population size, so you can audit the arithmetic and use the values directly in a lab report, a genetics homework set, or a conservation survey.

What makes this genotype frequency calculator more than a simple proportion tool is the second half of its output: it counts the alleles hidden inside those genotypes, computes p (the frequency of the dominant A allele) and q (the frequency of the recessive a allele), and then applies the Hardy-Weinberg equation to show the expected number of each genotype. Comparing observed and expected counts is the classic first test for selection, non-random mating, migration, mutation, or genetic drift acting on the gene pool.

The calculation runs in three connected stages. First, the calculator sums the three counts to find the total population size, N = AA + Aa + aa. It then divides each genotype count by that total to give the observed frequencies: the AA frequency is AA / N, the Aa frequency is Aa / N, and the aa frequency is aa / N. These three numbers always add up to 1 because every individual is counted exactly once.

Second, the tool reaches inside the genotypes to count alleles. Each diploid individual carries two alleles, so a population of N individuals holds 2N alleles at the locus. Each AA individual contributes two A alleles, each Aa individual contributes one A and one a, and each aa individual contributes two a alleles. Summing these gives the dominant allele frequency p = (2 × AA + Aa) / 2N and the recessive allele frequency q = (2 × aa + Aa) / 2N. By construction p + q = 1.

Third, the calculator applies the Hardy-Weinberg principle. Under random mating with no evolutionary forces, the expected genotype frequencies are p² for AA, 2pq for Aa, and q² for aa, and these three terms expand the binomial (p + q)² = 1. The tool reports each expected frequency and multiplies it by N to give an expected count rounded to the nearest individual, which you can compare directly with your observed counts.

The first panel of results shows the observed genotype frequencies drawn straight from your data. A reading such as AA = 0.2000 (20%), Aa = 0.5000 (50%), aa = 0.3000 (30%) tells you precisely how the population is partitioned across the three genotypes. These values are descriptive statistics: they are always correct for the counts you entered and make no assumptions about how the population mates or evolves.

The allele frequency panel reports p and q. Notice that the heterozygotes split their contribution evenly between the two alleles, which is why a population can have a high heterozygote frequency without either allele dominating the gene pool. Knowing p and q lets you summarise the genetic state of the whole locus in a single number, since the two always sum to one.

The expected (Hardy-Weinberg) panel is where interpretation gets interesting. It shows what the genotype frequencies should look like if the population were in equilibrium. When the observed heterozygote count is noticeably lower than the expected 2pq value, the population may show a heterozygote deficit, often caused by inbreeding, population subdivision (the Wahlund effect), or selection against heterozygotes. An excess of heterozygotes can signal overdominance or recent admixture.

To decide whether a difference is real or just sampling noise, geneticists follow up with a formal chi-square goodness-of-fit test that compares observed counts against the expected counts this tool provides. The genotype frequency calculator gives you both sets of numbers in one place, making it the natural launch pad for that statistical test.

It helps to trace the calculator's logic through its own default values: AA = 200, Aa = 500, and aa = 300. The total population is N = 200 + 500 + 300 = 1000 individuals, which means the gene pool contains 2N = 2000 alleles.

The observed genotype frequencies are AA = 200/1000 = 0.2000, Aa = 500/1000 = 0.5000, and aa = 300/1000 = 0.3000. These sum to exactly 1.0000, a quick sanity check that the data was entered correctly.

To find allele frequencies, count the alleles. The A count is (2 × 200) + 500 = 900, so p = 900 / 2000 = 0.4500. The a count is (2 × 300) + 500 = 1100, so q = 1100 / 2000 = 0.5500. As expected, p + q = 1.0000.

The expected Hardy-Weinberg frequencies follow: expected AA = p² = 0.4500² = 0.2025, expected Aa = 2pq = 2 × 0.45 × 0.55 = 0.4950, and expected aa = q² = 0.5500² = 0.3025. Multiplying by N gives expected counts of 203 AA, 495 Aa, and 303 aa. Comparing these with the observed 200, 500, and 300 shows the population is very close to equilibrium, with only a slight excess of heterozygotes.

Genotype frequencies sit at the heart of quantitative genetics, and this genotype frequency calculator turns up across teaching and research. In undergraduate population genetics courses it is the standard tool for working through Hardy-Weinberg problems, where students must move fluently between genotype counts, allele frequencies, and expected proportions.

• Carrier and disease estimation: Public-health geneticists convert the observed frequency of a recessive disorder (the aa frequency) into carrier frequencies, which inform genetic counselling and screening programmes.
• Conservation genetics: Researchers monitoring endangered species compare observed heterozygosity against Hardy-Weinberg expectations to detect inbreeding and loss of genetic diversity in small populations.
• Detecting evolution: A persistent mismatch between observed and expected genotype frequencies flags selection, assortative mating, or migration acting on the locus.
• Population structure: A heterozygote deficit across many loci often signals hidden subdivision (the Wahlund effect), a key concept when defining management units for wildlife.

By outputting observed frequencies, allele frequencies, and Hardy-Weinberg expectations side by side, the calculator produces exactly the inputs needed for downstream measures such as the inbreeding coefficient F, fixation indices like F_ST, and chi-square tests of equilibrium. It is a quick, reliable bridge from raw field or lab counts to interpretable genetic parameters.

The observed genotype frequencies this tool reports are pure descriptive statistics and require no assumptions; they are simply counts divided by the total. The expected genotype frequencies, however, depend on the Hardy-Weinberg assumptions: a large, randomly mating population with no mutation, no migration, no selection, and negligible genetic drift. When any of these conditions fails, observed and expected counts diverge, which is precisely the signal the calculator is designed to highlight.

This calculator models a single biallelic, autosomal locus with two alleles, A and a, expressed through three genotypes. It does not handle loci with three or more alleles, such as the ABO blood-group system, nor sex-linked loci, where allele counts differ between males and females and the simple 2N denominator no longer applies. For such systems the same counting logic must be extended allele by allele or chromosome by chromosome.

Finally, frequencies estimated from small samples carry real sampling error. With only a handful of individuals, a single rare genotype can swing the result substantially, and the expected counts may round in ways that look misleading. Use this genotype frequency calculator for teaching, quick checks, and exploratory analysis, and pair larger or publication-grade datasets with a formal statistical package and a chi-square or exact test of Hardy-Weinberg equilibrium.