Share This Article:

Assessing spatial genetic structure from molecular marker data via principal component analyses: A case study in a Prosopis sp. forest

Abstract Full-Text HTML Download Download as PDF (Size:354KB) PP. 89-99
DOI: 10.4236/abb.2014.52013    4,572 Downloads   6,816 Views   Citations

ABSTRACT

Advances in genotyping technology, such as molecular markers, have noticeably improved our capacity to characterize genomes at multiple loci. Concomitantly, the methodological framework to analyze genetic data has expanded, and keeping abreast with the latest statistical developments to analyze molecular marker data in the context of spatial genetics has become a difficult task. Most methods in spatial statistics are devoted to univariate data whereas the nature of molecular marker data is highly dimensional. Multivariate methods are aimed at finding proximities between entities characterized by multiple variables by summarizing information in few synthetic variables. In particular, Principal Component analysis (PCA) has been used to study genetic structure of geo-referenced allele frequency profiles, incorporating spatial information with a posteriori analysis. Conversely, the recently developed spatially restricted PCA (sPCA) explicitly includes spatial data in the optimization criterion. In this work, we compared the results of the application of PCA and sPCA in the study of the spatial genetic structure at fine scale of a Prosopis flexuosa and P. chilensis hybrid swarm. Data consisted in the genetic characterization of 87 trees sampled in Córdoba, Argentina and genotyped at six microsatellites, which yielded 72 alleles. As expected, principal components explained more variance than sPCA components, but were less spatially autocorrelated. The maps obtained by the interpolation of sPC1 values allowed a better visualization of a patchy spatial pattern of genetic variability than the PC1 synthetic map. We also proposed a PC-sPC scatter plot of allele loadings to better understand the allele contributions to spatial genetic variability.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Teich, I. , Verga, A. and Balzarini, M. (2014) Assessing spatial genetic structure from molecular marker data via principal component analyses: A case study in a Prosopis sp. forest. Advances in Bioscience and Biotechnology, 5, 89-99. doi: 10.4236/abb.2014.52013.

