Search

The marker association segregation chi-square (MASC) method was applied to a sample of 416 Caucasians affected with insulin-dependent diabetes mellitus (IDDM), for which information on the parental and sibship status was available, as well as HLA typing. We show that the model which best explains all the observations assumes a cis or trans complementation of two tightly linked genes within the HLA region, an additional maternal effect, and other familial factors. The HLA molecule corresponding to the complementation of Arg52(+) and Asp57(-) has been recently proposed as explaining susceptibility to IDDM. However, this hypothesis does not account for the overall observations made on the HLA marker in IDDM patients and their relatives. The MASC method may also be applied to evaluate the risk for relatives of an affected individual (the "index"). For example, the risk for a sib depends not only on the parental status and on the number of HLA haplotypes he shares with the index, but also on which haplotype the index himself inherited from his mother and father.

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.

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.

For elucidating the genetic component of multifactorial diseases, it is important to investigate the effect of several factors and the possible interaction between them. In particular, for many diseases it is interesting to study the interactive effect of two genes. In this context, the marker-association-segregation chi 2 method (MASC), initially proposed to detect the involvement of a candidate gene in multifactorial diseases, is developed here to investigate the involvement of two candidate genes and to model the joint effect of these two genes. In particular, it is possible to precisely determine whether the joint effect of both genes is multiplicative. This extension simultaneously uses information on two markers, one for each candidate gene, at both the population and the familial segregation level. We show here that there can be an important gai of power to detect the effect of a second gene in a disease when information is used simultaneously on two markers instead of studying each marker separately. This extension of MASC is then applied on a sample of insulin-dependent diabetes (IDD) families typed for the markers of two candidate regions: HLA and that of the insulin gene (INS). This analysis allows us to confirm the involvement of INS in IDD, and the best-fitting model is a multiplicative (noninteractive) effect of HLA and INS, with a biallelic locus for INS and a complementation model for HLA.