Bivariate familial correlation analysis of quantitative traits by use of estimating equations: Application to a familial analysis of the insulin resistance syndrome

Familial correlation analysis involving two traits may give a better insigh t into the etiology of multifactorial syndromes than familial analysis focu sed on single traits. Significant cross-trait correlations between biologic al relatives but not between spouses suggest that the two traits share comm on transmissible factors whereas correlations between spouses additionally suggest the influence of shared lifestyle factors. We apply the Estimating Equations (EE) technique to the estimation of intra-trait and cross-trait f amilial correlations on two quantitative traits. Unlike maximum likelihood methods, the EE method does not require one to specify the joint distributi on of the traits. Estimation of correlations and of their variance involves an iterative three-stage algorithm which converges rapidly. The generalize d Wald test can be used to test any specific hypothesis of familial resembl ance. This method has great flexibility for handling covariates and incompl ete family data. A simulation study indicated that the EE technique perform ed well in large samples (100 families), both in terms of type I error and coverage probability. However, in small samples (50 families), an increase of the type I error and a decrease of the coverage probability was observed . As an illustration, we applied this technique to a family study of metabo lic factors involved in the Insulin Resistance Syndrome (body mass index, i nsulin, triglycerides, HDL-cholesterol, and diastolic blood pressure). The study was carried out in a sample of 216 healthy nuclear families with grea ter than or equal to 2 offspring. The results suggested the existence of a common transmissible (genetic or cultural) factor influencing both body mas s index and insulin, whereas the weak clustering of triglycerides and HDL-c holesteroI would be more compatible with the influence of shared lifestyle factors. (C) 1999 Wiley-Liss, Inc.