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Microsatellites pp 271–295Cite as

Microsatellite Data Analysis for Population Genetics

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1006))

Abstract

Theories and analytical tools of population genetics have been widely applied for addressing various questions in the fields of ecological genetics, conservation biology, and any context where the role of dispersal or gene flow is important. Underlying much of population genetics is the analysis of variation at selectively neutral marker loci, and microsatellites continue to be a popular choice of marker. In recent decades, software programs to estimate population genetics parameters have been developed at an increasing pace as computational science and theoretical knowledge advance. Numerous population genetics software programs are presently available to analyze microsatellite genotype data, but only a handful are commonly employed for calculating parameters such as genetic variation, genetic structure, patterns of spatial and temporal gene flow, population demography, individual population assignment, and genetic relationships within and between populations. In this chapter, we introduce statistical analyses and relevant population genetic software programs that are commonly employed in the field of population genetics and molecular ecology.

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References

  1. Kim KS, Sappington TW (2006) Molecular genetic variation of boll weevil populations in North America estimated with microsatellites: implications for patterns of dispersal. Genetica 127:143–161

    Article  PubMed  CAS  Google Scholar 

  2. Jiang X-F, Luo L-Z, Zhang L (2007) Amplified fragment length polymorphism analysis of Mythimna separata (Lepidoptera: Noctuidae) geographic and melanic laboratory populations in China. J Econ Entomol 100:1525–2532

    Article  PubMed  CAS  Google Scholar 

  3. Jiang X-F, Cao W-J, Zhang L, Luo L-Z (2010) Beet webworm (Lepidoptera: Pyralidae) migration in China: evidence from genetic markers. Environ Entomol 39:232–242

    Article  PubMed  Google Scholar 

  4. Nagoshi RN, Fleischer S, Meagher RL (2009) Texas is the overwintering source of fall armyworm in central Pennsylvania: implications for migration into the northeastern United States. Environ Entomol 38:1546–1554

    Article  PubMed  Google Scholar 

  5. Kim KS, Coates BS, Bagley MJ, Hellmich RL, Sappington TW (2011) Genetic structure and gene flow among European corn borer (Lepidoptera: Crambidae) populations from the Great Plains to the Appalachians of North America. Agric For Entomol 13:383–393

    Article  Google Scholar 

  6. Kim KS, Bagley MJ, Coates BS, Hellmich RL, Sappington TW (2009) Spatial and temporal genetic analyses show high gene flow among European corn borer (Lepidoptera: Crambidae) populations across the central U.S. Corn Belt. Environ Entomol 38:1312–1323

    Article  PubMed  CAS  Google Scholar 

  7. Van Oosterhout C, Hutchinson W, Wills D, Shipley P (2004) Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Resour 4:535–538

    Google Scholar 

  8. Park SDE (2001) Trypanotolerance in West African cattle and the population genetic effects of selection. Ph.D. thesis, University of Dublin

    Google Scholar 

  9. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295

    Article  Google Scholar 

  10. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567

    Article  PubMed  Google Scholar 

  11. Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program Cervus accommodates genotyping error increases success in paternity assignment. Mol Ecol 16:1099–1106

    Article  PubMed  Google Scholar 

  12. Goudet J (1995) Fstat version 1.2: a computer program to calculate F statistics (version 2.9.03). J Hered 86:485–486

    Google Scholar 

  13. Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Heredity 86:248–249

    Google Scholar 

  14. Cornuet J, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014

    PubMed  CAS  Google Scholar 

  15. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    PubMed  CAS  Google Scholar 

  16. Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A (2004) GeneClass2: a software for genetic assignment and first-generation migrant detection. Heredity 95:536–539

    Article  CAS  Google Scholar 

  17. Harley EH (2001) AGARst. A programme for calculating allele frequencies, GST and RST from microsatellite data, version 2. University of Cape Town, Cape Town, South Africa

    Google Scholar 

  18. Ota T (1993) DISPAN: genetic distance and phylogenetic analysis. Pennsylvania State University, University Park, PA

    Google Scholar 

  19. Minch E (1998) MICROSAT version 1.5b. University of Stanford, Stanford, CA

    Google Scholar 

  20. Beerli P, Felsenstein J (1999) Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152:763–773

    PubMed  CAS  Google Scholar 

  21. Goodman SJ (1997) Rst Calc: a collection of computer programs for calculating estimates of genetic differentiation from microsatellite data and a determining their significance. Mol Ecol 6:881–885

    Article  CAS  Google Scholar 

  22. Chapuis M-P, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621–631

    Article  PubMed  CAS  Google Scholar 

  23. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New York

    Google Scholar 

  24. Kim KS, Stolz U, Miller NJ, Waits ER, Guillemaud T, Sumerford DV, Sappington TW (2008) A core set of microsatellite markers for western corn rootworm (Coleoptera: Chrysomelidae) population genetics studies. Environ Entomol 37:293–300

    Article  PubMed  CAS  Google Scholar 

  25. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370

    Article  Google Scholar 

  26. Slatkin M (1985) Gene flow in natural populations. Annu Rev Ecol Syst 16:393–430

    Article  Google Scholar 

  27. Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159

    PubMed  CAS  Google Scholar 

  28. Beerli P, Felsenstein J (2001) Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc Natl Acad Sci USA 98:4563–4568

    Article  PubMed  CAS  Google Scholar 

  29. Slatkin M (1985) Rare alleles as indicators of gene flow. Evolution 39:53–65

    Article  Google Scholar 

  30. Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in a finite population. Proc Natl Acad Sci USA 75:2868–2872

    Article  PubMed  CAS  Google Scholar 

  31. Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB (1994) Mutational processes of simple-sequence repeat loci in human populations. Proc Natl Acad Sci USA 91:3166–3170

