Challenges in analysis and interpretation of microsatellite data for population genetic studies

Ecology and Evolution

Volumn 4, Issue 22, 2014, Pages 4399-4428

  Putman, Alexander I ^a   Carbone, Ignazio ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Data analysis; Microsatellite; Population genetics; Population structure

Indexed keywords

EID: 84911398926     PISSN: None     EISSN: 20457758     Source Type: Journal    
DOI: 10.1002/ece3.1305     Document Type: Review

References (324)

1. Aandahl, R. Z., J. F. Reyes, S. A. Sisson, and M. M. Tanaka. 2012. A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis. PLoS Comput. Biol. 8:e1002573.
2. Adamack, A. T., and B. Gruber. 2014. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5:384-387. doi: 10.1111/2041-1210X.12158.
3. Allendorf, F. W., P. A. Hohenlohe, and G. Luikart. 2010. Genomics and the future of conservation genetics. Nat. Rev. Genet. 11:697-709.
4. Amos, W. 2010. Heterozygosity and mutation rate: evidence for an interaction and its implications. BioEssays 32:82-90.
5. Amos, W. 2011. Population-specific links between heterozygosity and the rate human microsatellite evolution. J. Mol. Evol. 72:215-221.
6. Amos, W. 2013. Variation in heterozygosity predicts variation in human substitution rates between populations, individuals and genomic regions. PLoS ONE 8:e63048.
7. Amos, W., S. J. Sawcer, R. W. Feakes, and D. C. Rubinsztein. 1996. Microsatellites show mutational bias and heterozygote instability. Nat. Genet. 13:390-391.
8. Amos, W., J. Flint, and X. Xu. 2008. Heterozygosity increases microsatellite mutation rate, linking it to demographic history. BMC Genet. 9:72.
9. Anderson, E. C., and K. K. Dunham. 2008. The influence of family groups on inferences made with the program Structure. Mol. Ecol. Resour. 8:1219-1229.
10. Anderson, E. C., and E. A. Thompson. 2002. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160:1217-1229.
11. Andrew, R. L., K. L. Ostevik, D. P. Ebert, and L. H. Rieseberg. 2012. Adaptation with gene flow across the landscape in a dune sunflower. Mol. Ecol. 21:2078-2091.
12. Andrews, K. R., and G. Luikart. 2014. Recent novel approaches for population genomics data analysis. Mol. Ecol. 23:1661-1667.
13. Anmarkrud, J. A., O. Kleven, L. Bachmann, and J. T. Lifjeld. 2008. Microsatellite evolution: mutations, sequence variation, and homoplasy in the hypervariable avian microsatellite locus HrU10. BMC Evol. Biol. 8:138.
14. Anmarkrud, J. A., O. Kleven, J. Augustin, K. H. Bentz, D. Blomqvist, K. J. Fernie, et al. 2011. Factors affecting germline mutations in a hypervariable microsatellite: a comparative analysis of six species of swallows (Aves: Hirundinidae). Mutat. Res. 708:37-43.
15. Antao, T., A. Pérez-Figueroa, and G. Luikart. 2011. Early detection of population declines: high power of genetic monitoring using effective population size estimators. Evol. Appl. 4:144-154.
16. Arthofer, W., F. M. Steiner, and B. C. Schlick-Steiner. 2011. Rapid and cost-effective screening of newly identified microsatellite loci by high-resolution melting analysis. Mol. Genet. Genomics 286:225-235.
17. Bachtrog, D., M. Agis, M. Imhof, and C. Schlötterer. 2000. Microsatellite variability differs between dinucleotide repeat motifs-evidence from Drosophila melanogaster. Mol. Biol. Evol. 17:1277-1285.
18. Balloux, F., and J. Goudet. 2002. Statistical properties of population differentiation estimators under stepwise mutation in a finite island model. Mol. Ecol. 11:771-783.
19. Balloux, F., and N. Lugon-Moulin. 2002. The estimation of population differentiation with microsatellite markers. Mol. Ecol. 11:155-165.
20. Balloux, F., H. Brünner, N. Lugon-Moulin, J. Hausser, and J. Goudet. 2000. Microsatellites can be misleading: an empirical and simulation study. Evolution 54:1414-1422.
21. Baran, Y., I. Quintela, A. Carracedo, B. Pasaniuc, and E. Halperin. 2013. Enhanced localization of genetic samples through linkage-disequilibrium correction. Am. J. Hum. Genet. 92:882-894.
22. Barker, J. S. F. 2011. Effective population size of natural populations of Drosophila buzzatii, with a comparative evaluation of nine methods of estimation. Mol. Ecol. 20:4452-4471.
23. Barkley, N. A., R. R. Krueger, C. T. Federici, and M. L. Roose. 2009. What phylogeny and gene genealogy analyses reveal about homoplasy in citrus microsatellite alleles. Plant Syst. Evol. 282:71-86.
24. Barthe, S., F. Gugerli, N. A. Barkley, L. Maggia, C. Cardi, and I. Scotti. 2012. Always look on both sides: phylogenetic information conveyed by simple sequence repeat allele sequences. PLoS ONE 7:e40699.
25. Beaumont, M. A. 1999. Detecting population expansion and decline using microsatellites. Genetics 153:2013-2029.
26. Beaumont, M. A. 2010. Approximate Bayesian computation in evolution and ecology. Annu. Rev. Ecol. Evol. Syst. 41:379-406.
27. Beaumont, M. A., and R. A. Nichols. 1996. Evaluating loci for use in the genetic analysis of population structure. Proc. R. Soc. B Biol. Sci. 263:1619-1626.
28. Beaumont, M. A., W. Zhang, and D. J. Balding. 2002. Approximate Bayesian computation in population genetics. Genetics 162:2025-2035.
29. Beerli, P. 2004. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13:827-836.
30. Beerli, P. 2006. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22:341-345.
31. Beerli, P. 2007. Estimation of the population scaled mutation rate from microsatellite data. Genetics 177:1967-1968.
32. Beerli, P., and J. Felsenstein. 1999. Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152:763-773.
33. Besnier, F., and K. A. Glover. 2013. ParallelStructure: a R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8:e70651.
34. Bhargava, A., and F. F. Fuentes. 2010. Mutational dynamics of microsatellites. Mol. Biotechnol. 44:250-266.
35. Bird, S. C. 2012. Towards improvements in the estimation of the coalescent: implications for the most effective use of Y chromosome short tandem repeat mutation rates. PLoS ONE 7:e48638.
36. Björklund, M., S. Bergek, E. Ranta, and V. Kaitala. 2010. The effect of local population dynamics on patterns of isolation by distance. Ecol. Inform. 5:167-172.
37. Blair, C., D. E. Weigel, M. Balazik, A. T. H. Keeley, F. M. Walker, E. Landguth, et al. 2012. A simulation-based evaluation of methods for inferring linear barriers to gene flow. Mol. Ecol. Resour. 12:822-833.
38. Blum, M. J., M. J. Bagley, D. M. Walters, S. A. Jackson, F. B. Daniel, D. J. Chaloud, et al. 2012. Genetic diversity and species diversity of stream fishes covary across a land-use gradient. Oecologia 168:83-95.
39. Bohling, J. H., J. R. Adams, and L. P. Waits. 2013. Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical red wolf data set. Mol. Ecol. 22:74-86.
40. Bornman, D. M., M. E. Hester, J. M. Schuetter, M. D. Kasoji, A. Minard-Smith, C. A. Barden, et al. 2012. Short-read, high-throughput sequencing technology for STR genotyping. Biotechniques Rapid Disp p.1-6. doi: 10.2144/000113857.
41. Broquet, T., and E. J. Petit. 2009. Molecular estimation of dispersal for ecology and population genetics. Annu. Rev. Ecol. Evol. Syst. 40:193-216.
42. Broquet, T., J. Yearsley, A. H. Hirzel, J. Goudet, and N. Perrin. 2009. Inferring recent migration rates from individual genotypes. Mol. Ecol. 18:1048-1060.
43. Broquet, T., S. Angelone, J. Jaquiery, P. Joly, J.-P. Lena, T. Lengagne, et al. 2010. Genetic bottlenecks driven by population disconnection. Conserv. Biol. 24:1596-1605.
44. Brouwer, J. R., R. Willemsen, and B. A. Oostra. 2009. Microsatellite repeat instability and neurological disease. BioEssays 31:71-83.
45. Buonaccorsi, V. P., S. R. Narum, K. A. Karkoska, S. Gregory, T. Deptola, and A. B. Weimer. 2011. Characterization of a genomic divergence island between black-and-yellow and gopher Sebastes rockfishes. Mol. Ecol. 20:2603-2618.
46. Buschiazzo, E., and N. J. Gemmell. 2006. The rise, fall and renaissance of microsatellites in eukaryotic genomes. BioEssays 28:1040-1050.
47. Buschiazzo, E., and N. J. Gemmell. 2010. Conservation of human microsatellites across 450 million years of evolution. Genome Biol. Evol. 2:153-165.
48. Calabrese, P. P., R. T. Durrett, and C. F. Aquadro. 2001. Dynamics of microsatellite divergence under stepwise mutation and proportional slippage/point mutation models. Genetics 159:839-852.
49. Campagne, P., P. E. Smouse, G. Varouchas, J.-F. Silvain, and B. Leru. 2012. Comparing the van Oosterhout and Chybicki-Burczyk methods of estimating null allele frequencies for inbred populations. Mol. Ecol. Resour. 12:975-982.
50. Cao, M. D., E. Tasker, K. Willadsen, M. Imelfort, S. Vishwanathan, S. Sureshkumar, et al. 2014. Inferring short tandem repeat variation from paired-end short reads. Nucleic Acids Res. 42:e16.
51. Carlsson, J. 2008. Effects of microsatellite null alleles on assignment testing. J. Hered. 99:616-623.
52. Carreras-Carbonell, J., E. Macpherson, and M. Pascual. 2006. Population structure within and between subspecies of the Mediterranean triplefin fish Tripterygion delaisi revealed by highly polymorphic microsatellite loci. Mol. Ecol. 15:3527-3539.
53. Chakraborty, R., and M. Nei. 1982. Genetic differentiation of quantitative characters between populations or species I. Mutation and randome genetic drift. Genet. Res. 39:303-314.
54. Chapuis, M.-P., and A. Estoup. 2007. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24:621-631.
55. Chapuis, M.-P., M. Lecoq, Y. Michalakis, A. Loiseau, G. A. Sword, S. Piry, et al. 2008. Do outbreaks affect genetic population structure? A worldwide survey in Locusta migratoria, a pest plagued by microsatellite null alleles. Mol. Ecol. 17:3640-3653.
56. Chapuis, M.-P., R. Streiff, and G. A. Sword. 2012. Long microsatellites and unusually high levels of genetic diversity in the Orthoptera. Insect Mol. Biol. 21:181-186.
57. Charlesworth, B. 1998. Measures of divergence between populations and the effect of forces that reduce variability. Mol. Biol. Evol. 15:538-543.
58. Charlesworth, B. 2009. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10:195-205.
59. Charpentier, M. J. E., M. C. Fontaine, E. Cherel, J. P. Renoult, T. Jenkins, L. Benoit, et al. 2012. Genetic structure in a dynamic baboon hybrid zone corroborates behavioural observations in a hybrid population. Mol. Ecol. 21:715-731.
60. Chen, C., E. Durand, F. Forbes, and O. François. 2007. Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study. Mol. Ecol. Notes 7:747-756.
61. Chhatre, V. 2012. StrAuto ver0.3.1: A Python utility to automate Structure analysis. Available at http://www.crypticlineage.net/pages/software.html.
62. Chikhi, L., V. C. Sousa, P. Luisi, B. Goossens, and M. A. Beaumont. 2010. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186:983-995.
63. Chistiakov, D., B. Hellemans, and F. Volckaert. 2006. Microsatellites and their genomic distribution, evolution, function and applications: a review with special reference to fish genetics. Aquaculture 255:1-29.
64. Choi, S. C., and J. Hey. 2011. Joint inference of population assignment and demographic history. Genetics 189:561-577.
65. Churbanov, A., R. Ryan, N. Hasan, D. Bailey, H. Chen, B. Milligan, et al. 2012. HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data. Bioinformatics 28:2797-2803.
66. Ciani, E., F. Cecchi, E. Castellana, M. D'Andrea, C. Incoronato, F. D'Angelo, et al. 2013. Poorer resolution of low-density SNP vs. STR markers in reconstructing genetic relationships among seven Italian sheep breeds. Large Anim. Rev. 19:236-241.
67. Coates, B. S., D. V. Sumerford, N. J. Miller, K. S. Kim, T. W. Sappington, B. D. Siegfried, et al. 2009. Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J. Hered. 100:556-564.
68. Colonna, V., T. Nutile, R. R. Ferrucci, G. Fardella, M. Aversano, G. Barbujani, et al. 2009. Comparing population structure as inferred from genealogical versus genetic information. Eur. J. Hum. Genet. 17:1635-1641.
69. Colson, I., and D. B. Goldstein. 1999. Evidence for complex mutations at microsatellite loci in Drosophila. Genetics 152:617-627.
70. Coombs, J. A., B. H. Letcher, and K. H. Nislow. 2012. GONe: software for estimating effective population size in species with generational overlap. Mol. Ecol. Resour. 12:160-163.
71. Corander, J., and J. Tang. 2007. Bayesian analysis of population structure based on linked molecular information. Math. Biosci. 205:19-31.
72. Corander, J., P. Waldmann, and M. J. Sillanpää. 2003. Bayesian analysis of genetic differentiation between populations. Genetics 163:367-374.
73. Corander, J., P. Waldmann, P. Marttinen, and M. J. Sillanpää. 2004. BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics 20:2363-2369.
74. Corander, J., P. Marttinen, and S. Mäntyniemi. 2006. A Bayesian method for identification of stock mixtures from molecular marker data. Fish. Bull. 104:550-558.
75. Cornuet, J. M., and G. Luikart. 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001-2014.
76. Cornuet, J. M., M. A. Beaumont, A. Estoup, and M. Solignac. 2006. Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo. Theor. Popul. Biol. 69:129-144.
77. Cornuet, J.-M., V. Ravigné, and A. Estoup. 2010. Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinformatics 11:401.
78. de Crespin de Billy, V., S. Doledec, and D. Chessel. 2000. Biplot presentation of diet composition data: an alternative for fish stomach contents analysis. J. Fish Biol. 56:961-973.
79. Crow, J. F., and M. Kimura. 1970. An introduction to population genetics theory. Harper and Row, New York.
80. Csilléry, K., O. François, and M. G. B. Blum. 2012. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3:475-479.
81. Dabrowski, M. J., M. Pilot, M. Kruczyk, M. Zmihorski, H. M. Umer, and J. Gliwicz. 2014. Reliability assessment of null allele detection: inconsistencies between and within different methods. Mol. Ecol. Resour. 14:361-373.
