title: Modern computational approaches for analysing molecular genetic variation data.
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.
authors.affiliation: University of Southern California, Keck School of Medicine, Preventive Medicine, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-99011, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
authors.forename: Simon
authors.initials: S
authors.lastname: Tavaré
mesh_terms: D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D013269:Stochastic Processes