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

• Published in: Theoretical population biology Vol. 104; pp. 46 - 58
• Main Authors: Mazet, Olivier, Rodríguez, Willy, Chikhi, Lounès
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2015; Elsevier
• Subjects: Applications; Coalescence time; Demographic history; Genetics; Genetics, Population; Humans; Life Sciences; Mathematics; Maximum likelihood estimation; Models, Genetic; Population Density; Population Dynamics; Population size change; Populations and Evolution; Probability; Quantitative Methods; Statistics; Symmetric island model; Maximum likelihood estimation; Demographic history; Coalescence time; Symmetric island model; Population size change
• ISSN: 0040-5809; 1096-0325
• DOI: 10.1016/j.tpb.2015.06.003

The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyze two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model rejection procedure by using a Kolmogorov–Smirnov test, and a model choice procedure based on the AIC, and (v) derive the explicit distribution for the number of differences between two non-recombining sequences. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual.