Smouse PE, Peakall R. Spatial autocorrelation analysis of multi-allele and multi-locus genetic microstructure. Heredity 82: 561-573

Department of Ecology, Evolution and Natural Resources and Center for Theoretical & Applied Genetics, Cook College, Rutgers University, New Brunswick, NJ 08901-8551, USA.
Heredity (Impact Factor: 3.81). 06/1999; 82 ( Pt 5)(5):561-73. DOI: 10.1038/sj.hdy.6885180
Source: PubMed


Population genetic theory predicts that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricted, but as an empirical reality, spatial structure is rarely consistent across loci or sites, and is generally weak. A lack of sensitivity in the statistical procedures may explain the discrepancy. Most work to date, based on allozymes, has involved pattern analysis for individual alleles, but new PCR-based genetic markers are coming into vogue, with vastly increased numbers of alleles. The field is badly in need of an explicitly multivariate approach to autocorrelation analysis, and our purpose here is to introduce a new approach that is applicable to multiallelic codominant, multilocus arrays. The procedure treats the genetic data set as a whole, strengthening the spatial signal and reducing the stochastic (allele-to-allele, and locus-to-locus) noise. We (i) develop a very general multivariate method, based on genetic distance methods, (ii) illustrate it for multiallelic codominant loci, and (iii) provide nonparametric permutational testing procedures for the full correlogram. We illustrate the new method with an example data set from the orchid Caladenia tentaculata, for which we show (iv) how the multivariate treatment compares with the single-allele treatment, (v) that intermediate frequency alleles from highly polymorphic loci perform well and rare alleles poorly, (vi) that a multilocus treatment provides clearer answers than separate single-locus treatments, and (vii) that weighting alleles differentially improves our resolution minimally. The results, though specific to Caladenia, offer encouragement for wider application.

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    • "The two matrices were then analyzed using the Isolation by Distance Web Service v3.23 (Jensen et al. 2005). We also performed a spatial autocorrelation analysis (Smouse and Peakall 1999) as implemented by GenAlEx to compare the genetic similarity among individuals at different intervals of distance. For this analysis we selected one individual from each unique trapping site (n = 150 individuals) in order to not exceed the computational capacity of the software. "

    Full-text · Dataset · Aug 2015
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    • "Existence of isolation-by-distance between individuals was tested using the Mantel test with 999 permutations on matrices of linearized genetic distance between individuals (Smouse and Peakall 1999) and a corresponding log-geographic distance in GenAlEx 6.5 (Peakall and Smouse 2012). "
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    • "We performed spatial autocor relation analyses in GENA LEX using genetic and geographic distance matrices. The detection of a positive spatial autocorrelation coefficient r (Smouse & Peakall 1999) that is significantly greater than ex pected under conditions of panmixia at the smallest distance classes is indicative of restricted dispersal (see Supplement 1 at com/ articles/ suppl/ m532 p001_ supp. "
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