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Abstract:
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The explosion of molecular level data presents an exciting opportunity to better understand the genetic underpinnings of complex diseases. With this in mind, researchers across a range of scientific disciplines have proposed an array of analytic techniques for the analysis of data arising from genetic association studies. Unfortunately, this broad spectrum of methodologies, with at times very distinct nomenclature, can be difficult to contextualize within the larger statistical literature. Thus as educators, we are put to the charge of both defining and communicating a wealth of disparate and complex approaches. Coupled with this is the challenge of identifying contemporary software tools for their application. In this presentation, I describe my experiences and lessons learned from writing and teaching from the elementary graduate level textbook Applied Statistical Genetics with R.
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