Title: Evaluating empirical bounds on complex disease genetic architecture
Abstract: David Altshuler and colleagues explore the genetic architecture of type 2 diabetes (T2D) using an integrated population genetics–based simulation framework calibrated with empirical data. Whereas they are able to exclude more extreme models, for example, those in which either common or rare variants explain all of the disease heritability, they find that a broad range of architecture remains consistent with current empirical data and suggest that continued large-scale sequencing and genotyping studies will be needed to more precisely characterize the genetic architecture of complex traits such as T2D. The genetic architecture of human diseases governs the success of genetic mapping and the future of personalized medicine. Although numerous studies have queried the genetic basis of common disease, contradictory hypotheses have been advocated about features of genetic architecture (for example, the contribution of rare versus common variants). We developed an integrated simulation framework, calibrated to empirical data, to enable the systematic evaluation of such hypotheses. For type 2 diabetes (T2D), two simple parameters—(i) the target size for causal mutation and (ii) the coupling between selection and phenotypic effect—define a broad space of architectures. Whereas extreme models are excluded by the combination of epidemiology, linkage and genome-wide association studies, many models remain consistent, including those where rare variants explain either little (<25%) or most (>80%) of T2D heritability. Ongoing sequencing and genotyping studies will further constrain the space of possible architectures, but very large samples (for example, >250,000 unselected individuals) will be required to localize most of the heritability underlying T2D and other traits characterized by these models.