References

[1] Keitt, T.H., Bjørnstad, O.N., Dixon, P.M. and Citron-Pousty, S. (2002) Accounting for spatial pattern when modeling organism-environment interactions. Ecography, 25, 616-625.
http://dx.doi.org/10.1034/j.1600-0587.2002.250509.x
[2] Vucetich, J. and Waite, T. (2003) Spatial patterns of demography and genetic processes across the species’ range: Null hypotheses for landscape conservation genetics. Conservation Genetics, 4, 639-645.
http://dx.doi.org/10.1023/A:1025671831349
[3] Vekemans, X. and Hardy, O.J. (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Molecular Ecology, 13, 921-935.
http://dx.doi.org/10.1046/j.1365-294X.2004.02076.x
[4] Wright, S. (1943) Isolation by distance. Genetics, 28, 114-138.
[5] Wright, S. (1946) Isolation by distance under diverse systems of mating. Genetics, 31, 39-59.
[6] Epperson, B.K. (1993) Spatial and space-time correlations in systems of subpopulations with genetic drift and migration. Genetics, 133, 711-727.
[7] Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209-220.
[8] Moran, P.A.P. (1950) Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.
[9] Smouse, P.E. and Peakall, R. (1999) Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity (Edinb), 82, 561-573.
http://dx.doi.org/10.1038/sj.hdy.6885180
[10] Cavalli-Sforza, L.L. (1966) Population structure and human evolution. Proceedings of the Royal Society of London. Series B, Biological Sciences, 164, 362-379.
http://dx.doi.org/10.1098/rspb.1966.0038
[11] Jombart, T., Pontier, D. and Dufour, A.B. (2009) Genetic markers in the playground of multivariate analysis. Heredity (Edinb), 102, 330-341.
[12] Balzarini, M., Teich, I., Bruno, C. and Peña, A. (2011) Making genetic biodiversity measurable: A review of statistical multivariate methods to study variability at gene level. Revista de la Facultad de Ciencias Agrarias de la Universidad Nacional de Cuyo, 43, 261-275.
[13] Wang, C., Zöllner, S. and Rosenberg, N.A. (2012) A quantitative comparison of the similarity between genes and geography in worldwide human populations. PLoS Genetics, 8, e1002886.
http://dx.doi.org/10.1371/journal.pgen.1002886
[14] Hotelling, H. (1933) Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441.
http://dx.doi.org/10.1037/h0071325
[15] Manel, S., Joost, S., Epperson, B.K., Holderegger, R., Storfer, A., Rosenberg, M.S., et al. (2010) Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Molecular Ecology, 19, 3760-3772. http://dx.doi.org/10.1111/j.1365-294X.2010.04717.x
[16] Manel, S., Schwartz, M.K., Luikart, G. and Taberlet, P. (2003) Landscape genetics: Combining landscape ecology and population genetics. Trends in Ecology & Evolution, 18, 189-197.
http://dx.doi.org/10.1016/S0169-5347(03)00008-9
[17] Manel, S. and Segelbacher, G. (2009) Perspectives and challenges in landscape genetics. Molecular Ecology, 19, 1821-1822.
http://dx.doi.org/10.1111/j.1365-294X.2009.04151.x
[18] Storfer, A., Murphy, M.A., Evans, J.S., Goldberg, C.S., Robinson, S., Spear, S.F., et al. (2007) Putting the “landscape” in landscape genetics. Heredity (Edinb), 98, 128-142. http://dx.doi.org/10.1038/sj.hdy.6800917
[19] Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A.R., Auton, A., et al. (2008) Genes mirror geography within Europe. Nature, 456, 98-101.
http://dx.doi.org/10.1038/nature07331
[20] Thioulouse, J., Chessel, D. and Champely, S. (1995) Multivariate analysis of spatial patterns: A unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1-14.
http://dx.doi.org/10.1007/BF00452928
[21] Wartenberg, D. (1985) Multivariate spatial correlation: A method for exploratory geographical analysis. Geographical Analysis, 17, 263-283.
http://dx.doi.org/10.1111/j.1538-4632.1985.tb00849.x
[22] Jombart, T., Devillard, S., Dufour, A.B. and Pontier, D. (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity (Edinb), 101, 92-103. http://dx.doi.org/10.1038/hdy.2008.34
[23] Legendre, P. and Legendre, L. (1998) Numerical ecology. Elsevier Science B.V., Amsterdam.
[24] Upton, G.J.G. and Fingleton, B. (1985) Spatial data analysis by example. Wiley, Chichester/New York.
[25] Gabriel, K.R. and Sokal, R.R. (1969) A new statistical approach to geographic variation analysis. Systematic Biology, 18, 259-278.
[26] Mottura, M.C. (2006) Development of microsatellites in Prosopis spp. and their application to study the reproduction system. Library of Lower Saxony State and Georg-August University of Göttingen, Göttingen.