82. Dawson, D. A., A. D. Ball, L. G. Spurgin, D. Martín-Gálvez, I. R. K. Stewart, G. J. Horsburgh, et al. 2013. High-utility conserved avian microsatellite markers enable parentage and population studies across a wide range of species. BMC Genom. 14:176.
83. De Mita, S., and M. Siol. 2012. EggLib: processing, analysis and simulation tools for population genetics and genomics. BMC Genet. 13:27.
84. Defaveri, J., H. Viitaniemi, E. Leder, and J. Merilä. 2013. Characterizing genic and nongenic molecular markers: comparison of microsatellites and SNPs. Mol. Ecol. Resour. 13:377-392.
85. Dharmarajan, G., W. S. Beatty, and O. E. Rhodes. 2013. Heterozygote deficiencies caused by a Wahlund effect: dispelling unfounded expectations. J. Wildl. Manag. 77:226-234.
86. Di Rienzo, A., A. C. Peterson, J. C. Garza, A. M. Valdes, M. Slatkin, and N. B. Freimer. 1994. Mutational processes of simple-sequence repeat loci in human populations. Proc. Natl Acad. Sci. 91:3166-3170.
87. Do, C., R. S. Waples, D. Peel, G. M. Macbeth, B. J. Tillett, and J. R. Ovenden. 2014. NeEstimator v2.0: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14:209-214.
88. Duchesne, P., and J. Turgeon. 2012. FLOCK provides reliable solutions to the "number of populations" problem. J. Hered. 103:734-743.
89. Dufresne, F., M. Stift, R. Vergilino, and B. K. Mable. 2014. Recent progress and challenges in population genetics of polyploid organisms: an overview of current state-of-the-art molecular and statistical tools. Mol. Ecol. 23:40-69.
90. Duran, C., R. Singhania, H. Raman, J. Batley, and D. Edwards. 2013. Predicting polymorphic EST-SSRs in silico. Mol. Ecol. Resour. 13:538-545.
91. Epperson, B. K., B. H. McRae, K. Scribner, S. A. Cushman, M. S. Rosenberg, M.-J. Fortin, et al. 2010. Utility of computer simulations in landscape genetics. Mol. Ecol. 19:3549-3564.
92. Eriksson, A., and A. Manica. 2011. Detecting and removing ascertainment bias in microsatellites from the HGDP-CEPH Panel. Genes Genom. Genet. 1:479-488.
93. Eschbach, E., and S. Schöning. 2013. Identification of high-resolution microsatellites without a priori knowledge of genotypes using a simple scoring approach. Methods Ecol. Evol. 4:1076-1082.
94. Estoup, A., P. Jarne, and J.-M. Cornuet. 2002. Homoplasy and mutation model at microsatellite loci and their consequences for population genetic analysis. Mol. Ecol. 11:1591-1604.
95. Excoffier, L., and G. Heckel. 2006. Computer programs for population genetics data analysis: a survival guide. Nat. Rev. Genet. 7:745-758.
96. Excoffier, L., P. E. Smouse, and J. M. Quattro. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitrochondrial DNA restriction data. Genetics 131:479-491.
97. Falush, D., M. Stephens, and J. K. Pritchard. 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567-1587.
98. Falush, D., M. Stephens, and J. K. Pritchard. 2007. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes 7:574-578.
99. Faubet, P., and O. E. Gaggiotti. 2008. A new Bayesian method to identify the environmental factors that influence recent migration. Genetics 178:1491-1504.
100. Faubet, P., R. S. Waples, and O. E. Gaggiotti. 2007. Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol. Ecol. 16:1149-1166.
101. Faurby, S., and C. Pertoldi. 2012. The consequences of the unlikely but critical assumption of stepwise mutation in the population genetic software, MSVAR. Evol. Ecol. Res. 14:859-879.
102. Felsenstein, J. 2004. Inferring phylogenies. Sinauer Associates, Sunderland, MA.
103. Fernandez-Silva, I., J. Whitney, B. Wainwright, K. R. Andrews, H. Ylitalo-Ward, B. W. Bowen, et al. 2013. Microsatellites for next-generation ecologists: a post-sequencing bioinformatics pipeline. PLoS ONE 8:e55990.
104. Ferretti, L., S. E. Ramos-Onsins, and M. Pérez-Enciso. 2013. Population genomics from pool sequencing. Mol. Ecol. 22:5561-5576.
105. Fitzpatrick, B. M. 2012. Estimating ancestry and heterozygosity of hybrids using molecular markers. BMC Evol. Biol. 12:131.
106. Fogelqvist, J., A. Niittyvuopio, J. Ågren, O. Savolainen, and M. Lascoux. 2010. Cryptic population genetic structure: the number of inferred clusters depends on sample size. Mol. Ecol. Resour. 10:314-323.
107. Forstmeier, W., H. Schielzeth, J. C. Mueller, H. Ellegren, and B. Kempenaers. 2012. Heterozygosity-fitness correlations in zebra finches: microsatellite markers can be better than their reputation. Mol. Ecol. 21:3237-3249.
108. Frantz, A. C., S. Cellina, A. Krier, L. Schley, and T. Burke. 2009. Using spatial Bayesian methods to determine the genetic structure of a continuously distributed population: clusters or isolation by distance? J. Appl. Ecol. 46:493-505.
109. Fraser, D. J., M. M. Hansen, S. Østergaard, N. Tessier, M. Legault, and L. Bernatchez. 2007. Comparative estimation of effective population sizes and temporal gene flow in two contrasting population systems. Mol. Ecol. 16:3866-3889.
110. Fu, R., D. K. Dey, and K. E. Holsinger. 2005. Bayesian models for the analysis of genetic structure when populations are correlated. Bioinformatics 21:1516-1529.
111. Fung, T., and K. Keenan. 2014. Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size. PLoS ONE 9:e85925.
112. Gaggiotti, O. E., and M. Foll. 2010. Quantifying population structure using the F-model. Mol. Ecol. Resour. 10:821-830.
113. Gaggiotti, O. E., O. Lange, K. Rassmann, and C. Gliddon. 1999. A comparison of two indirect methods for estimating average levels of gene flow using microsatellite data. Mol. Ecol. 8:1513-1520.
114. Gao, H., S. Williamson, and C. D. Bustamante. 2007. A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data. Genetics 176:1635-1651.
115. Gardner, M. G., A. J. Fitch, T. Bertozzi, and A. J. Lowe. 2011. Rise of the machines - recommendations for ecologists when using next generation sequencing for microsatellite development. Mol. Ecol. Resour. 11:1093-1101.
116. Gärke, C., F. Ytournel, B. Bed'hom, I. Gut, M. Lathrop, S. Weigend, et al. 2012. Comparison of SNPs and microsatellites for assessing the genetic structure of chicken populations. Anim. Genet. 43:419-428.
117. Garrick, R. C., A. Caccone, and P. Sunnucks. 2010. Inference of population history by coupling exploratory and model-driven phylogeographic analyses. Int. J. Mol. Sci. 11:1190-1227.
118. Garza, J. C., and E. G. Williamson. 2001. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10:305-318.
119. Gayevskiy, V., S. Klaere, S. Knight, and M. R. Goddard. 2014. ObStruct: a method to objectively analyse factors driving population structure using bayesian ancestry profiles. PLoS ONE 9:e85196.
