Trihybrid Cross Calculator - Punnett Square

How to use this trihybrid cross calculator

What this calculator does. This tool combines three two-allele loci – A/a, B/b, and C/c – to enumerate offspring genotypes from two parents. It applies the classical assumptions of allele segregation, complete dominance, equal gamete probability, and independent assortment. The result is a probability model, not a prediction of a specific child or organism. It does not account for linkage, recombination distance, incomplete dominance, codominance, sex linkage, lethal alleles, penetrance, environmental effects, or polygenic traits. For an authoritative overview of the underlying framework, see the National Human Genome Research Institute's explanation of Mendelian inheritance.

When to use it. Use the calculator to check genetics homework, design a plant or animal breeding exercise involving three independent loci, compare how changing one parent from heterozygous to homozygous alters expected offspring, or verify a hand-built 8 × 8 Punnett square. It is especially useful when the number of gamete combinations makes manual counting error-prone.

How to calculate. The calculator opens with a complete demonstration cross, AaBbCc × AaBbCc, so every result and the Excel workbook are available immediately. (1) Replace the demonstration selections in the six genotype controls. (2) Read the live headline probabilities and the genotype-frequency list. (3) Inspect the Punnett square to see every gamete pairing and use the phenotype summary to group genotypes by expressed dominant or recessive states. (4) Select Download Excel to export the current typed inputs and calculated tables as an OOXML workbook. Reset clears all six selections and calculated content; Download Excel then remains unavailable until a complete valid cross is entered again.

Input guide. Mother's Trait A genotype, Trait B genotype, and Trait C genotype are required categorical selections. The father's three controls work the same way. At each locus, choose the homozygous dominant option (AA, BB, or CC), the heterozygous option (Aa, Bb, or Cc), or the homozygous recessive option (aa, bb, or cc). For example, Aa means that the parent can pass A or a with equal probability. AA means every gamete receives A; aa means every gamete receives a. A common mistake is treating the first letter as a phenotype label rather than an allele symbol. Another is assuming that heterozygous always means a 50% dominant phenotype in offspring; the actual result depends on the other parent's genotype.

Output guide. Triple dominant phenotype probability is the chance that offspring show the dominant phenotype at all three loci, written A – B – C – . Triple recessive genotype is the exact aabbcc probability. AaBbCc genotype is the chance of heterozygosity at every locus. Possible maternal gametes and Possible paternal gametes count distinct gamete strings after duplicates are consolidated. The Offspring genotype frequency list reports every distinct genotype and its percentage. The Punnett square gives exact genotype outcomes for each equally likely gamete pairing. In the Phenotype and genotype summary, “dominant” means at least one uppercase allele at that locus and “recessive” means two lowercase alleles. These are exact identities under the model assumptions, not medical or breeding recommendations.

Worked example. In the startup cross AaBbCc × AaBbCc, each heterozygous locus produces a 3/4 dominant-phenotype probability and a 1/4 recessive probability. Independent assortment lets us multiply across loci. Therefore, the triple-dominant probability is (3/4) × (3/4) × (3/4) = 27/64 = 42.19%. The exact aabbcc probability is (1/4)³ = 1/64 = 1.56%. At each locus, Aa × Aa produces Aa in 2 of 4 outcomes, so AaBbCc occurs with (1/2)³ = 1/8 = 12.50%. Each parent produces eight distinct gametes, giving 8 × 8 = 64 equally likely cells and 27 distinct genotype classes.

Learn more. OpenStax explains the law of independent assortment and how alleles separate into gametes. Its companion discussion of Mendel's experiments and probability provides useful context for why expected ratios emerge from repeated crosses.