Proposed number of control chromosomes. A statistical algorithm helps to calculate the number of controls required to minimize the error. Let the prevalence of a mutation in patient chromosomes be p₁ and the prevalence in control chromosomes be p₀. Then the probability of an arbitrary control chromosome not carrying the mutation is (1 – p₀). Because the world control population is large, the probability P of arbitrarily choosing n chromosomes thereof without the mutation may be approximated by P = (1 – p₀)ⁿ. The null hypothesis would be that the mutation frequency is equal in patient and control chromosomes, i.e., p₀ = p₁ and P = (1 – p₁)ⁿ. The number of control chromosomes to be tested can be calculated by resolution of the equation for the number n = ln(P)/ln(1 – p₁). When the error probability P is set at 1%, the number of required control chromosomes is n = –4.6/ln(1 – p₁) and n = 460 for the example of p₁ = 1%. The curve demonstrates that 100 control individuals (200 chromosomes) would be adequate for a p₁ of 2.5%, a prevalence that is much higher than that of the most frequent monogenic disorder. Adapted with permission from Neurology (90).