An efficient protocol for rare mutation genotyping in a large population

We introduce a method to efficiently detect rare mutations for individual subjects in a large population by pooling samples and retesting subgroups of positive pooled samples. We conducted computer simulations of this method and discovered that it seems efficient for mutation prevalences less than 0.1, regardless of the number of samples. The simulations also indicate that splitting the pooled samples into three to five subgroups at each level is optimal. The expected number of necessary tests and relative efficiency of this method are given, by mutation prevalence and sample size.