Comparing inferences derived from microsatellite and RADseq datasets: a case study involving threatened bull trout

  • Justin BohlingEmail author
  • Maureen Small
  • Jennifer Von Bargen
  • Amelia Louden
  • Patrick DeHaan
Research Article


Technological advancements have allowed geneticists to exploit an increasing array of molecular markers, many of which have different properties and may provide contrasting insights into the evolutionary history and structure of populations. This has important consequences for conservation managers attempting to identify units at which to conserve intraspecific diversity. In this study we compared the inferences derived from nuclear microsatellites and restriction-site associated DNA (RADseq) data for a threatened freshwater fish, the bull trout Salvelinus confluentus. For both marker types we generated data for the same suite of individuals collected from 24 populations distributed across the species range. The RADseq data were low coverage (mean site coverage < 3X), so we implemented a probabilistic genotyping approach. We performed a comparable suite of analyses for both datasets. Both datasets revealed similar broad patterns of subdivision that reflected primary evolutionary lineages (Coastal and Interior clades). However, the RADseq more clearly and consistently identified the hierarchical phylogenetic structure. Some populations had varying assignments to these lineages depending on the dataset. RADseq data also suggested admixture has shaped the genomic character of several populations. Such a signal was not apparent with the microsatellites, suggesting that the datasets are revealing different aspects of population history. Our study provides a valuable case study in how advances in molecular technology can enhance our understanding of a relatively well-studied species. It also underscores the importance of framing findings generated with high-throughput sequencing technology within the context of past research to enhance conservation decision making.


Salmonidae Salvelinus confluentus Restriction-site associated DNA sequencing Conservation genomics Intraspecific diversity 



Funding for this project was provided by the US Fish and Wildlife Service Fish and Aquatic Conservation Program and Washington State general funds. We sincerely thank the numerous individual biologists and technicians from the different federal, state, tribal, and non-governmental agencies who collected tissue samples used in these analyses. We also thank Sewall Young and Ken Warheit (WDFW) for sharing scripts for running Stacks. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the US Fish and Wildlife Service.

Supplementary material

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Supplementary material 1 (DOCX 111 KB)
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Supplementary material 2 (DOCX 2951 KB)


