Most common diseases have substantial heritable components but are characterized by complex inheritance patterns implicating numerous genetic and environmental factors. A longstanding goal of human genetics research is to delineate the genetic architecture of these traits - the number, frequencies, and effect sizes of disease-causing alleles - to inform mapping studies, elucidate mechanisms of disease, and guide development of targeted clinical therapies and diagnostics. Although vast empirical genetic data has now been collected for common diseases, different and contradictory hypotheses have been advocated about features of genetic architecture (e.g., the contribution of rare vs. common variants). Here, we present a framework which combines multiple empirical datasets and simulation studies to enable systematic testing of hypotheses about both global and locus-specific complex trait architecture. We apply this to type 2 diabetes (T2D).
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/11181075 |
| Date | 18 October 2013 |
| Creators | Agarwala, Vineeta |
| Contributors | Altshuler, David Matthew |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Rights | open |
Page generated in 0.0025 seconds