Interactive effect of two candidate genes in a disease: extension of the marker-association-segregation chi(2) method.

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.