120. Gerlach, G., A. Jueterbock, P. Kraemer, J. Deppermann, and P. Harmand. 2010. Calculations of population differentiation based on G(ST) and D: forget G(ST) but not all of statistics!. Mol. Ecol. 19:3845-3852.
121. Gilbert, K. J., R. L. Andrew, D. G. Bock, M. T. Franklin, N. C. Kane, J.-S. Moore, et al. 2012. Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program STRUCTURE. Mol. Ecol. 21:4925-4930.
122. Girod, C., R. Vitalis, R. Leblois, and H. Fréville. 2011. Inferring population decline and expansion from microsatellite data: a simulation-based evaluation of the Msvar method. Genetics 188:165-179.
123. Glenn, T. C., and N. A. Schable. 2005. Isolating microsatellite DNA loci. Methods Enzymol. 395:202-222.
124. Glover, K. A., M. M. Hansen, S. Lien, T. D. Als, B. Høyheim, and O. Skaala. 2010. A comparison of SNP and STR loci for delineating population structure and performing individual genetic assignment. BMC Genet. 11:2.
125. Goldstein, D. B., and D. D. Pollock. 1997. Launching microsatellites: a review of mutation processes and methods of phylogenetic inference. J. Hered. 88:335-342.
126. Goldstein, D. B., A. R. Linares, L. L. Cavalli-Sforzaf, and M. W. Feldman. 1995. An evaluation of genetic distances for use with microsatellite loci. Genetics 139:463-471.
127. Gompert, Z., and C. A. Buerkle. 2013. Analyses of genetic ancestry enable key insights for molecular ecology. Mol. Ecol. 22:5278-5294.
128. Granevitze, Z., L. David, T. Twito, S. Weigend, M. Feldman, and J. Hillel. 2014. Phylogenetic resolution power of microsatellites and various single-nucleotide polymorphism types assessed in 10 divergent chicken populations. Anim. Genet. 45:87-95.
129. Grover, A., and P. C. Sharma. 2011. Is spatial occurrence of microsatellites in the genome a determinant of their function and dynamics contributing to genome evolution? Curr. Sci. 100:859-869.
130. Grover, C. E., A. Salmon, and J. F. Wendel. 2012. Targeted sequence capture as a powerful tool for evolutionary analysis. Am. J. Bot. 99:312-319.
131. Guichoux, E., L. Lagache, S. Wagner, P. Chaumeil, P. Léger, O. Lepais, et al. 2011. Current trends in microsatellite genotyping. Mol. Ecol. Resour. 11:591-611.
132. Guillot, G. 2008. Inference of structure in subdivided populations at low levels of genetic differentiation-the correlated allele frequencies model revisited. Bioinformatics 24:2222-2228.
133. Guillot, G., and M. Foll. 2009. Correcting for ascertainment bias in the inference of population structure. Bioinformatics 25:552-554.
134. Haasl, R. J., and B. A. Payseur. 2010. The number of alleles at a microsatellite defines the allele frequency spectrum and facilitates fast accurate estimation of theta. Mol. Biol. Evol. 27:2702-2715.
135. Haasl, R. J., and B. A. Payseur. 2011. Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity 106:158-171.
136. Haasl, R. J., and B. A. Payseur. 2013. Microsatellites as targets of natural selection. Mol. Biol. Evol. 30:285-298.
137. Hale, M. L., T. M. Burg, and T. E. Steeves. 2012. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7:e45170.
138. Hardy, O. J., and X. Vekemans. 2002. spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2:618-620.
139. Hardy, O. J., N. Charbonnel, H. Fréville, and M. Heuertz. 2003. Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163:1467-1482.
140. Hauser, L., M. Baird, R. Hilborn, L. W. Seeb, and J. E. Seeb. 2011. An empirical comparison of SNPs and microsatellites for parentage and kinship assignment in a wild sockeye salmon (Oncorhynchus nerka) population. Mol. Ecol. Resour. 11(Suppl. 1):150-161.
141. Hedrick, P. W. 1999. Perspective: highly variable loci and their interpretation in evolution and conservation. Evolution 53:313-318.
142. Hedrick, P. W. 2005. A standardized genetic differentiation measure. Evolution 59:1633-1638.
143. Heller, R., and H. R. Siegismund. 2009. Relationship between three measures of genetic differentiation G(ST), D(EST) and G'(ST): how wrong have we been? Mol. Ecol. 18:2080-2083.
144. Helyar, S. J., J. Hemmer-Hansen, D. Bekkevold, M. I. Taylor, R. Ogden, M. T. Limborg, et al. 2011. Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol. Ecol. Resour. 11(Suppl. 1):123-136.
145. Herráeza, D. L., H. Schäfer, J. Mosner, H.-R. Fries, and M. Wink. 2005. Comparison of microsatellite and single nucleotide polymorphism markers for the genetic analysis of a Galloway cattle population. J. Biosci. 60:637-643.
146. Hess, J. E., A. P. Matala, and S. R. Narum. 2011. Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Mol. Ecol. Resour. 11(Suppl. 1):137-149.
147. Hess, M. A., J. G. Rhydderch, L. L. Leclair, R. M. Buckley, M. Kawase, and L. Hauser. 2012. Estimation of genotyping error rate from repeat genotyping, unintentional recaptures and known parent-offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus). Mol. Ecol. Resour. 12:1114-1123.
148. Hey, J. 2011. Documentation for IMa2 department of genetics. Rutgers University, New Brunswick, NJ.
149. Hey, J., and R. Nielsen. 2004. Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167:747-760.
150. Hey, J., Y.-J. Won, A. Sivasundar, R. Nielsen, and J. A. Markert. 2004. Using nuclear haplotypes with microsatellites to study gene flow between recently separated Cichlid species. Mol. Ecol. 13:909-919.
151. Highnam, G., C. Franck, A. Martin, C. Stephens, A. Puthige, and D. Mittelman. 2013. Accurate human microsatellite genotypes from high-throughput resequencing data using informed error profiles. Nucleic Acids Res. 41:e32.
152. Hoban, S., G. Bertorelle, and O. E. Gaggiotti. 2012. Computer simulations: tools for population and evolutionary genetics. Nat. Rev. Genet. 13:110-122.
153. Hoban, S., O. Gaggiotti, and G. Bertorelle. 2013a. Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples. Methods Ecol. Evol. 4:299-303.
154. Hoban, S. M., O. E. Gaggiotti, and G. Bertorelle. 2013b. The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: a simulation-based study. Mol. Ecol. 22:3444-3450.
155. Hoban, S. M., M. Mezzavilla, O. E. Gaggiotti, A. Benazzo, C. van Oosterhout, and G. Bertorelle. 2013c. High variance in reproductive success generates a false signature of a genetic bottleneck in populations of constant size: a simulation study. BMC Bioinformatics 14:309.
156. Hoehn, M., B. Gruber, S. D. Sarre, R. Lange, and K. Henle. 2012. Can genetic estimators provide robust estimates of the effective number of breeders in small populations? PLoS ONE 7:e48464.
157. Holsinger, K. E., and B. S. Weir. 2009. Genetics in geographically structured populations: defining, estimating and interpreting F(ST). Nat. Rev. Genet. 10:639-650.
158. Hubisz, M. J., D. Falush, M. Stephens, and J. K. Pritchard. 2009. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9:1322-1332.
159. Intarapanich, A., P. J. Shaw, A. Assawamakin, P. Wangkumhang, C. Ngamphiw, K. Chaichoompu, et al. 2009. Iterative pruning PCA improves resolution of highly structured populations. BMC Bioinformatics 10:382.
