Linkage analysis versus association analysis: distinguishing between two models that explain disease-marker associations

Abstract

Human genetics researchers have been intrigued for many years by weak-to-moderate associations between markers and diseases. However, in most cases of association, the cause of this phenomenon is still not known. Recently, interest has grown in pursuing association studies for complex diseases, either instead of or in addition to linkage studies. Hence, it is timely to reconsider what a disease-marker association, particularly in the weak-to-moderate range (relative risk < 10), can tell us about disease etiology. To this end, this study accomplishes three aims: (1) It formulates two different models explaining weak-to-moderate associations and derives the relationship between them. One is a linkage disequilibrium model, and the other is a "susceptibility," or pure association, model. The importance of drawing the distinction between these two models and the implications for our understanding of the genetics of human disease will also be discussed. It will be argued that the linkage disequilibrium model represents true linkage but that the susceptibility model does not. (2) It examines two family-based association tests proposed recently by Parsian et al. and Spielman et al. and derives formulas for their behavior under the two models described above. It demonstrates that these tests yield almost identical results under these two models. It shows that, whereas these tests can confirm an association, they cannot determine whether the association is caused by the linkage disequilibrium model or the susceptibility model. The study also characterizes the probabilities yielded by the family association tests in the presence of weak-to-moderate associations, which will aid researchers using these tests. (3) It proposes two approaches, both based on linkage analysis, which can distinguish between the two models described above. One approach involves a straightforward linkage analysis of the data; the other involves a partitioned association-linkage (PAL) test, as suggested by Greenberg. Formulas are derived for testing identity by descent in affected sib pairs by using both approaches. (4) Finally, the formulas and arguments are illustrated with two examples from the literature and one computer-simulated data set.