Evaluating empirical bounds on complex disease genetic architecture

作者: Vineeta Agarwala , Jason Flannick , Shamil Sunyaev , David Altshuler , GoT2D Consortium

DOI: 10.1038/NG.2804

关键词:

摘要: The genetic architecture of human diseases governs the success mapping and future personalized medicine. Although numerous studies have queried basis common disease, contradictory hypotheses been advocated about features (for example, contribution rare versus variants). We developed an integrated simulation framework, calibrated to empirical data, enable systematic evaluation such hypotheses. For type 2 diabetes (T2D), two simple parameters--(i) target size for causal mutation (ii) coupling between selection phenotypic effect--define a broad space architectures. Whereas extreme models are excluded by combination epidemiology, linkage genome-wide association studies, many remain consistent, including those where variants explain either little ( 80%) T2D heritability. Ongoing sequencing genotyping will further constrain possible architectures, but very large samples >250,000 unselected individuals) be required localize most heritability underlying other traits characterized these models.

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