Allelic heterogeneity in genetic association meta-analysis: an application to DTNBP1 and schizophrenia

Meta-analysis of genetic association studies is a useful approach when individual investigations do not yield studywise significant results but the evidence across studies is modest and homogeneous. Current meta-analysis methods account for heterogeneity by down-weighting studies as a function of between-study variance. We contend that current approaches may obscure interesting phenomena in genetic association data. However, an appropriate approach to examining heterogeneity across studies is lacking.

We develop a novel approach, based on the EM algorithm, to detect allelic heterogeneity, identify subpopulations and assign studies to those subpopulations. We then apply these methods to the association between DTNBP1 and schizophrenia (Scz), one of the most studied relationships in complex disease genetics. We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the DTNBP1 locus.

We explored heterogeneity in several ways including meta-regression and approaches aimed at exploring the mixture of heterogeneous studies at a particular SNP. We found significant evidence for a mixture of association distributions at multiple loci.

We propose a novel approach that is broadly applicable and may be useful in large scale genetic association meta-analyses to detect significant allelic heterogeneity.