We conceptualize the gene set approach as follows. Consider a particular set of genes that produce proteins that act in a pathway to produce a given metabolite, or that convey a signal from the cell surface to the cell nucleus, or that come together to form a multimeric receptor. Functional variants in any one of the genes might produce abnormal proteins, many of which would impair the function accomplished by that set. Many variants in that gene set would result in similar perturbations in biological function, which could then affect propensity to a complex disease. If one variant in one of these genes conferred substantially increased disease risk, then that variant would be relatively easy to detect by traditional association methods. However natural selection tends to remove such genes, but is less efficient at removing variants which increase risk only slightly. If each of several variants of genes within that set confer modest increases in disease risk, those variants might be quite difficult to detect when examined one at a time. However greater statistical power might be achieved toward detecting a disease association if all these variants were analyzed jointly as an a priori statistical unit.