[27] Verga, A. (2000) Clave para la identificación de híbridos entre Prosopis chilensis y P. flexuosa sobre la base de carcateres cuantitativos. Multequina, 9, 17-22.
[28] Mottura, M.C., Finkeldey, R., Verga, A.R. and Gailing, O. (2005) Development and characterization of microsatellite markers for Prosopis chilensis and Prosopis flexuosa and cross-species amplification. Molecular Ecology Notes, 5, 487-489.
http://dx.doi.org/10.1111/j.1471-8286.2005.00965.x
[29] Jombart, T. (2008) Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics, 24, 1403-1405.
http://dx.doi.org/10.1093/bioinformatics/btn129
[30] R Development Core Team, R. (2011) R: A language and environment for statistical computing. Vienna, Austria.
[31] Bivand, R., Altman, M., Anselin, L., Assunção, R., Berke, O., Bernat, A., et al. (2011) Spdep: Spatial dependence: weighting schemes, statistics and models. R package version 0.5-31.
[32] Dray, S. and Dufour, A.B. (2007) The ade4 package: Implementing the duality diagram for ecologists. Journal of Statistical Software, 22, 1-20.
[33] Di Rienzo, J.A., Casanoves, F., Balzarini, M.G., Gonzalez, L., Tablada, M. and Robledo, C.W. (2011) InfoStat.
[34] Xuebin, Q., Jianlin, H., Lkhagva, B., Chekarova, I., Badamdorj, D., Rege, J.E.O., et al. (2005) Genetic diversity and differentiation of Mongolian and Russian yak populations. Journal of Animal Breeding and Genetics, 122, 117-126. http://dx.doi.org/10.1111/j.1439-0388.2004.00497.x
[35] Matsuoka, Y., Vigouroux, Y., Goodman, M.M., Sanchez G., J., Buckler, E. and Doebley, J. (2002) A single domestication for maize shown by multilocus microsatellite genotyping. Proceedings of the National Academy of Sciences, 99, 6080-6084.
[36] Weir, B.S. (1996) Genetic data analysis II: Methods for discrete population genetic data. Sinauer Associates, Sunderland.
[37] Demey, J.R., Vicente-Villardón, J.L., Galindo-Villardón, M.P. and Zambrano, A.Y. (2008) Identifying molecular markers associated with classification of genotypes by External Logistic Biplots. Bioinformatics, 24, 2832-2838.
http://dx.doi.org/10.1093/bioinformatics/btn552
[38] Laloë, D., Moazami-Goudarzi, K., Lenstra, J.A., Marsan, P.A., Azor, P., Baumung, R., et al. (2010) Spatial trends of genetic variation of domestic ruminants in Europe. Diversity, 2, 932-945.
http://dx.doi.org/10.3390/d2060932
[39] Bessega, C., Pometti, C.L., Ewens, M., Saidman, B.O. and Vilardi, J.C. (2012) Strategies for conservation for disturbed Prosopis alba (Leguminosae, Mimosoidae) forests based on mating system and pollen dispersal parameters. Tree Genetics & Genomes, 8, 277-288.
http://dx.doi.org/10.1007/s11295-011-0439-6
[40] Pasiecznik, N.M., Felker, P., Harris, P.J.C., Harsh, L.N., Cruz, G., Tewari, J.C., et al. (2001) The Prosopis juliflora-Prosopis pallida complex: A monograph. HDRA, Coventry.
[41] Palacios, R. (1998) Taxonomía numérica (Descriptores). Prosopis en la Argentina. Facultad de Ciencias Agrarias, Universidad Nacional de Córdoba, Argentina, 91-96.
[42] Saidman, B.O., Bessega, C.F., Ferreira, L.I., Julio, N. and Vilardi, J. (2000) The use of genetic markers to assess population structure and relationships among species of the genus Prosopis (Leguminosae). Boletín de la Sociedad Argentina de Botánica, 35, 315-324.
[43] Ferreyra, L., Vilardi, J., Verga, A., López, V. and Saidman, B. (2013) Genetic and morphometric markers are able to differentiate three morphotypes belonging to Section Algarobia of genus Prosopis (Leguminosae, Mimosoideae). Plant Systematics and Evolution, 299, 1157-1173. http://dx.doi.org/10.1007/s00606-013-0786-x
[44] Verga, A.R. (1995) Genetische untersuchungen an Prosopis chilensis und P. flexuosa (Mimosaceae) im trockenen Chaco Argentiniens. Göttingen Research Notes in Forest Genetics. Abteilung für Forstgenetik und Forstpflanzenzüchtung der Universität Göttingen, 19, 1-96.
[45] Harrison, R.G. (1990) Hybrid zones: Windows on evolutionary process. Oxford Surveys in Evolutionary Biology, 7, 59.
[46] Barton, N.H. (2001) The role of hybridization in evolution. Molecular Ecology, 10, 551-568.
http://dx.doi.org/10.1046/j.1365-294x.2001.01216.x
[47] Soltis, P.S. and Soltis, D.E. (2009) The role of hybridization in plant speciation. Annual Review of Plant Biology, 60, 561-588.
http://dx.doi.org/10.1146/annurev.arplant.043008.092039

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.