This paper introduces a novel statistical method called the weighted-sum method for identifying rare mutations associated with diseases. The method is designed to analyze groups of mutations, such as those identified through resequencing studies, to test for groupwise association with disease status. The authors compare the weighted-sum method to alternative methods, including the cohort allelic sums test (CAST) and the combined multivariate and collapsing (CMC) method, and demonstrate its superior performance in identifying disease-associated genes using both simulated and real data. The weighted-sum method is particularly effective in detecting rare mutations with low population attributable risks (PARs), even when the number of affected individuals is limited to 1,000 to 7,000. The study highlights the importance of specialized analysis methods in resequencing studies to uncover genetic associations in the presence of genetic heterogeneity. The method is flexible and can be adapted to various study designs and data types, making it a valuable tool for researchers studying genetically heterogeneous diseases.This paper introduces a novel statistical method called the weighted-sum method for identifying rare mutations associated with diseases. The method is designed to analyze groups of mutations, such as those identified through resequencing studies, to test for groupwise association with disease status. The authors compare the weighted-sum method to alternative methods, including the cohort allelic sums test (CAST) and the combined multivariate and collapsing (CMC) method, and demonstrate its superior performance in identifying disease-associated genes using both simulated and real data. The weighted-sum method is particularly effective in detecting rare mutations with low population attributable risks (PARs), even when the number of affected individuals is limited to 1,000 to 7,000. The study highlights the importance of specialized analysis methods in resequencing studies to uncover genetic associations in the presence of genetic heterogeneity. The method is flexible and can be adapted to various study designs and data types, making it a valuable tool for researchers studying genetically heterogeneous diseases.