  1. Angers B, Bernatchez L, Angers A, Desgroseillers L (1995) Specific microsatellite loci for brook charr (Salvelinus fontinalis Mitchill) reveal strong population subdivision on a microgeographic scale. J Fish Biol 47:177–185CrossRefGoogle Scholar
  2. Ardren WR, DeHaan PW, Smith CT et al (2011) Genetic structure, evolutionary history, and conservation units of bull trout in the coterminous United States. Trans Am Fish Soc 140:506–525. CrossRefGoogle Scholar
  3. Baird, NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoSOne 3:e3376CrossRefGoogle Scholar
  4. Besnier F, Glover KA (2013) Parallel structure: a R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8:1–9. CrossRefGoogle Scholar
  5. Blankenship SM, Campbell MR, Hess JE et al (2011) Major lineages and metapopulations in Columbia River Oncorhynchus mykiss are structured by dynamic landscape features and environments. Trans Am Fish Soc 140:665–684. CrossRefGoogle Scholar
  6. Bradbury IR, Hamilton LC, Dempson B et al (2015) Transatlantic secondary contact in Atlantic Salmon, comparing microsatellites, a single nucleotide polymorphism array and restriction-site associated DNA sequencing for the resolution of complex spatial structure. Mol Ecol 24:5130–5144. CrossRefPubMedGoogle Scholar
  7. Buerkle AC, Gompert Z (2013) Population genomics based on low coverage sequencing: how low should we go? Mol Ecol 22:3028–3035. CrossRefGoogle Scholar
  8. Catchen J, Hohenlohe PA, Bassham S et al (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Corander J, Majander KK, Cheng L, Merilä J (2013) High degree of cryptic population differentiation in the Baltic Sea herring Clupea harengus. Mol Ecol 22:2931–2940. CrossRefPubMedGoogle Scholar
  10. Costello AB, Down TE, Pollard SM et al (2003) Influence of history and contemporary stream hydrology on the evolution of genetic diversity within species: an examination of microsatellite DNA variation in bull trout, Salvelinus confluentus (Pisces: Salmonidae). Evolution 57:328.;2 CrossRefPubMedGoogle Scholar
  11. Crane PA, Lewis CJ, Kretschmer EJ, Miller SJ, Spearman WJ, DeCicco AL, Lisac MJ, Wenburg JK (2004) Characterization and inheritance of seven microsatellite loci from Dolly Varden, Salvelinus malma, and cross-species amplification in Arctic char, S. alpinus. Con Gen 5:737–741CrossRefGoogle Scholar
  12. DeFaveri J, Viitaniemi H, Leder E, Merilä J (2013) Characterizing genic and nongenic molecular markers: comparison of microsatellites and SNPs. Mol Ecol Resour 13:377–392. CrossRefPubMedGoogle Scholar
  13. DeHaan PW, Ardren WR (2005) Characterization of 20 highly variable tetranucleotide microsatellite loci for bull trout (Salvelinus confluentus) and cross-amplification in other Salvelinus species. Mol Ecol Notes 5:582–585. CrossRefGoogle Scholar
  14. DeHaan PW, Bernall SR, Dossantos JM et al (2011) Use of genetic markers to aid in re-establishing migratory connectivity in a fragmented metapopulation of bull trout (Salvelinus confluentus). Can J Fish Aquat Sci 68:1952–1969. CrossRefGoogle Scholar
  15. DeYoung RW, Honeycutt RL (2008) The molecular toolbox: genetic techniques in wildlife ecology and management. J Wildl Manage 69:1362–1384.;2 CrossRefGoogle Scholar
  16. Elbers JP, Clostio RW, Taylor SS (2016) Population genetic inferences using immune gene SNPs mirror patterns inferred by microsatellites. Mol Ecol Resour 17:481–491. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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. CrossRefGoogle Scholar
  18. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes. Genetics 131:479–491. CrossRefPubMedPubMedCentralGoogle Scholar
  19. Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40:481–501. CrossRefGoogle Scholar
  20. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedPubMedCentralGoogle Scholar
  21. Fumagalli M, Vieira FG, Korneliussen TS et al (2013) Quantifying population genetic differentiation from next-generation sequencing data. Genetics 195:979–992. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Fumagalli M, Vieira FG, Linderoth T, Nielsen R (2014) NgsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics 30:1486–1487. CrossRefPubMedPubMedCentralGoogle Scholar
  23. Gompert Z, Buerkle CA (2016) What, if anything, are hybrids: enduring truths and challenges associated with population structure and gene flow. Evol Appl 9:909–923. CrossRefPubMedPubMedCentralGoogle Scholar
  24. Groves CP, Cotterill FPD, Gippoliti S et al (2017) Species definitions and conservation: a review and case studies from African mammals. Conserv Genet 18:1247–1256. CrossRefGoogle Scholar
