Our model is directed toward the Common Disease Common Variant hypothesis. We posit that each variant contributes independently and additively to risk. This can be simply expressed in a logistic regression model: the logit of relative risk is predicted by a weighted sum of counts of risk-bearing alleles as in equation (1): (1)log(p1−p)=∑i∈Pβini where ni represents the counts of allele i and βi reflects the contribution of allele i to the phenotype. We are anticipating many small contributions of common alleles to AD risk and therefore anticipate that many of the true coefficients βi will be non-zero but not large. Therefore when we fit model (1) to our data, we expect to see that many of the fitted estimates bi, of βi, will be significantly different from 0, i.e. more fitted coefficients bi will be associated with large increases in deviance explained in model (1), and therefore small p-values, than we would see in similarly powered studies if there no underlying effects (if all βi were 0). In order to test whether there are more non-zero coefficients than we would
