A new eigenfunction spatial analysis describing population genetic structure
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Data
2013
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Resumo
Several methods of spatial analyses have been
proposed to infer the relative importance of evolutionary
processes on genetic population structure. Here we show
how a new eigenfunction spatial analysis can be used to
model spatial patterns in genetic data. Considering a
sample of n local populations, the method starts by modeling
the response variable (allele frequencies or phenotypic
variation) against the eigenvectors sequentially
extracted from a geographic distance matrix (n 9 n). The
relationship between the coefficient of determination (R2)
of the models and the cumulative eigenvalues, which we
named the spatial signal-representation (SSR) curve, can be
more efficient than Moran’s I correlograms in describing
different patterns. The SSR curve was also applied to
simulated data (under distinct scenarios of population differentiation)
and to analyze spatial patterns in alleles from
microsatellite data for 25 local populations of Dipteryx
alata, a tree species endemic to the Brazilian Cerrado. The
SSR curves are consistent with previous phylogeographical
patterns of the species, revealing combined effects of
isolation-by-distance and range expansion. Our analyses demonstrate that the SSR curve is a useful exploratory tool
for describing spatial patterns of genetic variability and for
selecting spatial eigenvectors for models aiming to explain
spatial responses to environmental variables and landscape
features.
Descrição
Palavras-chave
Cerrado, Dipteryx alata, Eigenfunction analyses, Spatial genetic structure, Microsatellites, Spatial autocorrelation
Citação
DINIZ-FILHO, José Alexandre Felizola et al. A new eigenfunction spatial analysis describing population genetic structure. Genetica, Heidelberg, v. 141, p. 479-489, 2013.