Modern computational approaches for analysing molecular genetic variation data

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Abstract
An explosive growth is occurring in the quantity, quality and complexity of molecular variation data that are being collected. Historically, such data have been analysed by using model-based methods. Models are useful for sharpening intuition, for explanation and for prediction: they add to our understanding of how the data were formed, and they can provide quantitative answers to questions of interest. We outline some of these model-based approaches, including the coalescent, and discuss the applicability of the computational methods that are necessary given the highly complex nature of current and future data sets.