  25. Haasl RJ, Payseur B (2010) Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity 106:158–171. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Hodel RGJ, Segovia-Salcedo MC, Landis JB et al (2016) The report of my death was an exaggeration: a review for researchers using microsatellites in the 21st Century. Appl Plant Sci 4:1600025. CrossRefGoogle Scholar
  27. Hodel RGJ, Chen S, Payton AC et al (2017) Adding loci improves phylogeographic resolution in red mangroves despite increased missing data: comparing microsatellites and RAD-Seq and investigating loci filtering. Sci Rep 7:17598. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Jeffries DL, Copp GH, Lawson Handley L et al (2016) Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the Crucian carp, Carassius carassius, L. Mol Ecol 25:2997–3018. CrossRefPubMedGoogle Scholar
  29. Jensen EL, Govindarajulu P, Russello M (2013) When the shoe doesn’t fit: applying conservation unit concepts to western painted turtles at their northern periphery. Conserv Genet 15:261–274. CrossRefGoogle Scholar
  30. Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405. CrossRefPubMedGoogle Scholar
  31. Kohn MH, Murphy WJ, Ostrander EA, Wayne RK (2006) Genomics and conservation genetics. Trends Ecol Evol 21:629–637. CrossRefPubMedGoogle Scholar
  32. Korneliussen TS, Albrechtsen A, Nielsen R (2014) ANGSD: analysis of next generation sequencing data. BMC Bioinform 15:356. CrossRefGoogle Scholar
  33. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. CrossRefPubMedPubMedCentralGoogle Scholar
  34. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079. CrossRefPubMedPubMedCentralGoogle Scholar
  35. Liu N, Chen L, Wang S et al (2005) Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure. BMC Genet 6:26. CrossRefGoogle Scholar
  36. Luikart G, Allendorf FW, Cornuet J-M, Sherwin WB (1998) Distortion of allele frequency distributions provides a test for recent population bottlenecks. J of Heredity 89:238–247CrossRefGoogle Scholar
  37. Marin K, Coon A, Fraser DJ (2017) Traditional ecological knowledge reveals the extent of sympatric lake trout diversity and habitat preferences. Ecol Soc 22:20. CrossRefGoogle Scholar
  38. Martin CH, Cutler JS, Friel JP et al (2015) Complex histories of repeated gene flow in Cameroon crater lake cichlids cast doubt on one of the clearest examples of sympatric speciation. Evolution 69:1406–1422. CrossRefPubMedGoogle Scholar
  39. Mastretta-Yanes A, Arrigo N, Alvarez N et al (2015) Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference. Mol Ecol Resour 15:28–41. CrossRefPubMedGoogle Scholar
  40. McDevitt AD, Mariani S, Hebblewhite M et al (2009) Survival in the Rockies of an endangered hybrid swarm from diverged caribou (Rangifer tarandus) lineages. Mol Ecol 18:665–679. CrossRefPubMedGoogle Scholar
  41. McKenna A, Hanna M, Banks E et al (2010) The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. CrossRefPubMedPubMedCentralGoogle Scholar
  42. McMahon BJ, Teeling EC, Höglund J (2014) How and why should we implement genomics into conservation? Evol Appl 7:999–1007. CrossRefPubMedPubMedCentralGoogle Scholar
  43. McPhail JD, Baxter JS (1996) A review of bull trout (Salvelinus confluentus) life-history and habitat use in relation to compensation and improvement opportunities. Department of Zoology, University of British Columbia, VancouverGoogle Scholar
  44. Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA (2007) Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res 17:240–248CrossRefGoogle Scholar
  45. Mogen JT, Kaeding LR (2005) Identification and characterization of migratory and nonmigratory Bull Trout populations in the St. Mary River Drainage, Montana. Trans Amer Fish Soc 134:841–852CrossRefGoogle Scholar
  46. Narum SR, Hess JE, Matala AP (2010) Examining genetic lineages of Chinook salmon in the Columbia River Basin. Trans Am Fish Soc 139:1465–1477. CrossRefGoogle Scholar
  47. Nielsen R, Paul JS, Albrechtsen A, Song YS (2011) Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 12:443–451. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Nielsen R, Korneliussen T, Albrechtsen A et al (2012) SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data. PLoS ONE 7:e37558. CrossRefPubMedPubMedCentralGoogle Scholar
  49. Northcote TG (1997) Potamodromy in Salmonidae—living and moving in the fast lane. N Amer J Fish Manag 17:1029–1045CrossRefGoogle Scholar
  50. Paris JR, Stevens JR, Catchen JM (2017) Lost in parameter space: a road map for Stacks. Methods Ecol Evol 8:1360–1373. CrossRefGoogle Scholar
  51. Piccolo JJ (2016) Conservation genomics: coming to a salmonid near you. J Fish Biol 89:2735–2740. CrossRefPubMedGoogle Scholar
  52. Pickrell J, Pritchard J (2012) Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet 8:e1002967. CrossRefPubMedPubMedCentralGoogle Scholar
