Meta‐analysis of genetic association studies under different inheritance models using data reported as merged genotypes

Abstract Meta‐analysis of population‐based genetic association studies is often challenged by obstacles associated with the underlying inheritance model. For a simple genetic variant with two alleles, a recessive, dominant or co‐dominant model is typically assumed. In the absence of a strong biological rationale for a particular inheritance model, a recently suggested inheritance‐model‐free approach can be implemented. To enable a flexible choice among these models, summary results from each of the three genotypes are required. Incompatibility of the data across studies because of different inheritance models is a common problem. For instance, if the underlying model is dominant, studies that have assumed the recessive model and presented the results accordingly, have so far been excluded from the meta‐analysis. We show how to combine data and make inferences under any inheritance model, irrespective of the models assumed within each study and the way that data are presented. Within a Bayesian framework we describe prospective models for binaryand continuous outcomes, and retrospective models for binary outcomes. The methods exploit an assumption of Hardy–Weinberg equilibrium, prior information about genotype prevalence or assumption of a specific inheritance model. On application to meta‐analyses of the associations between a polymorphism in the lipoprotein lipase gene and coronary heart disease or high‐density lipoprotein cholesterol, we observe substantial gains in precision when there is a large proportion of studies in which different inheritance models have been assumed. Copyright © 2007 John Wiley & Sons, Ltd.