Assessment of functional convergence across study designs in autism

Background Disagreements over genetic signatures associated with disease have been particularly prominent in the field of psychiatric genetics, creating a sharp divide between disease burdens attributed to common and rare variation, with study designs independently targeting each. Meta-analysis within each of these study designs is routine, whether using raw data or summary statistics, but no method for combining the functional conclusions across study designs exist. Method In this work, we develop a general solution which integrates the disparate genetic contributions constrained by their observed effect sizes to determine functional convergence in the underlying architecture of complex diseases, which we illustrate on autism spectrum disorder (ASD) data. Our approach looks not only for similarities in the functional conclusions drawn from each study type individually but also those which are consistent with the known effect sizes across these studies. We name this the "functional effect size trend" and it can be understood as a generalization of a classic meta-analytic method, the funnel plot test. Results We detected remarkably significant trends in aggregate (p~ 1.92e-31) with 20 individually significant properties (FDR<0.01), many in areas researchers have targeted based on different reasoning, such as the fragile X mental retardation protein (FMRP) interactor enrichment (FDR~0.006). We are also able to detect novel technical effects and we see that network enrichment from protein-protein interaction data is heavily confounded with study design, arising readily in control data. Conclusions As a novel means for meta-analysis across study designs, this method reveals a convergent functional signal in ASD from all sources of genetic variation. Meta-analytic approaches explicitly accounting for different study designs can be adapted to other diseases to discover novel functional associations and increase statistical power.