TESTING ASSOCIATION BETWEEN CANDIDATE-GENE MARKERS AND PHENOTYPE IN RELATED INDIVIDUALS, BY USE OF ESTIMATING EQUATIONS

Association studies are one of the major strategies for identifying ge netic factors underlying complex traits. In samples of related individ uals, conventional statistical procedures are not valid for testing as sociation, and maximum likelihood (ML) methods have to be used, but th ey are computationally demanding and are not necessarily robust to vio lations of their assumptions. Estimating equations (EE) offer an alter native to ML methods, for estimating association parameters in correla ted data. We studied through simulations the behavior of EE in a large range of practical situations, including samples of nuclear families of varying sizes and mixtures of related and unrelated individuals. Fo r a quantitative phenotype, the power of the EE test was comparable to that of a conventional ML test and close to the power expected in a s ample of unrelated individuals. For a binary phenotype, the power of t he EE test decreased with the degree of clustering, as did the power o f the ML test. This result might be partly explained by a modeling of the correlations between responses that is less efficient than that in the quantitative case. In small samples (<50 families), the variance of the EE association parameter tended to be underestimated, leading t o an inflation of the type I error. The heterogeneity of cluster size induced a slight loss of efficiency of the FE estimator, by comparison with balanced samples. The major advantages of the EE technique are i ts computational simplicity and its great flexibility, easily allowing investigation of gene-gene and gene-environment interactions. It cons titutes a powerful tool for testing genotype-phenotype association in related individuals.