Evaluating empirical bounds on complex disease genetic architecture.

Area of Science:

• Genetics
• Human Disease
• Personalized Medicine

Background:

• The genetic architecture of human diseases influences genetic mapping and personalized medicine.
• Contradictory hypotheses exist regarding the contribution of rare versus common variants to common disease heritability.

Purpose of the Study:

• To develop an integrated simulation framework to systematically evaluate hypotheses about genetic architecture.
• To define and explore the space of possible genetic architectures for type 2 diabetes (T2D).

Main Methods:

• Developed an integrated simulation framework calibrated to empirical data.
• Utilized epidemiology, linkage, and genome-wide association studies to constrain models.
• Defined T2D genetic architecture by target size for causal mutation and coupling between selection and phenotypic effect.

Main Results:

• Two parameters define a broad space of T2D genetic architectures.
• Extreme models are excluded, but models where rare variants explain <25% or >80% of heritability remain consistent.
• Very large sample sizes (>250,000 individuals) are needed to localize most T2D heritability.

Conclusions:

• The genetic basis of T2D is complex and can be explained by models with varying contributions from rare and common variants.
• Ongoing and future genetic studies, particularly with large sample sizes, are essential for a complete understanding of T2D heritability.
• This framework aids in evaluating genetic architecture hypotheses for complex human diseases.