A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

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 play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. 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 compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.