A groupwise association test for rare mutations using a weighted sum statistic is proposed to identify disease-associated genes by analyzing groups of mutations. The method is compared to alternative approaches and shown to be effective in detecting genetic associations, both in simulated and real data. The weighted-sum method groups mutations by function (e.g., genes) and calculates a weighted sum of mutation counts for each individual. Permutation of disease status helps adjust for mutation weighting and ensures accurate type I error control. The method is particularly effective in identifying rare mutations with low individual population attributable risk (PAR), even when each mutation has a low PAR. It is demonstrated that resequencing studies, when combined with this method, can identify important genetic associations, even when each mutation has a low PAR. The method is robust to genetic heterogeneity and can detect multiple rare mutations contributing to disease risk. It is also efficient computationally and can be applied to large-scale studies. The study highlights the importance of specialized analysis methods in identifying genetic associations in the context of rare mutations and genetically heterogeneous diseases.A groupwise association test for rare mutations using a weighted sum statistic is proposed to identify disease-associated genes by analyzing groups of mutations. The method is compared to alternative approaches and shown to be effective in detecting genetic associations, both in simulated and real data. The weighted-sum method groups mutations by function (e.g., genes) and calculates a weighted sum of mutation counts for each individual. Permutation of disease status helps adjust for mutation weighting and ensures accurate type I error control. The method is particularly effective in identifying rare mutations with low individual population attributable risk (PAR), even when each mutation has a low PAR. It is demonstrated that resequencing studies, when combined with this method, can identify important genetic associations, even when each mutation has a low PAR. The method is robust to genetic heterogeneity and can detect multiple rare mutations contributing to disease risk. It is also efficient computationally and can be applied to large-scale studies. The study highlights the importance of specialized analysis methods in identifying genetic associations in the context of rare mutations and genetically heterogeneous diseases.