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Statistical power for detecting genetic divergence—organelle versus nuclear markers

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Abstract

Statistical power is critical in conservation for detecting genetic differences in space or time from allele frequency data. Organelle and nuclear genetic markers have fundamentally different transmission dynamics; the potential effect of these differences on power to detect divergence have been speculated on but not investigated. We examine, analytically and with computer simulations, the relative performance of organelle and nuclear markers under basic, ideal situations. We conclude that claims of a generally higher resolving power of either marker type are not correct. The ratio R = F ST,organelle/F ST,nuclear varies between 1 and 4 during differentiation and this greatly affects the power relationship. When nuclear F ST is associated with organelle differentiation four times higher, the power of the organelle marker is similar to two nuclear loci with the same allele frequency distribution. With large sample sizes (≥ 50) and several populations or many alleles per locus (≥5), the power difference may typically be disregarded when nuclear F ST > 0.05. To correctly interpret observed patterns of genetic differentiation in practical situations, the expected F STs and the statistical properties (i.e., power analysis) of the genetic markers used should be evaluated, taking the observed allele frequency distributions into consideration.

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References

  • Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell Publishing, Malden, Massachusetts

    Google Scholar 

  • Angers B, Bernatchez L (1998) Combined use of SMM and non-SMM methods to infer fine structure and evolutionary history of closely related brook charr (Salvelinus fontinalis, Salmonidae) populations from microsatellites. Mol Biol Evol 15:143–159

    CAS  Google Scholar 

  • Arnaud-Haond S, Bonhomme F, Blanc F (2003) Large discrepancies in differentiation of allozymes, nuclear and mitochondrial DNA loci in recently founded Pacific populations of pearl oyster Pinctada margaritifera. J Evol Biol 16:388–398. doi:10.1046/j.1420-9101.2003.00549.x

    Article  CAS  PubMed  Google Scholar 

  • Avise JC (2004) Molecular markers, natural history, and evolution, 2nd edn. Sinauer Associates, Inc. Publishers, Sunderland, Massachusetts

    Google Scholar 

  • Bamshad M, Fraley AE, Crawford MH et al (1996) mtDNA variation in caste populations of Andhra Pradesh, India. Hum Biol 68:1–28

    CAS  PubMed  Google Scholar 

  • Birky CW Jr, Maruyama T, Fuerst P (1983) An approach to population and evolutionary genetic theory for genes in mitochondria and chloroplasts, and some results. Genetics 103:513–527

    PubMed  Google Scholar 

  • Birky CW Jr, Fuerst P, Maruyama T (1989) Organelle gene diversity under migration, mutation, and drift-equilibrium expectations, approach to equilibrium, effects of heteroplasmic cells, and comparison to nuclear genes. Genetics 121:613–627

    PubMed  Google Scholar 

  • Bowen BW, Bass AL, Soares L et al (2005) Conservation implications of complex population structure: lessons from the loggerhead turtle (Caretta caretta). Mol Ecol 14:2389–2402. doi:10.1111/j.1365-294X.2005.02598.x

    Article  CAS  PubMed  Google Scholar 

  • Crochet PA (2000) Genetic structure of avian populations—allozymes revisited. Mol Ecol 9:1463–1469. doi:10.1046/j.1365-294x.2000.01026.x

    Article  CAS  PubMed  Google Scholar 

  • Crow JF, Kimura M (1970) An introduction to population genetics theory. Alpha Editions, Minneapolis, MN

    Google Scholar 

  • Dizon AE, Taylor BL, O’Corry-Crowe GM (1995) Why statistical power is necessary to link analyses of molecular variation to decisions about population structure. Am Fish Soc Symp 17:288–294

    Google Scholar 

  • Escorza-Treviño S, Dizon AE (2000) Phylogeography, intraspecific structure and sex-biased dispersal of Dall’s porpoise, Phocoenoides dalli, revealed by mitochondrial and microsatellite DNA analyses. Mol Ecol 9:1049–1060. doi:10.1046/j.1365-294x.2000.00959.x

    Article  PubMed  Google Scholar 

  • Geffen E, Waidyaratne S, Dalén L et al (2007) Sea ice occurrence predicts genetic isolation in the Arctic fox. Mol Ecol 16:4241–4255. doi:10.1111/j.1365-294X.2007.03507.x

    Article  CAS  PubMed  Google Scholar 

  • Haavie J, Saetre GP, Moum T (2000) Discrepancies in population differentiation at microsatellites, mitochondrial DNA and plumage colour in the pied flycatcher—inferring evolutionary processes. Mol Ecol 9:1137–1148. doi:10.1046/j.1365-294x.2000.00988.x

    Article  CAS  PubMed  Google Scholar 

  • Hamilton MB, Miller JR (2002) Comparing relative rates of pollen and seed gene flow in the island model using nuclear and organelle measures of population structure. Genetics 162:1897–1909

    PubMed  Google Scholar 

  • Hedrick PW (1999) Perspective: highly variable loci and their interpretation in evolution and conservation. Evolution Int J Org Evolution 53:313–318. doi:10.2307/2640768

