The paper introduces the Sequence Kernel Association Test (SKAT), a statistical method designed to identify associations between rare and common genetic variants and complex traits in sequencing studies. SKAT is a supervised, flexible, and computationally efficient regression method that can test for association between genetic variants and a continuous or dichotomous trait while adjusting for covariates. Unlike classical single-marker association tests, SKAT can effectively handle the limited power issue associated with rare variants. The method uses a score-based variance-component test to calculate p-values analytically, making it suitable for genome-wide data analysis. Through simulations and real data analysis, the authors demonstrate that SKAT outperforms several alternative rare-variant association tests, particularly in scenarios with a wide range of effect sizes and variant frequencies. The paper also provides analytic power and sample-size calculations to guide the design of sequencing-based association studies. SKAT's flexibility in handling epistatic effects and its ability to incorporate covariates make it a powerful tool for genetic studies of complex traits.The paper introduces the Sequence Kernel Association Test (SKAT), a statistical method designed to identify associations between rare and common genetic variants and complex traits in sequencing studies. SKAT is a supervised, flexible, and computationally efficient regression method that can test for association between genetic variants and a continuous or dichotomous trait while adjusting for covariates. Unlike classical single-marker association tests, SKAT can effectively handle the limited power issue associated with rare variants. The method uses a score-based variance-component test to calculate p-values analytically, making it suitable for genome-wide data analysis. Through simulations and real data analysis, the authors demonstrate that SKAT outperforms several alternative rare-variant association tests, particularly in scenarios with a wide range of effect sizes and variant frequencies. The paper also provides analytic power and sample-size calculations to guide the design of sequencing-based association studies. SKAT's flexibility in handling epistatic effects and its ability to incorporate covariates make it a powerful tool for genetic studies of complex traits.