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Genetic estimates of population age in the water flea, Daphnia magna. 2012

John D Robinson, and Christoph R Haag, and David W Hall, and V Ilmari Pajunen, and John P Wares
Department of Genetics, University of Georgia, Athens, GA 30602, USA. robinson.johnd@gmail.com

Genetic datasets can be used to date evolutionary events, even on recent time scales if sufficient data are available. We used statistics calculated from multilocus microsatellite datasets to estimate population ages in data generated through coalescent simulations and in samples from populations of known age in a metapopulation of Daphnia magna in Finland. Our simulation results show that age estimates improve with additional loci and define a time frame over which these statistics are most useful. On the most recent time scales, assumptions regarding the model of mutation (infinite sites vs. stepwise mutation) have little influence on estimated ages. In older populations, size homoplasy among microsatellite alleles results in a downwards bias for estimates based on the infinite sites model (ISM). In the Finnish D. magna metapopulation, our genetically derived estimated ages were biased upwards. Potential sources of this bias include the underlying model of mutation, gene flow, founder size, and the possibility of persistent source populations in the system. Our simulated data show that genetic age estimation is possible, even for very young populations, but our empirical data highlight the importance of factors such as migration when these statistics are applied in natural populations.

UI MeSH Term Description Entries
D008957 Models, Genetic Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment. Genetic Models,Genetic Model,Model, Genetic
D009154 Mutation Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations. Mutations
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D003621 Daphnia A diverse genus of minute freshwater CRUSTACEA, of the suborder CLADOCERA. They are a major food source for both young and adult freshwater fish. Daphnias
D005387 Finland A country in northern Europe, bordering the Baltic Sea, Gulf of Bothnia, and Gulf of Finland, between Sweden and Russia. The capital is Helsinki. Aland Islands,Åland Islands
D005828 Genetics, Population The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES. Population Genetics
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D051456 Gene Flow The change in gene frequency in a population due to migration of gametes or individuals (ANIMAL MIGRATION) across population barriers. In contrast, in GENETIC DRIFT the cause of gene frequency changes are not a result of population or gamete movement. Flow, Gene
D018895 Microsatellite Repeats A variety of simple repeat sequences that are distributed throughout the GENOME. They are characterized by a short repeat unit of 2-8 basepairs that is repeated up to 100 times. They are also known as short tandem repeats (STRs). Microsatellite Markers,Pentanucleotide Repeats,Simple Repetitive Sequence,Tetranucleotide Repeats,Microsatellites,Short Tandem Repeats,Simple Sequence Repeats,Marker, Microsatellite,Markers, Microsatellite,Microsatellite,Microsatellite Marker,Microsatellite Repeat,Pentanucleotide Repeat,Repeat, Microsatellite,Repeat, Pentanucleotide,Repeat, Short Tandem,Repeat, Simple Sequence,Repeat, Tetranucleotide,Repeats, Microsatellite,Repeats, Pentanucleotide,Repeats, Short Tandem,Repeats, Simple Sequence,Repeats, Tetranucleotide,Repetitive Sequence, Simple,Repetitive Sequences, Simple,Sequence Repeat, Simple,Sequence Repeats, Simple,Sequence, Simple Repetitive,Sequences, Simple Repetitive,Short Tandem Repeat,Simple Repetitive Sequences,Simple Sequence Repeat,Tandem Repeat, Short,Tandem Repeats, Short,Tetranucleotide Repeat

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