The Journal of Heredity 2001:92(5)
© 2001 The American Genetic Association 92:447-448
Computer Note |
SGS—Spatial Genetic Software: A Computer Program for Analysis of Spatial Genetic and Phenotypic Structures of Individuals and Populations
From SILVOLAB Guyane, INRA Station de Recherches Forestières, Campus agronomique, BP 709, 97387—Kourou cedex, France D.O.M. Guyane (French Guiana) (Degen), and INRA Station de Recherches Forestières, Laboratoire de Génétique des Arbres Forestiers, BP 45, F-33611 Gazinet cedex, France (Petit and Kremer).
Address correspondence to B. Degen at the address above or e-mail: degen.b{at}kourou.cirad.fr.
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Abstract |
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We have developed a program called Spatial Genetic Software (SGS), which provides a user-friendly Windows tool to analyze both local and broad scale genetic and phenotypic structure. It can deal with nearly any type of genetic data, codominant (allozyme, PCR-RFLP, microsatellite) or dominant (RAPD, AFLP) markers, or biparentally (nuclear) or uniparentally (cpDNA and mtDNA) inherited markers. Data based on any of these markers can be analyzed, either as individual genotypes within a single population (local scale) or as allele or haplotype frequencies from different populations (broad scale). We also include a simple approach to analysis of spatial structure for continuous quantitative traits. The program implements various parameters to analyze spatial genetic and phenotypic structure: Moran's index, Geary's index, number of alleles in common, and approaches using genetic distances and FST values. The statistical significance of all measures is verified by the use of a permutation test. The results are assessed by graphics that can be integrated, via the clipboard, to other Windows programs. The details of the computations are given in a table and can be stored as ASCII files.
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Introduction |
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Over the last 20 years, following the seminal articles of Sokal and Oden (1978a, b), the analysis of spatial genetic structure has become an important issue (e.g., Epperson 1992). The majority of the studies, based on allozymes, represent either broad-scale analyses of allele frequencies (e.g., Jones et al. 1980; Sokal and Menozzi 1982) or complete inventories within a population of sessile organisms, such as plants (e.g., Bacilieri et al. 1994; Chung et al. 1998; Epperson and Allard 1989; Leonardi et al. 1996; Merzeau et al. 1994; Waser 1987). Studies using DNA markers are becoming more numerous, both on a cross-population scale (e.g., Dumolin-Lapègue et al. 1997; Petit et al. 1993, 1997) and within a single population (e.g., Streiff et al. 1998; Wagner et al. 1991).
Spatial statistical approaches differ from other statistical approaches that are common in population genetic analyses by emphasizing the interaction of a population or an individual with its neighbors. Local spatial structure is generated after a few generations as a consequence of fine-scale genetic processes, such as limited seed and pollen flow and local selection pressures. Spatial analysis also complements the more traditional methods aimed at detecting processes occurring at larger geographic scales, such as migration and colonization. The analysis of spatial structure enables an estimation of those forcing processes (e.g., Hardy and Vekemans 1999).
Different parameters have been used to quantify spatial genetic structure. Moran's index and Geary's index (Cliff and Ord 1973; Sokal and Oden 1978a) are among the most frequently used measures, but others have used the "number of alleles in common" to measure genetic divergence (Boshier et al. 1995). FST and GST statistics have also been used to quantify genetic structure (Dumolin-Lapègue et al. 1997; Streiff et al. 1998). More recently, multilocus measures of spatial autocorrelation, based on genetic distances, have been introduced (Cassens et al. 2000; Degen and Scholz 1998; Smouse and Peakall 1999; Vendramin et al. 1999).
Other population genetics programs offer complementary but generally limited features for the analysis of spatial genetic structure. Arlequin (Schneider et al. 2000) has a Mantel test procedure, which computes the correlation between genetic and other distance matrices, whereas GENEPOP (Raymond and Rousset 1995) includes a routine for regression of FST or FST/(1 - FST) onto geographic distance, and also performs a Mantel test.
A versatile software for analysis of spatial genetic and phenotypic structure is missing. Spatial Genetic Software (SGS), with its broad set of features for analysis, fills this gap which has been identified repeatedly (e.g., Smouse and Peakall 1999).
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Program Description |
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TopAbstractIntroductionProgram DescriptionResultsReferences |
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Spatial Distance Classes
All calculated statistics are computed for pairs of data points belonging to a series of spatial distance classes (sq). The spatial distance between two data points is computed as the Euclidean ground distance. The dimension of all distance classes is equal. To check the influence of distance class definition on the results, it is possible to repeat the calculations with different scales for distance classes. The plots of the calculated statistic as a function of interpoint distance are called correlograms for Moran's index and Geary's index or distograms for genetic distance-based measures.
Permutation Testing
A Monte Carlo permutation procedure is applied to test significant deviations from a spatially random distribution of each calculated measure (Manly 1997). Each permutation consists of a random shuffling of genetic or phenotypic data over the spatial coordinates of the sampled points. For each of the spatial distance classes, observed values are compared with a null distribution, obtained from N Monte Carlo trials. Then a user-defined 
% confidence interval for the parameters is constructed, by ordering the permuted estimates (e.g., Bacilieri et al. 1994; Streiff et al. 1998).
Analysis of the Spatial Structure
The program works with various types of genetic data, codominant (allozyme, PCR-RFLP, microsatellite) or dominant (RAPD, AFLP) markers, or biparentally (nuclear) or uniparentally (cpDNA and mtDNA) inherited markers. Data based on any of these markers can be analyzed, either as individual genotypes within a single population (local scale) or as allele or haplotype frequencies from different populations (broad scale). We also include a simple approach to analysis of spatial structure for continuous quantitative traits. The program implements various parameters to available measures. An overview is given in Table 1. The user will find details about formulas and the calculations in the manual (see below).
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Results |
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The software offers various possibilities for viewing and saving the results: distograms or correlograms, and their confidence intervals, based on the permutation test, graphs showing the spatial distribution of data points, or diagrams to view the frequencies of pairs in all spatial distance classes. All graphs of SGS can be imported, via the clipboard, to other Windows applications. Moreover, one can generate tables and files with the results of the spatial analysis and results of the permutation test.
Availability
SGS is written in Visual Basic version 5.0 (Professional Edition) and has been compiled as 32-bit versions for the Microsoft Windows (Windows 95/98/00 and Windows NT) operating system. The program and the user's manual are available on our Internet homepage: http://kourou.cirad.fr/genetique/software.html.
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Acknowledgments |
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We are grateful to Hans-Rolf Gregorius, Giovanni Vendramin, and Birgit Ziegenhagen for valuable discussions and suggestions about the program. We would also like to thank Henri Caron, Cyril Dutech, Brigitte Demesure, and Sylvie Oddou for critical testing of the program and for helpful suggestions. We also wish to thank Peter Smouse and two anonymous reviewers for their very helpful comments and improvements to the manuscript.
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Footnotes |
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Corresponding Editor: Sudhir Kumar
Received April 23, 2001
Accepted June 30, 2001
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References |
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