INTERACTIVE EFFECT OF 2 CANDIDATE GENES IN A DISEASE - EXTENSION OF THE MARKER-ASSOCIATION-SEGREGATION CHI(2)-METHOD

For elucidating the genetic component of multifactorial diseases, it i s important to investigate the effect of several factors and the possi ble interaction between them. In particular, for many diseases it is i nteresting to study the interactive effect of two genes. In this conte xt, the marker-association-segregation chi(2) method (MASC), initially proposed to detect the involvement of a candidate gene in multifactor ial diseases, is developed here to investigate the involvement of two candidate genes and to model the joint effect of these two genes. In p articular, it is possible to precisely determine whether the joint eff ect of both genes is multiplicative. This extension simultaneously use s information on two markers, one for each candidate gene, at both the population and the familial segregation level. We show here that ther e can be an important gain of power to detect the effect of a second g ene in a disease when information is used simultaneously on two marker s instead of studying each marker separately. This extension of MASC i s then applied on a sample of insulin-dependent diabetes (IDD) familie s 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 (nonint eractive) effect of HLA and INS, with a biallelic locus for INS and a complementation model for HLA.