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Evaluating empirical bounds on complex disease genetic architecture

Abstract

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.

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Figure 1
Figure 2: Patterns of genetic variation: forward simulated versus empirically observed.
Figure 3: Sensitivity of genetic architecture to parameters of disease models.
Figure 4: Genetic study results for T2D under different disease models.
Figure 5: Simulated study results under representative disease models and comparison to T2D empirical data.
Figure 6: Prediction of ongoing sequencing and large-scale genotyping studies for T2D under different disease models consistent with empirical data.

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Acknowledgements

We gratefully acknowledge B. Lambert and K. Weiss (authors of the simulation tool ForSim) for helpful conversation, encouragement and technical assistance. Without their software, this work would not have been possible. We thank B. Voight for tremendous help in matching simulated genetic studies to those empirically conducted for T2D. We also thank E. Lander, C. Hartl, P. Fontanillas, B. Neale, M. McCarthy, M. Boehnke, M. Daly, S. Purcell and E. Stahl for discussion and insightful critiques. This work was supported by grants from the Doris Duke Charitable Foundation (award 2006087 to D.A.), the National Institute of General Medical Sciences (NIGMS; award R01GM078598 to S.S.) and the National Institute of Mental Health (NIMH; grant R01MH084676 to S.S.). V.A. is also supported by US National Institutes of Health (NIH) Training grants T32GM007753 and T32GM008313. J.F. is supported in part by NIH Training grant T32GM007748-33 as well as by funding from Pfizer. The GoT2D Study is supported by grant 1RC2DK088389-01 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

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V.A., J.F., S.S. and D.A. conceived and designed the simulation framework. V.A., J.F. and S.S. fit population genetic parameters to match simulated and empirical data. V.A. performed the simulation studies. V.A. and J.F. analyzed simulation data under each disease model. V.A., J.F. and D.A. wrote the manuscript. All authors edited and approved the manuscript.

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Correspondence to David Altshuler.

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Agarwala, V., Flannick, J., Sunyaev, S. et al. Evaluating empirical bounds on complex disease genetic architecture. Nat Genet 45, 1418–1427 (2013). https://doi.org/10.1038/ng.2804

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