Applications of the estimating equations theory to genetic epidemiology: a review

Abstract

Unlike monogenic diseases for which considerable progress has been made in past years, the identification of susceptibility genes involved in multifactorial 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 standard maximum likelihood methods for analysing correlated data. Since most genetic studies rely on family data, the EE found a natural field of application in genetic epidemiology. The objective of this review is to give a brief description of the EE principles, and to outline its applications in the main areas of genetic epidemiology, including familial aggregation analysis, segregation analysis, linkage analysis and association studies.