Supplementary Material for: Allelic Heterogeneity in Genetic Association Meta-Analysis: An Application to <i>DTNBP1</i> and Schizophrenia

<i>Background/Aims:</i> 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. <i>Methods:</i> 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 <i>DTNBP1</i> 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 We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the <i>DTNBP1</i> locus. <i>Results:</i> 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. <i>Conclusion:</i> 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.