160. Jakobsson, M., M. D. Edge, and N. A. Rosenberg. 2013. The relationship between F(ST) and the frequency of the most frequent allele. Genetics 193:515-528.
161. Jennings, T. N., B. J. Knaus, T. D. Mullins, S. M. Haig, and R. C. Cronn. 2011. Multiplexed microsatellite recovery using massively parallel sequencing. Mol. Ecol. Resour. 11:1060-1067.
162. Jochens, A., A. Caliebe, U. Rösler, and M. Krawczak. 2011. Empirical evaluation reveals best fit of a logistic mutation model for human Y-chromosomal microsatellites. Genetics 189:1403-1411.
163. Jombart, T. 2008. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403-1405.
164. Jombart, T., S. Devillard, A.-B. Dufour, and D. Pontier. 2008. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92-103.
165. Jombart, T., D. Pontier, and A.-B. Dufour. 2009. Genetic markers in the playground of multivariate analysis. Heredity 102:330-341.
166. Jombart, T., S. Devillard, and F. Balloux. 2010. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11:94.
167. Jorde, P. E. 2012. Allele frequency covariance among cohorts and its use in estimating effective size of age-structured populations. Mol. Ecol. Resour. 12:476-480.
168. Jost, L. 2008. G(ST) and its relatives do not measure differentiation. Mol. Ecol. 17:4015-4026.
169. Kaeuffer, R., D. Réale, D. W. Coltman, and D. Pontier. 2007. Detecting population structure using STRUCTURE software: effect of background linkage disequilibrium. Heredity 99:374-380.
170. Kalinowski, S. T. 2005. Do polymorphic loci require large sample sizes to estimate genetic distances? Heredity 94:33-36.
171. Kalinowski, S. T. 2009. How well do evolutionary trees describe genetic relationships among populations? Heredity 102:506-513.
172. Kalinowski, S. T. 2011. The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure. Heredity 106:625-632.
173. Kane, N. C., and M. G. King. 2009. Using parentage analysis to examine gene flow and spatial genetic structure. Mol. Ecol. 18:1551-1552.
174. Karl, S. A., R. J. Toonen, W. S. Grant, and B. W. Bowen. 2012. Common misconceptions in molecular ecology: echoes of the modern synthesis. Mol. Ecol. 21:4171-4189.
175. Kelkar, Y. D., S. Tyekucheva, F. Chiaromonte, and K. D. Makova. 2008. The genome-wide determinants of human and chimpanzee microsatellite evolution. Genome Res. 18:30-38.
176. Kelkar, Y. D., N. Strubczewski, S. E. Hile, F. Chiaromonte, K. A. Eckert, and K. D. Makova. 2010. What is a microsatellite: a computational and experimental definition based upon repeat mutational behavior at A/T and GT/AC repeats. Genome Biol. Evol. 2:620-635.
177. Kelkar, Y. D., K. A. Eckert, F. Chiaromonte, and K. D. Makova. 2011. A matter of life or death: how microsatellites emerge in and vanish from the human genome. Genome Res. 21:2038-2048.
178. Kelly, A. C., N. E. Mateus-Pinilla, M. Douglas, P. Shelton, and J. Novakofski. 2011. Microsatellites behaving badly: empirical evaluation of genotyping errors and subsequent impacts on population studies. Genet. Mol. Res. 10:2534-2553.
179. Kim, K. S., and T. W. Sappington. 2013. Microsatellite data analysis for population genetics. Methods Mol. Biol. 1006:271-295.
180. Kimura, M., and J. F. Crow. 1964. The number of alleles that can be maintained in a finite population. Genetics 49:725-738.
181. Kondo, T., M. D. Crisp, C. Linde, D. M. J. S. Bowman, K. Kawamura, S. Kaneko, et al. 2012. Not an ancient relic: the endemic Livistona palms of arid central Australia could have been introduced by humans. Proc. R. Soc. B Biol. Sci. 279:2652-2661.
182. Kronholm, I., O. Loudet, and J. de Meaux. 2010. Influence of mutation rate on estimators of genetic differentiation-lessons from Arabidopsis thaliana. BMC Genet. 11:33.
183. Kuhner, M. K. 2006. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22:768-770.
184. Kyrkjeeide, M. O., K. Hassel, K. I. Flatberg, and H. K. Stenøien. 2012. The rare peat moss Sphagnum wulfianum (Sphagnaceae) did not survive the last glacial period in northern European refugia. Am. J. Bot. 99:677-689.
185. Lai, Y., and F. Sun. 2003. The relationship between microsatellite slippage mutation rate and the number of repeat units. Mol. Biol. Evol. 20:2123-2131.
186. Laloë, D., T. Jombart, A.-B. Dufour, and K. Moazami-Gouarzi. 2007. Consensus genetic structuring and typological value of markers using multiple co-inertia analysis. Genet. Sel. Evol. 39:545-567.
187. Latch, E. K., G. Dharmarajan, J. C. Glaubitz, and O. E. Rhodes. 2006. Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv. Genet. 7:295-302.
188. Lawson, D. J., G. Hellenthal, S. Myers, and D. Falush. 2012. Inference of population structure using dense haplotype data. PLoS Genet. 8:11-17.
189. Leberg, P. 2005. Genetic approaches for estimating the effective size of populations. J. Wildl. Manag. 69:1385-1399.
190. Leblois, R., A. Estoup, and R. Streiff. 2006. Genetics of recent habitat contraction and reduction in population size: does isolation by distance matter? Mol. Ecol. 15:3601-3615.
191. Leclercq, S., E. Rivals, and P. Jarne. 2010. DNA slippage occurs at microsatellite loci without minimal threshold length in humans: a comparative genomic approach. Genome Biol. Evol. 2:325-335.
192. Leng, L., and D.-X. Zhang. 2011. Measuring population differentiation using G(ST) or D? A simulation study with microsatellite DNA markers under a finite island model and nonequilibrium conditions. Mol. Ecol. 20:2494-2509.
193. Leng, L., and D.-X. Zhang. 2013. Time matters: some interesting properties of the population differentiation measures G(ST) and D overlooked in the equilibrium perspective. J. Syst. Evol. 51:44-60.
194. Li, B., and M. Kimmel. 2013. Factors influencing ascertainment bias of microsatellite allele sizes: impact on estimates of mutation rates. Genetics 195:563-572.
195. Lia, V. V., M. Bracco, A. M. Gottlieb, L. Poggio, and V. A. Confalonieri. 2007. Complex mutational patterns and size homoplasy at maize microsatellite loci. Theor. Appl. Genet. 115:981-991.
196. Limborg, M. T., R. Hanel, P. V. Debes, A. K. Ring, C. André, C. S. Tsigenopoulos, et al. 2012. Imprints from genetic drift and mutation imply relative divergence times across marine transition zones in a pan-European small pelagic fish (Sprattus sprattus). Heredity 109:96-107.
197. Limpiti, T., A. Intarapanich, A. Assawamakin, P. Wangkumhang, and S. Tongsima. 2011. Iterative PCA for population structure analysis. 597-600.
198. Liu, N., and H. Zhao. 2006. A non-parametric approach to population structure inference using multilocus genotypes. Hum. Genome 2:353-364.
199. Livingstone, D. S., J. C. Motamayor, R. J. Schnell, K. Cariaga, B. Freeman, A. W. Meerow, et al. 2010. Development of single nucleotide polymorphism markers in Theobroma cacao and comparison to simple sequence repeat markers for genotyping of Cameroon clones. Mol. Breeding 27:93-106.
