Applications of the estimating equations theory to genetic epidemiology: areview

Unlike monogenic diseases for which considerable progress has been made in past years. the identification of susceptibility genes involved in multifac torial diseases still poses numerous challenges, including the development of new statistical methodologies. Recently, several authors have advocated the use of the estimating equations (EE) approach as an alternative to stan dard maximum likelihood methods for analysing correlated data. Since most g enetic studies rely on family data, the EE found a natural field of applica tion in genetic epidemiology. The objective of this review is to give a bri ef description of the EE principles, and to outline its applications in the main areas of genetic epidemiology, including familial aggregation analysi s, segregation analysis, linkage analysis and association studies.