Pattern-oriented modelling of population genetic structure
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2014
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Resumo
Although several statistical approaches can be used to describe patterns of genetic variation and infer stochastic
differentiation, selective responses, or interruptions of gene flow due to physical or environmental barriers, it is
worthwhile to note that similar processes, controlled by several parameters in theoretical models, frequently give
rise to similar patterns. Here, we develop a Pattern-Oriented Modelling (POM) approach that allows us to
determine how a complex set of parameters potentially driving empirical genetic differentiation among populations
generate alternative scenarios that can be fitted to observed data. We generated 10 000 random combinations of
parameters related to population size, gene flow and response to gradients (both driven by dispersal and selection)
in a spatially explicit model, and analysed simulated patterns with FST statistics and mean correlograms using
Moran’s I spatial autocorrelation coefficients. These statistics were compared with observed patterns for a tree
species endemic to the Brazilian Cerrado. For a best match with observed FST (equal to 0.182), the important
parameters driving simulated scenario are mainly related to population structure, including low population size
with closed populations (low Nm), strong distance decay of gene flow, in addition to a strong effect of the initial
variance of allele frequencies. These scenarios present a low autocorrelation of allele frequencies. Best matching
of correlograms, on the other hand, appears in simulations with a large population size, high Nm and low population
differentiation and FST (as well as more gene flow). Thus, targeting the two statistics (correlograms and FST) shows
that best matches with empirical data with two distinct sets of parameters in the simulations, because observed
patterns involve both a relatively high FST and significant autocorrelation. This conflict can be resolved by
assuming that initial variance in allele frequencies can be interpreted as reflecting deep-time historical variation
and evolutionary dynamics of allele frequencies, creating a relatively high level of population differentiation,
whereas current patterns in gene flow creates spatial autocorrelation. This make sense in terms of the previous
knowledge on population differentiation in D. alata, especially if patterns are explained by a combination of
isolation-by-distance and allelic surfing due to range expansion after the last glacial maximum. This reveals the
potential for more complex applications of POM in population genetics.
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Palavras-chave
Autocorrelation, Correlograms, F-statistics, Population structure, Geographical genetics, Pattern, Simulation, Oriented Modelling
Citação
DINIZ-FILHO, Jose Alexandre Felizola; SOARES, Thannya Nascimento; TELLES, Mariana Pires de Campos. Pattern-oriented modelling of population genetic structure. Biological Journal of the Linnean Society, Kettering , v. 113, n. 4, p. 1152-1161, 2014.