In this work, we propose a specialised method to identify multiple rare mutations underlying a genetically heterogeneous disease. Analysis of real data and power simulations show that the proposed weighted-sum method performs very well compared to existing methods. This demonstrates that the use of specialised analytical methods can improve power to identify genetic components of complex (genetically heterogeneous) diseases. On the other hand, it must be kept in mind that the power of such specialisation is at the cost of generality, and therefore the methods must be used in combination with other strategies covering other biological scenarios such as the common variant common disease scenario. It must further be noticed that all methods using the grouping approach (i.e. CMC, CAST and weighted-sum) are sensitive to misclassification of which allele is treated as the mutation (i.e. disease-related allele). If disease-related alleles from some variants are grouped with wild-type alleles from other variants it may hide a true signal. As stated in the Background section, it may be natural to treat e.g. non-synonymous substitutions, frame shift indels and very rare alleles as
