Each branch represents one possible outcome for a trait, so multiplying probabilities combines the required outcomes across traits. For example, a branch leading to one phenotype for the first trait and another phenotype for the second trait receives the product of those separate probabilities. This calculation produces the probability of that particular combined outcome.
The Forked Line Method is most appropriate when the traits assort independently, because the probability assigned to one trait can then be combined with the probability assigned to another. Under that condition, separate trait calculations remain distinct before they are joined. If alternative branches lead to the same final outcome, their probabilities must be added rather than treated as separate results.
Addition is used when more than one pathway produces the same phenotype or genotype. The method first calculates each pathway separately, then combines those mutually alternative routes to obtain the total probability for the shared outcome. This prevents equivalent outcomes from being overlooked when several inheritance combinations contribute to one predicted category.
Begin by separating the cross into individual traits and determine the possible genotype or phenotype probabilities for each one. Draw branches for those alternatives, then extend each branch through the remaining traits. Multiply probabilities along every complete branch. Finally, add probabilities from branches that produce the same final genotype or phenotype.
For a dihybrid cross, the method treats each of the two traits as a separate probability problem before combining them. This avoids filling a large Punnett square with every combined possibility. The resulting branches show how individual trait probabilities contribute to expected offspring categories, making the predicted ratio easier to organize and inspect.
It becomes especially useful as the number of traits increases, because a full Punnett square can become difficult to construct and interpret. Separate branches preserve the probability calculation for each trait while allowing combined outcomes to be assembled systematically. In biology, this supports predictions for complex crosses and helps analyze how inheritance patterns contribute to variation within populations.