200. Ljungqvist, M., M. Akesson, B. Hansson. 2010. Do microsatellites reflect genome-wide genetic diversity in natural populations? A comment on Väli, et al. (2008). Mol. Ecol. 19:851-855.
201. Lopes, J. S., D. Balding, and M. A. Beaumont. 2009. PopABC: a program to infer historical demographic parameters. Bioinformatics 25:2747-2749.
202. Lowe, W. H., and F. W. Allendorf. 2010. What can genetics tell us about population connectivity? Mol. Ecol. 19:3038-3051.
203. Luikart, G., N. Ryman, D. A. Tallmon, M. K. Schwartz, and F. W. Allendorf. 2010. Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv. Genet. 11:355-373.
204. Manel, S., O. E. Gaggiotti, and R. S. Waples. 2005. Assignment methods: matching biological questions with appropriate techniques. Trends Ecol. Evol. 20:136-142.
205. Mardulyn, P., M.-A. Vaesen, and M. C. Milinkovitch. 2008. Controlling population evolution in the laboratory to evaluate methods of historical inference. PLoS ONE 3:e2960.
206. Marjoram, P., and S. Tavaré. 2006. Modern computational approaches for analysing molecular genetic variation data. Nat. Rev. Genet. 7:759-770.
207. Marko, P. B., and M. W. Hart. 2011. Retrospective coalescent methods and the reconstruction of metapopulation histories in the sea. Evol. Ecol. 26:291-315.
208. Masters, B. S., L. S. Johnson, B. G. P. Johnson, J. L. Brubaker, S. K. Sakaluk, and C. F. Thompson. 2011. Evidence for heterozygote instability in microsatellite loci in house wrens. Biol. Lett. 7:127-130.
209. McCormack, J. E., S. M. Hird, A. J. Zellmer, B. C. Carstens, and R. T. Brumfield. 2013. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol. Phylogenet. Evol. 66:526-538.
210. Meirmans, P. G. 2006. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60:2399-2402.
211. Meirmans, P. G. 2012. The trouble with isolation by distance. Mol. Ecol. 21:2839-2846.
212. Meirmans, P. G. 2014. Non-convergence in Bayesian estimation of migration rates. Mol. Ecol. Resour. 14:726-733.
213. Meirmans, P. G., and P. W. Hedrick. 2011. Assessing population structure: F(ST) and related measures. Mol. Ecol. Resour. 11:5-18.
214. Meirmans, P. G., and P. H. Van Tienderen. 2012. The effects of inheritance in tetraploids on genetic diversity and population divergence. Heredity 110:131-137.
215. Michalakis, Y., and L. Excoffier. 1996. A generic estimation of population subdivision using distanced between alleles with special reference for microsatellite loci. Genetics 142:1061-1064.
216. Miller, J. M., R. M. Malenfant, P. David, C. S. Davis, J. Poissant, J. T. Hogg, et al. 2014. Estimating genome-wide heterozygosity: effects of demographic history and marker type. Heredity 112:240-247.
217. Morin, P., F. Archer, V. Pease, B. Hancock-Hanser, K. Robertson, R. Huebinger, et al. 2012. Empirical comparison of single nucleotide polymorphisms and microsatellites for population and demographic analyses of bowhead whales. Endanger. Species Res. 19:129-147.
218. Narum, S. R., M. Banks, T. D. Beacham, M. R. Bellinger, M. R. Campbell, J. Dekoning, et al. 2008. Differentiating salmon populations at broad and fine geographical scales with microsatellites and single nucleotide polymorphisms. Mol. Ecol. 17:3464-3477.
219. Nauta, M. J., and F. J. Weissing. 1996. Constraints on allele size at microsatellite loci: implications for genetic differentiation. Genetics 143:1021-1032.
220. Nei, M. 1973. Analysis of gene diversity in subdivided populations. Proc. Natl Acad. Sci. 70:3321-3323.
221. Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583-590.
222. Nielsen, R., and P. J. Palsbøll. 1999. Single-locus tests of microsatellite evolution: multi-step mutations and constraints on allele size. Mol. Phylogenet. Evol. 11:477-484.
223. Oddou-Muratorio, S., G. G. Vendramin, J. Buiteveld, and B. Fady. 2009. Population estimators or progeny tests: what is the best method to assess null allele frequencies at SSR loci? Conserv. Genet. 10:1343-1347.
224. Odong, T. L., J. van Heerwaarden, J. Jansen, T. J. L. van Hintum, and F. A. van Eeuwijk. 2011. Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data? Theor. Appl. Genet. 123:195-205.
225. Odong, T. L., J. van Heerwaarden, T. J. L. van Hintum, F. A. van Eeuwijk, and J. Jansen. 2013. Improving hierarchical clustering of genotypic data via principal component analysis. Crop Sci. 53:1546-1554.
226. Orozco-terWengel, P., J. Corander, and C. Schlötterer. 2011. Genealogical lineage sorting leads to significant, but incorrect Bayesian multilocus inference of population structure. Mol. Ecol. 20:1108-1121.
227. Ozerov, M., A. Vasemägi, V. Wennevik, R. Diaz-Fernandez, M. Kent, J. Gilbey, et al. 2013. Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification. PLoS ONE 8:e82434.
228. Paetkau, D., R. Slade, M. Burden, and A. Estoup. 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.
229. Palsbøll, P. J., M. Zachariah Peery, and M. Bérubé. 2010. Detecting populations in the 'ambiguous' zone: kinship-based estimation of population structure at low genetic divergence. Mol. Ecol. Resour. 10:797-805.
230. Palstra, F. P., and D. E. Ruzzante. 2008. Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Mol. Ecol. 17:3428-3447.
231. Patterson, N., A. L. Price, and D. Reich. 2006. Population structure and eigenanalysis. PLoS Genet. 2:e190.
232. Payseur, B. A., and A. D. Cutter. 2006. Integrating patterns of polymorphism at SNPs and STRs. Trends Genet. 22:424-429.
233. Payseur, B. A., and P. Jing. 2009. A genomewide comparison of population structure at STRPs and nearby SNPs in humans. Mol. Biol. Evol. 26:1369-1377.
234. Paz-Vinas, I., E. Quéméré, L. Chikhi, G. Loot, and S. Blanchet. 2013. The demographic history of populations experiencing asymmetric gene flow: combining simulated and empirical data. Mol. Ecol. 22:3279-3291.
235. Pearse, D. E., and K. A. Crandall. 2004. Beyond F(ST): analysis of population genetic data for conservation. Conserv. Genet. 5:585-602.
236. Pearson, C. E., K. Nichol Edamura, and J. D. Cleary. 2005. Repeat instability: mechanisms of dynamic mutations. Nat. Rev. Genet. 6:729-742.
237. Peel, D., R. S. Waples, G. M. Macbeth, C. Do, and J. R. Ovenden. 2013. Accounting for missing data in the estimation of contemporary genetic effective population size (Ne). Mol. Ecol. Resour. 13:243-253.
238. Peery, M. Z., R. Kirby, B. N. Reid, R. Stoelting, E. Doucet-Bëer, S. Robinson, et al. 2012. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21:3403-3418.
239. Peery, M. Z., B. N. Reid, R. Kirby, R. Stoelting, E. Doucet-Bëer, S. Robinson, et al. 2013. More precisely biased: increasing the number of markers is not a silver bullet in genetic bottleneck testing. Mol. Ecol. 22:3451-3457.