  53. Pisa G, Orioli V, Spilotros G et al (2015) Detecting a hierarchical genetic population structure: the case study of the Fire Salamander (Salamandra salamandra) in Northern Italy. Ecol Evol 5:743–758. CrossRefPubMedPubMedCentralGoogle Scholar
  54. Polfus JL, Manseau M, Simmons D et al (2016) Łeghágots’enetę (learning together): the importance of indigenous perspectives in the identification of biological variationGoogle Scholar
  55. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  56. Puckett EE (2017) Variability in total project and per sample genotyping costs under varying study designs including with microsatellites or SNPs to answer conservation genetic questions. Conserv Genet Resour 9:289–304. CrossRefGoogle Scholar
  57. Putman AI, Carbone I (2014) Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol Evol 4:4399–4428. CrossRefPubMedPubMedCentralGoogle Scholar
  58. Ramey IIRR, Wehausen JD, Liu H-P et al (2007) How King et al. (2006) define an “evolutionary distinction”. of a mouse subspecies: a response. Mol Ecol 16:3518–3521. CrossRefGoogle Scholar
  59. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.
  60. Rieman BE, McIntyre JD (1993) Demographic and habitat requirements for conservation of bull trout. Ogen, TampaCrossRefGoogle Scholar
  61. Rieman BE, Dunham JB (2000) Metapopulations and salmonids: a synthesis of life history patterns and empirical observations. Eco Fresh Fish 9:51–64CrossRefGoogle Scholar
  62. Skotte L, Korneliussen TS, Albrechtsen A (2013) Estimating individual admixture proportions from next generation sequencing data. Genetics 195:693–702. CrossRefPubMedPubMedCentralGoogle Scholar
  63. Spruell P, Rieman BE, Knudsen KL et al (1999) Genetic population structure within streams: microsatellite analysis of bull trout populations. Ecol Freshw Fish 8:114–121. CrossRefGoogle Scholar
  64. Spruell P, Hemmingsen AR, Howell PJ et al (2003) Conservation genetics of bull trout: geographic distribuition of variation at microsatellite loci. Conserv Genet 4:17–29CrossRefGoogle Scholar
  65. Sunnucks P (2000) Efficient genetic markers for population biology. Trends Ecol Evol 15:199–203. CrossRefPubMedGoogle Scholar
  66. Taylor EB, Pollard S, Louie D (1999) Mitochondrial DNA variation in bull trout (Salvelinus confluentus) from northwestern North America: implications for zoogeography and conservation. Mol Ecol 8:1155–1170. CrossRefPubMedGoogle Scholar
  67. Thrasher DJ, Butcher BG, Campagna L et al (2018) Double-digest RAD sequencing outperforms microsatellite loci at assigning paternity and estimating relatedness: a proof of concept in a highly promiscuous bird. Mol Ecol Resour. CrossRefPubMedGoogle Scholar
  68. Twyford AD, Ennos RA (2012) Next-generation hybridization and introgression. Heredity 108:179–189. CrossRefPubMedGoogle Scholar
  69. U.S. Fish and Wildlife Service (2015) Recovery plan for the coterminous United States population of bull trout. Portland, ORGoogle Scholar
  70. Unger S Jr, Sutton OR, Williams T R (2013) Population genetics of the eastern hellbender (Cryptobranchus alleganiensis alleganiensis) across multiple spatial scales. PLoS ONE 8:1–14. CrossRefGoogle Scholar
  71. Urban J (2014) How does bowtie2 assign MAPQ scores? [Blog] Biofinysics. URL:
  72. Väli Ü, Saag P, Dombrovski V et al (2010) Microsatellites and single nucleotide polymorphisms in avian hybrid identification: a comparative case study. J Avian Biol 41:34–49. CrossRefGoogle Scholar
  73. Waples RS, Gaggiotti O (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. CrossRefPubMedGoogle Scholar
  74. Warnock WG, Rasmussen JB, Taylor EB (2010) Genetic clustering methods reveal bull trout (Salvelinus confluentus) fine-scale population structure as a spatially nested hierarchy. Conserv Genet 11:1421–1433. CrossRefGoogle Scholar
  75. Wayne RK, Shaffer HB (2016) Hybridization and endangered species protection in the molecular era. Mol Ecol 81:778–793. CrossRefGoogle Scholar
  76. Whiteley AR, Spruell P, Rieman BE, Allendorf FW (2006) Fine-scale genetic structure of bull trout at the southern Llimit of their distribution. Trans Am Fish Soc 135:1238–1253. CrossRefGoogle Scholar
  77. Zink RM, Barrowclough GF (2008) Mitochondrial DNA under siege in avian phylogeography. Mol Ecol 17:2107–2121. CrossRefPubMedGoogle Scholar

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© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.Abernathy Fish Technology Center, US Fish and Wildlife ServiceLongviewUSA
  2. 2.Molecular Genetics Laboratory, Washington Department of Fish and WildlifeOlympiaUSA
  3. 3.Western Washington Fish and Wildlife Conservation Office, Fish and Wildlife ServiceLaceyUSA

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