title: What association analysis can and cannot tell us about the genetics of complex disease.
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 unknown. Recently, interest has grown in pursuing association studies for complex psychiatric diseases, either instead of or in addition to linkage studies. However, it is one thing to detect association; the next important step is to determine why. Here we consider what a disease-marker association in the weak-to-moderate range (relative risk < 5) can tell us about disease etiology. Two distinct models (not the only possibilities) which could explain such an association are formulated. One is a linkage disequilibrium or major disease gene model, involving tight linkage with a "necessary" major gene. The other is a pure association model, involving a "susceptibility" gene that has only a minor effect on disease state. It will be shown that association analysis cannot distinguish between these two models, and hence cannot elucidate the biological mechanism behind the association. (This statement holds for traditional population-based association tests, as well as for more recent family-based association tests.) The importance of drawing the distinction between these two genetic models and the implications for understanding the genetics of complex human disease will also be discussed.
authors.affiliation: Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.
authors.forename: S E
authors.initials: SE
authors.lastname: Hodge
mesh_terms: D000437:Alcoholism / Q000235:genetics; D001714:Bipolar Disorder / Q000235:genetics; D005260:Female; D020022:Genetic Predisposition to Disease; D005826:Genetics, Medical; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology* / Q000235:genetics*; D008957:Models, Genetic; D010375:Pedigree; D012307:Risk Factors