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Titolo:
Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure
Autore:
Smouse, PE; Peakall, R;
Indirizzi:
Rutgers Univ, Dept Ecol, Cook Coll, New Brunswick, NJ 08901 USA Rutgers Univ New Brunswick NJ USA 08901 Coll, New Brunswick, NJ 08901 USA Rutgers Univ, Ctr Theoret & Appl Genet, Cook Coll, New Brunswick, NJ 08901USA Rutgers Univ New Brunswick NJ USA 08901 Coll, New Brunswick, NJ 08901USA Australian Natl Univ, Div Botany & Zool, Canberra, ACT 0200, Australia Australian Natl Univ Canberra ACT Australia 0200 rra, ACT 0200, Australia
Titolo Testata:
HEREDITY
, volume: 82, anno: 1999,
parte:, 5
pagine: 561 - 573
SICI:
0018-067X(199905)82:<561:SAAOIM>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
AUTO-CORRELATION ANALYSIS; BUFFALOGRASS BUCHLOE-DACTYLOIDES; NATURAL-POPULATIONS; PLANT-POPULATIONS; ARBITRARY PRIMERS; RAPD VARIATION; GENOTYPES; DISTANCE; MARKERS; POLYMORPHISMS;
Keywords:
Caladenia; multilocus allozyme analysis; spatial autocorrelation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Life Sciences
Citazioni:
44
Recensione:
Indirizzi per estratti:
Indirizzo: Smouse, PE Rutgers Univ, Dept Ecol, Cook Coll, Rm 152A,Environm & Nat Resources Sci Bldg,14 Coll, New Brunswick, NJ 08901 USA Rutgers Univ Rm 152A,Environm & Nat Resources Sci Bldg,14 Coll New Brunswick NJ USA 08901
Citazione:
P.E. Smouse e R. Peakall, "Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure", HEREDITY, 82, 1999, pp. 561-573

Abstract

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 signaland 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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Documento generato il 04/12/20 alle ore 09:51:41