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Abstract:
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Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity and individual rare mutations with different effects could collectively pay an important role in human diseases. Many existing statistical models for association analysis assume homogenous effects of genetic variants across all individuals, and could be subject to power loss in the presence of disease heteogeneity. In this study, we propose a CAR model for association analysis of sequencing data. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compared the type I error and power performance of the proposed method with those of GGRF and SKAT under different disease scenarios. We found our method outperformed the other two methods when (i) the rare variants had the major contribution to the disease, or (ii) the genetic effects varied in different individuals. Finally, we illustrated the new method by applying it to a large-scale substance dependence whole-genome sequencing data.
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