240. Pemberton, T. J., C. I. Sandefur, M. Jakobsson, and N. A. Rosenberg. 2009. Sequence determinants of human microsatellite variability. BMC Genom. 10:612.
241. Petit, R. J., M.-F. Deguilloux, J. Chat, D. Grivet, P. Garnier-Géré, and G. G. Vendramin. 2005. Standardizing for microsatellite length in comparisons of genetic diversity. Mol. Ecol. 14:885-890.
242. Piry, S., G. Luikart, and J. Cornuet. 1999. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90:502-503.
243. Piry, S., A. Alapetite, J.-M. Cornuet, D. Paetkau, L. Baudouin, and A. Estoup. 2004. GENECLASS2: a software for genetic assignment and first-generation migrant detection. J. Hered. 95:536-539.
244. Portnoy, D. S., and J. R. Gold. 2012. Evidence of multiple vicariance in a marine suture-zone in the Gulf of Mexico. J. Biogeogr. 39:1499-1507.
245. Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.
246. Pritchard, J. K., X. Wen, and D. Falush. 2010. Documentation for structure software department of human genetics. University of Chicago, Chicago, IL.
247. Puebla, O., E. Bermingham, and W. O. McMillan. 2012. On the spatial scale of dispersal in coral reef fishes. Mol. Ecol. 21:5675-5688.
248. Queloz, V., A. Duò, T. N. Sieber, and C. R. Grünig. 2010. Microsatellite size homoplasies and null alleles do not affect species diagnosis and population genetic analysis in a fungal species complex. Mol. Ecol. Resour. 10:348-367.
249. Ramakrishnan, U., and J. L. Mountain. 2004. Precision and accuracy of divergence time estimates from STR and SNPSTR variation. Mol. Biol. Evol. 21:1960-1971.
250. Reich, D., A. L. Price, and N. Patterson. 2008. Principal component analysis of genetic data. Nat. Genet. 40:491-492.
251. Rodríguez-Ramilo, S. T., and J. Wang. 2012. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12:873-884.
252. Ross, C. T., J. A. Weise, S. Bonnar, D. Nolin, J. Satkoski Trask, D. G. Smith, et al. 2014. An empirical comparison of short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) for relatedness estimation in Chinese rhesus macaques (Macaca mulatta). Am. J. Primatol. 76:313-324.
253. Rousset, F. 1996. Equilibrium values of measures of population subdivision for stepwise mutation processes. Genetics 142:1357-1362.
254. Rowe, H. C., S. Renaut, and A. Guggisberg. 2011. RAD in the realm of next-generation sequencing technologies. Mol. Ecol. 20:3499-3502.
255. Roy, D., T. R. Hurlbut, D. E. Ruzzante, and D. J. Fraser. 2012. Biocomplexity in a demersal exploited fish, white hake (Urophycis tenuis): depth-related structure and inadequacy of current management approaches. Can. J. Fish Aquat. Sci. 69:415-429.
256. RoyChoudhury, A., and M. Stephens. 2007. Fast and accurate estimation of the population-scaled mutation rate, θ, from microsatellite genotype data. Genetics 176:1363-1366.
257. Ruzzante, D. E. 1998. A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance. Can. J. Fish Aquat. Sci. 55:1-14.
258. Ryman, N., F. W. Allendorf, P. E. Jorde, L. Laikre, and O. Hössjer. 2014. Samples from subdivided populations yield biased estimates of effectivee size that overestimate the rate of loss of genetic variation. Mol. Ecol. Resour. 14:87-99.
259. Safner, T., M. P. Miller, B. H. McRae, M.-J. Fortin, and S. Manel. 2011. Comparison of bayesian clustering and edge detection methods for inferring boundaries in landscape genetics. Int. J. Mol. Sci. 12:865-889.
260. Santure, A. W., J. Stapley, A. D. Ball, T. R. Birkhead, T. Burke, and J. Slate. 2010. On the use of large marker panels to estimate inbreeding and relatedness: empirical and simulation studies of a pedigreed zebra finch population typed at 771 SNPs. Mol. Ecol. 19:1439-1451.
261. Sanz, N., R. M. Araguas, R. Fernández, M. Vera, and J.-L. García-Marín. 2009. Efficiency of markers and methods for detecting hybrids and introgression in stocked populations. Conserv. Genet. 10:225-236.
262. Sawaya, S., A. Bagshaw, E. Buschiazzo, P. Kumar, S. Chowdhury, M. A. Black, et al. 2013. Microsatellite tandem repeats are abundant in human promoters and are associated with regulatory elements. PLoS ONE 8:e54710.
263. Schlötterer, C. 2001. Genealogical inference of closely related species based on microsatellites. Genet. Res. 78:209-212.
264. Schopen, G. C. B., H. Bovenhuis, M. H. P. W. Visker, and J. A. M. van Arendonk. 2008. Comparison of information content for microsatellites and SNPs in poultry and cattle. Anim. Genet. 39:451-453.
265. Schwartz, M. K., and K. S. McKelvey. 2008. Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conserv. Genet. 10:441-452.
266. Seeb, J. E., G. Carvalho, L. Hauser, K. Naish, S. Roberts, and L. W. Seeb. 2011. Single-nucleotide polymorphism (SNP) discovery and applications of SNP genotyping in nonmodel organisms. Mol. Ecol. Resour. 11(Suppl. 1):1-8.
267. Sefc, K. M., R. B. Payne, and M. D. Sorenson. 2007. Genetic differentiation after founder events: an evaluation of F(ST) estimators with empirical and simulated data. Evol. Ecol. Res. 9:21-39.
268. Segarra-Moragues, J. G., and P. Catalán. 2008. Glacial survival, phylogeography, and a comparison of microsatellite evolution models for resolving population structure in two species of dwarf yams (Borderea, Dioscoreaceae) endemic to the central Pyrenees. Plant Ecol. Divers 1:229-243.
269. Selkoe, K. A., and R. J. Toonen. 2006. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9:615-629.
270. Serneels, S., and T. Verdonck. 2008. Principal component analysis for data containing outliers and missing elements. Comput. Stat. Data Anal. 52:1712-1727.
271. Shannon, C. E. 1948a. A mathematical theory of communication. Bell Syst. Tech. J. 27:623-656.
272. Shannon, C. E. 1948b. A mathematical theory of communication. Bell Syst. Tech. J. 27:379-423.
273. Sherwin, W. B. 2010. Entropy and information approaches to genetic diversity and its expression: genomic geography. Entropy 12:1765-1798.
274. Sherwin, W. B., F. Jabot, R. Rush, and M. Rossetto. 2006. Measurement of biological information with applications from genes to landscapes. Mol. Ecol. 15:2857-2869.
275. Skrbinšek, T., M. Jelenčič, L. Waits, I. Kos, K. Jerina, and P. Trontelj. 2012. Monitoring the effective population size of a brown bear (Ursus arctos) population using new single-sample approaches. Mol. Ecol. 21:862-875.
276. Slatkin, M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:457-462.
277. Song, S., D. K. Dey, and K. E. Holsinger. 2011. Genetic diversity of microsatellite loci in hierarchically structured populations. Theor. Popul. Biol. 80:29-37.
278. Sorenson, M. D., and J. M. DaCosta. 2011. Genotyping HapSTR loci: phase determination from direct sequencing of PCR products. Mol. Ecol. Resour. 11:1068-1075.
