Linkage analysis of "necessary" disease loci versus "susceptibility" loci

The association of some diseases with specific alleles of certain genetic markers has been difficult to explain. Several explanations have been proposed for the phenomenon of association, e.g., the existence of multiple, interacting genes (epistasis) or a disease locus in linkage disequilibrium with the marker locus. One might suppose that when marker data from families with associated diseases are analyzed for linkage, the existence of the association would assure that linkage will be found, and found at a tight recombination fraction. In fact, however, linkage analyses of some diseases associated with HLA, as well as diseases associated with alleles at other loci located throughout the genome, show significant evidence against linkage, and others show loose linkage, to the puzzlement of many researchers. In part, the puzzlement arises because linkage analysis is ideal for looking for loci that are necessary, even if not sufficient, for disease expression but may be much less useful for finding loci that are neither necessary nor sufficient for disease expression (so-called susceptibility loci). This work explores what happens when one looks for linkage to susceptibility loci. A susceptibility locus in this case means that the allele increases risk but is neither necessary nor sufficient for disease expression. It might be either an allele at the marker locus itself that is increasing susceptibility or an allele at a locus in linkage disequilibrium with the marker. This work uses computer simulation to examine how linkage analyses behave when confronted with data from such a model. The results show that if the probability of having the disease with the associated allele is less than 10 times greater than the probability of having the disease without the allele, then it may be difficult to detect linkage, even in data sets consisting of 30 nuclear families with at least two people affected. On the basis of these results, it would appear that doing linkage analysis on risk-factor data may not yield additional information about linkage in the usual sense but may help distinguish between different hypotheses to explain the association. As the difficulties inherent in linkage analysis for analyzing common disease become more apparent, investigators will turn to association analysis to find genes involved in disease. Before embarking on a search for a susceptibility locus, one must weigh the potential importance of such a gene against the possibility that the locus detected via association analysis may represent a relatively minor contribution to disease causality.