    Article  PubMed  Google Scholar 

  32. Estoup A, Wilson IJ, Sullivan C, Cornuet JM, Moritz C (2001) Inferring population history from microsatellite and enzyme data in serially introduced cane toads, Bufo marinus. Genetics 159:1671–1687

    PubMed  CAS  Google Scholar 

  33. Luikart G, Allendorf FW, Cornuet JM, Sherwin B (1998) Distortion of allele frequency distributions provides a test for recent population bottlenecks. J Hered 89:238–247

    Article  PubMed  CAS  Google Scholar 

  34. Garza JC, Williamson EG (2001) Detection of reduction of population size using data from microsatellite loci. Mol Ecol 10:305–318

    Article  PubMed  CAS  Google Scholar 

  35. Nei M, Tajima F, Tateno Y (1983) Accuracy of estimated phylogenetic trees from molecular data. J Mol Evol 19:153–170

    Article  PubMed  CAS  Google Scholar 

  36. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425

    PubMed  CAS  Google Scholar 

  37. Sneath PHA, Sokal RR (1973) Numerical taxonomy. W.H. Freedman and Co., San Francisco

    Google Scholar 

  38. Goudet J (1999) PCAGEN version 1.2. Population genetics laboratory, University of Lausanne, Lausanne, Switzerland

    Google Scholar 

  39. Felsenstein J (1993) PHYLIP-phylogenetic inference package, version 3.5c. University of Washington, Seattle, WA

    Google Scholar 

  40. Cornuet JM, Piry S, Luikart G, Estoup A, Solignac M (1999) New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153:1989–2000

    PubMed  CAS  Google Scholar 

  41. Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191

    PubMed  Google Scholar 

  42. Paetkau D, Slade R, Burdens M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation based exploration of accuracy and power. Mol Ecol 13:55–65

    Article  PubMed  CAS  Google Scholar 

  43. Wang J, Whitlock MC (2003) Estimating effective population size and migration rates from genetic samples over space and time. Genetics 163:429–446

    PubMed  CAS  Google Scholar 

  44. Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA 94:9197–9201

    Article  PubMed  CAS  Google Scholar 

  45. Paetkau D, Calvert W, Stirling I, Strobeck C (1995) Microsatellite analysis of population structure in Canadian polar bears. Mol Ecol 4:347–354

    Article  PubMed  CAS  Google Scholar 

  46. Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc 78:316–331

    Article  Google Scholar 

  47. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620

    Article  PubMed  CAS  Google Scholar 

  48. Slatkin M (1993) Isolation by distance in equilibrium and nonequilibrium populations. Evolution 47:264–279

    Article  Google Scholar 

  49. Wright S (1943) Isolation by distance. Genetics 28:114–138

    PubMed  CAS  Google Scholar 

  50. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219–1228

    PubMed  CAS  Google Scholar 

  51. de Sousa SN, Finkeldey R, Gailing O (2005) Experimental verification of microsatellite null alleles in Norway spruce (Picea abies [L.] Karst.): implications for population genetic studies. Plant Mol Biol Rep 23:113–119

    Article  Google Scholar 

  52. Girard P, Angers B (2008) Assessment of power and accuracy of methods for detection and frequency-estimation of null alleles. Genetica 134:187–197

    Article  PubMed  Google Scholar 

  53. Slatkin M (1995) Hitchhiking and associative overdominance at a microsatellite locus. Mol Biol Evol 12:473–480

    PubMed  CAS  Google Scholar 

  54. Paetkau D, Waits IP, Clarkson PL, Craighead I, Strobeck C (1997) An empirical evaluation of genetic distance statistics using microsatellite data from bear (Ursidae) populations. Genetics 147:1943–1957

    PubMed  CAS  Google Scholar 

  55. Pemberton JM, Slate J, Bancroft DR, Barrett JA (1995) Nonamplifying alleles at microsatellite loci: a caution for parentage and population studies. Mol Ecol 4:249–252

    Article  PubMed  CAS  Google Scholar 

  56. Rice WR (1989) Analysing tables of statistical tests. Evolution 43:223–225

    Article  Google Scholar 

  57. Benjamini Y, Yekutieli D (2001) The control of false discovery rate under dependency. Ann Stat 29:1165–1188

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (No. 2009-0080227). Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer.

Author information

Authors and Affiliations

  1. College of Veterinary Medicine, Seoul National University, Seoul, South Korea

    Kyung Seok Kim

  2. Genetics Laboratory, Corn Insects and Crop Genetics Research Unit, USDA-ARS, Iowa State University, Ames, IA, USA

    Thomas W. Sappington

Authors
  1. Kyung Seok Kim
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  2. Thomas W. Sappington
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Editor information

Editors and Affiliations

  1. , Department of Plant, Soil,, Southern Illinois University, Carbondale, Lincoln Drive 1205, Carbondale, 62901, Illinois, USA

    Stella K. Kantartzi

Appendix

Appendix

The figures in Appendix illustrate correctly formatted input files for most of the population genetics software programs described in this chapter. Instructions on formatting are provided on the programs’ respective websites (Table 1). Each input file contains the same genotype data for a total of ten individuals from four populations (two individuals for popA, three individuals for popB, two individuals for popC, three individuals for popD) at five microsatellite loci.

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Kim, K.S., Sappington, T.W. (2013). Microsatellite Data Analysis for Population Genetics. In: Kantartzi, S. (eds) Microsatellites. Methods in Molecular Biology, vol 1006. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-389-3_19

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  • DOI: https://doi.org/10.1007/978-1-62703-389-3_19

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  • Publisher Name: Humana Press, Totowa, NJ

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