279. Sousa, V. C., A. Grelaud, and J. Hey. 2011. On the nonidentifiability of migration time estimates in isolation with migration models. Mol. Ecol. 20:3956-3962.
280. Stolle, E., J. H. Kidner, and R. F. A. Moritz. 2013. Patterns of evolutionary conservation of microsatellites (SSRs) suggest a faster rate of genome evolution in Hymenoptera than in Diptera. Genome Biol. Evol. 5:151-162.
281. Storz, J. F., and M. A. Beaumont. 2002. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution 56:154-166.
282. Strasburg, J. L., and L. H. Rieseberg. 2010. How robust are "isolation with migration" analyses to violations of the im model? A simulation study. Mol. Biol. Evol. 27:297-310.
283. Strasburg, J. L., and L. H. Rieseberg. 2011. Interpreting the estimated timing of migration events between hybridizing species. Mol. Ecol. 20:2353-2366.
284. Sun, J. X., J. C. Mullikin, N. Patterson, and D. E. Reich. 2009. Microsatellites are molecular clocks that support accurate inferences about history. Mol. Biol. Evol. 26:1017-1027.
285. Tajima, F. 1996. Infinite-allele model and infinite-site model in population genetics. J. Genet. 75:27-31.
286. Tallmon, D. A., A. Koyuk, G. Luikart, and M. A. Beaumont. 2008. ONeSAMP: a program to estimate effective population size using approximate Bayesian computation. Mol. Ecol. Resour. 8:299-301.
287. Tallmon, D. A., D. Gregovich, R. S. Waples, C. Scott Baker, J. Jackson, B. L. Taylor, et al. 2010. When are genetic methods useful for estimating contemporary abundance and detecting population trends? Mol. Ecol. Resour. 10:684-692.
288. Tsitrone, A., F. Rousset, and P. David. 2001. Heterosis, Marker Mutational Processes and Population Inbreeding History. Genetics 159:1845-1859.
289. Twyford, A. D., and R. A. Ennos. 2012. Next-generation hybridization and introgression. Heredity 108:179-189.
290. Uwimana, B., L. D'Andrea, F. Felber, D. A. P. Hooftman, H. C. M. den Nijs, M. J. M. Smulders, et al. 2012. A Bayesian analysis of gene flow from crops to their wild relatives: cultivated (Lactuca sativa L.) and prickly lettuce (L. serriola L.) and the recent expansion of L. serriola in Europe. Mol. Ecol. 21:2640-2654.
291. Vähä, J.-P., and C. R. Primmer. 2006. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol. Ecol. 15:63-72.
292. Väli, U., A. Einarsson, L. Waits, and H. Ellegren. 2008. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations? Mol. Ecol. 17:3808-3817.
293. Väli, Ü., P. Saag, V. Dombrovski, B.-U. Meyburg, G. Maciorowski, T. Mizera, et al. 2010. Microsatellites and single nucleotide polymorphisms in avian hybrid identification: a comparative case study. J. Avian Biol. 41:34-49.
294. Van Oosterhout, C., W. F. Hutchinson, D. P. M. Wills, and P. Shipley. 2004. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4:535-538.
295. Vaughan, L. K., J. Divers, M. Padilla, D. T. Redden, H. K. Tiwari, D. Pomp, et al. 2009. The use of plasmodes as a supplement to simulations: a simple example evaluating individual admixture estimation methodologies. Comput. Stat. Data Anal. 53:1755-1766.
296. Verdu, P., and N. A. Rosenberg. 2011. A general mechanistic model for admixture histories of hybrid populations. Genetics 189:1413-1426.
297. Vucetich, J. A., and T. A. Waite. 1998. Number of censuses required for demographic estimation of effective population size. Conserv. Biol. 12:1023-1030.
298. Wang, J. 2005. Estimation of effective population sizes from data on genetic markers. Philos. Trans. R. Soc. B Biol. Sci. 360:1395-1409.
299. Wang, J. 2009. A new method for estimating effective population sizes from a single sample of multilocus genotypes. Mol. Ecol. 18:2148-2164.
300. Wang, J. 2012. On the measurements of genetic differentiation among populations. Genet. Res. 94:275-289.
301. Wang, J., and K. T. Scribner. 2014. Parentage and sibship inference from markers in polyploids. Mol. Ecol. Resour. 14:541-553 doi: 10.1111/1755-0998.12210.
302. Wang, J., P. Brekke, E. Huchard, L. A. Knapp, and G. Cowlishaw. 2010. Estimation of parameters of inbreeding and genetic drift in populations with overlapping generations. Evolution 64:1704-1718.
303. Wang, C., K. B. Schroeder, and N. A. Rosenberg. 2012. A maximum-likelihood method to correct for allelic dropout in microsatellite data with no replicate genotypes. Genetics 192:651-669.
304. Waples, R. S. 2010. Spatial-temporal stratifications in natural populations and how they affect understanding and estimation of effective population size. Mol. Ecol. Resour. 10:785-796.
305. Waples, R. S., and C. Do. 2010. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3:244-262.
306. Waples, R. S., and P. R. England. 2011. Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics 189:633-644.
307. Waples, R. S., and O. Gaggiotti. 2006. What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol. Ecol. 15:1419-1439.
308. Waples, R. S., and R. K. Waples. 2011. Inbreeding effective population size and parentage analysis without parents. Mol. Ecol. Resour. 11:162-171.
309. Wegmann, D., C. Leuenberger, S. Neuenschwander, and L. Excoffier. 2010. ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics 11:116.
310. Wei, N., J. B. Bemmels, and C. W. Dick. 2014. The effects of read length, quality and quantity on microsatellite discovery and primer development: from Illumina to PacBio. Mol. Ecol. Resour. 14:953-965.
311. Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38:1358-1370.
312. Weir, B. S., and W. G. Hill. 2002. Estimating F-statistics. Annual Review. Genetics 36:721-750.
313. Whitlock, M. C. 2011. G'(ST) and D do not replace F(ST). Mol. Ecol. 20:1083-1091.
314. Whittaker, J. C., R. M. Harbord, N. Boxall, I. Mackay, G. Dawson, and R. M. Sibly. 2003. Likelihood-based estimation of microsatellite mutation rates. Genetics 164:781-787.
315. Wierdl, M., M. Dominska, and T. D. Petes. 1997. Microsatellite instability in yeast: dependence on the length of the microsatellite. Genetics 146:769-779.
316. Williamson-Natesan, E. G. 2005. Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv. Genet. 6:551-562.
317. Wilson, G. A., and B. Rannala. 2003. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177-1191.
318. Wright, S. 1943. Isolation by distance. Genetics 28:114-138.
319. Wright, S. 1951. The genetical structure of populations. Ann. Eugen. 15:323-354.
320. Wu, C.-H., and A. J. Drummond. 2011. Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo. Genetics 188:151-164.
321. Xu, H., and Y.-X. Fu. 2004. Estimating effective population size or mutation rate with microsatellites. Genetics 166:555-563.
322. Xu, R., and D. Wunsch. 2005. Survey of clustering algorithms. IEEE Trans. Neural Networks 16:645-678.
323. Zhang, J., P. Niyogi, and M. S. McPeek. 2009. Laplacian eigenfunctions learn population structure. PLoS ONE 4:e7928.
324. Ziegler, J. O., M. Wälther, T. R. Linzer, G. Segelbacher, M. Stauss, C. Roos, et al. 2009. Frequent non-reciprocal exchange in microsatellite-containing-DNA-regions of vertebrates. J. Zool. Syst. Evol. Res. 47:15-20.