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It has been shown that genetic factors within the HLA region are involved in the etiology of several diseases. For some of these, the existence of another genetic factor has been suggested, although not proven. A possible way to give evidence for another locus (G) is to show that the disease and an unlinked HLA-marker locus (M) do not segregate independently. The usual lod-score method, which assumes monogenic inheritance, is inappropriate for this test. We propose a correction of this method for performing a linkage analysis between the G and M loci, taking into account the role of HLA. A very simple way of using the HLA information is by modifying, for each individual of a pedigree, the penetrance values at the G locus according to the number of HLA haplotypes shared with the index case. These penetrance values are inferred from the observed IBD (identity-by-descent) distribution of HLA haplotypes in a sample of affected sib-pairs. The advantage of using this empirical distribution is that it is not based on any assumptions concerning the mode of inheritance at the HLA-linked locus. This correction method was established using a two-locus model with restrictive assumptions. Its value is discussed for various sets of parameters in more general and realistic two-locus models using simulations.

One of the current issues in genetic epidemiology is detecting susceptibility genes on the genome. It is common now to undertake systematic screening of the genome using approaches based on a measure of the haplotype sharing in sib pairs. Here, we compare the efficiency of two statistics, the maximum likelihood score (MLS) and the nonparametric linkage score (NPLa) on the simulated data provided for GAW11. A question often raised is whether it is better to perform a single-step or a two-step strategy. For the simulated model, and whatever the strategy used, we show here that the answer is not unequivocal. In both cases, the power to detect susceptibility genes in a single replicate with MLS or NPL is extremely low. With two replicates, only one of the four simulated loci could be detected with reasonable power. When gametic disequilibrium is suspected, methods testing for both linkage and association might be more powerful.

Our recent segregation analysis, carried out on 27 large pedigrees from a Caribbean island (Desirade), has shown the presence of recessive major gene(s) controlling susceptibility to leprosy per se and nonlepromatous leprosy, respectively. Linkage analysis was performed between each of these two detected genes and each of five markers typed in the Desirade population: HLA, ABO, Rhesus, Gm and Km. No positive significant lod score was observed. However, for leprosy per se close linkage was excluded with Rhesus and Gm (and also with ABO and HLA, considering a lower value for the frequency of the gene controlling susceptibility to leprosy per se). The highest lod score, although not significant, was obtained between the gene for nonlepromatous leprosy and ABO. Our overall results, joined with previous studies and experimental data, suggest that the gene controlling susceptibility to leprosy per se and that controlling susceptibility to nonlepromatous leprosy might be different, acting at successive stages of the immune response to infection with Mycobacterium leprae.

The power to detect linkage by the LOD-score method is investigated here for diseases that depend on the effects of two genes. The classical strategy is, first, to detect a major-gene (MG) effect by segregation analysis and, second, to seek for linkage with genetic markers by the LOD-score method using the MG parameters. We already showed that segregation analysis can lead to evidence for a MG effect for many two-locus models, with the estimates of the MG parameters being very different from those of the two genes involved in the disease. We show here that use of these MG parameter estimates in the LOD-score analysis may lead to a failure to detect linkage for some two-locus models. For these models, use of the sib-pair method gives a non-negligible increase of power to detect linkage. The linkage-homogeneity test among subsamples differing for the familial disease distribution provides evidence of parameter misspecification, when the MG parameters are used. Moreover, for most of the models, use of the MG parameters in LOD-score analysis leads to a large bias in estimation of the recombination fraction and sometimes also to a rejection of linkage for the true recombination fraction. A final important point is that a strong evidence of an MG effect, obtained by segregation analysis, does not necessarily imply that linkage will be detected for at least one of the two genes, even with the true parameters and with a close informative marker.