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The rapid development of sequencing technologies represents new opportunitiesfor population genetics research. It is expected that genomic data willincrease our ability to reconstruct the history of populations. While thisincrease in genetic information will likely help biologists and anthropologiststo reconstruct the demographic history of populations, it also represents newchallenges. Recent work has shown that structured populations generate signalsof population size change. As a consequence it is often difficult to determinewhether demographic events such as expansions or contractions (bottlenecks)inferred from genetic data are real or due to the fact that populations arestructured in nature. Given that few inferential methods allow us to accountfor that structure, and that genomic data will necessarily increase theprecision of parameter estimates, it is important to develop new approaches. Inthe present study we analyse two demographic models. The first is a model ofinstantaneous population size change whereas the second is the classicalsymmetric island model. We (i) re-derive the distribution of coalescence timesunder the two models for a sample of size two, (ii) use a maximum likelihoodapproach to estimate the parameters of these models (iii) validate thisestimation procedure under a wide array of parameter combinations, (iv)implement and validate a model choice procedure by using a Kolmogorov-Smirnovtest. Altogether we show that it is possible to estimate parameters underseveral models and perform efficient model choice using genetic data from asingle diploid individual.

Demographic inference using genetic data from a single individual: separating population size variation from population structure