Testing association in the presence of linkage--a powerful score for binary traits.

We present a score for testing association in the presence of linkage for binary traits. The score is robust to varying degrees of linkage, and it is valid under any ascertainment scheme based on trait values as well as under population stratification. The score test is derived from a mixed effects model where population level association is modeled using a fixed effect and where correlation among related individuals is allowed for by using log-gamma random effects. The score, as presented in this paper, does not assume full information about the inheritance pattern in families or parental genotypes. We compare the score to the semi-parametric family-based association test (FBAT), which has won ground because of its flexible and simple form. We show that a random effects formulation of co-inheritance can improve the power substantially. We apply the method to data from the Collaborative Study on the Genetics of Alcoholism. We compare our findings to previously published results.