    Google Scholar 

  • Hedrick PW (2001) Conservation genetics: where are we now? Trends Ecol Evol 16:629–636. doi:10.1016/S0169-5347(01)02282-0

    Article  Google Scholar 

  • Hedrick PW (2005) A standardized genetic differentiation measure. Evolution Int J Org Evolution 59:1633–1638

    CAS  Google Scholar 

  • Hoarau G, Piquet AMT, Van der Veer HW et al (2004) Population structure of plaice (Pleuronectes platessa L.) in northern Europe: a comparison of resolving power between microsatellites and mitochondrial DNA data. J Sea Res 51:183–190. doi:10.1016/j.seares.2003.12.002

    Article  CAS  Google Scholar 

  • Johnson JA, Toepfer JE, Dunn PO (2003) Contrasting patterns of mitochondrial and microsatellite population structure in fragmented populations of greater prairie-chickens. Mol Ecol 12:3335–3347. doi:10.1046/j.1365-294X.2003.02013.x

    Article  CAS  PubMed  Google Scholar 

  • Keyser-Tracqui C, Crubézy E, Ludes B (2003) Nuclear and mitochondrial DNA analysis of a 2,000-year-old necropolis in the Egyin Gol Valley of Mongolia. Am J Hum Genet 73:247–260. doi:10.1086/377005

    Article  CAS  PubMed  Google Scholar 

  • Laikre L, Jorde PE, Ryman N (1998) Temporal change of mitochondrial DNA haplotype frequencies and female effective size in a brown trout (Salmo trutta) population. Evolution Int J Org Evolution 52:910–915. doi:10.2307/2411286

    Google Scholar 

  • Li W-H (1976) Effect of migration on genetic distance. Am Nat 110:841–847. doi:10.1086/283106

    Article  Google Scholar 

  • Liu ZJ, Cordes JF (2004) DNA marker technologies and their applications in aquaculture genetics. Aquaculture 238:1–37. doi:10.1016/j.aquaculture.2004.05.027

    Article  CAS  Google Scholar 

  • Nei M (1973) Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA 70:3321–3323. doi:10.1073/pnas.70.12.3321

    Article  CAS  PubMed  Google Scholar 

  • Nei M (1975) Molecular population genetics and evolution. North-Holland Publishing Company, Amsterdam

    Google Scholar 

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

    Google Scholar 

  • Nyström V, Angerbjörn A, Dalén L (2006) Genetic consequences of a demographic bottleneck in the Scandinavian arctic fox. Oikos 114:84–94. doi:10.1111/j.2006.0030-1299.14701.x

    Article  Google Scholar 

  • Ryman N, Jorde PE (2001) Statistical power when testing for genetic differentiation. Mol Ecol 10:2361–2373. doi:10.1046/j.0962-1083.2001.01345.x

    Article  CAS  PubMed  Google Scholar 

  • Ryman N, Palm S (2006) POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes 6:600–602. doi:10.1111/j.1471-8286.2006.01378.x

    Article  Google Scholar 

  • Ryman N, Palm S, André C et al (2006) Power for detecting genetic divergence: differences between statistical methods and marker loci. Mol Ecol 15:2031–2045. doi:10.1111/j.1365-294X.2006.02839.x

    Article  CAS  PubMed  Google Scholar 

  • Ryman N, Leimar O (2008) Effect of mutation on genetic differentiation among non-equilibrium populations. Evolution Int J Org Evolution. doi:10.1111/j.1558-5646.2008.0453.x

    Google Scholar 

  • Seeb JE, Habitch C, Templin WD et al (1999) Allozyme and mitochondrial DNA variation describe ecologically important genetic structure of even-year pink salmon inhabiting Prince William Sound, Alaska. Ecol Freshw Fish 8:122–140. doi:10.1111/j.1600-0633.1999.tb00064.x

    Article  Google Scholar 

  • Snedecor GW, Cochran WG (1967) Statistical methods, 6th edn. The Iowa State University Press, Ames, Iowa

    Google Scholar 

  • Sunnucks P (2000) Efficient genetic markers for population biology. Trends Ecol Evol 15:199–203. doi:10.1016/S0169-5347(00)01825-5

    Article  PubMed  Google Scholar 

  • Williams ST, Jara J, Gomez E et al (2002) The marine Indo-West Pacific break: contrasting the resolving power of mitochondrial and nuclear genes. Integr Comp Biol 42:941–952. doi:10.1093/icb/42.5.941

    Article  Google Scholar 

  • Wright S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution Int J Org Evolution 19:395–420. doi:10.2307/2406450

    Google Scholar 

Download references

Acknowledgments

We thank Per Erik Jorde, Barbara Taylor, Robin Waples, and two anonymous reviewers for valuable comments and suggestions. The work was funded by grants from the Swedish Research Council (LL, NR), the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (LL, NR), and the Swedish Environmental Protection Agency (LL).

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Correspondence to Lena C. Larsson.

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Larsson, L.C., Charlier, J., Laikre, L. et al. Statistical power for detecting genetic divergence—organelle versus nuclear markers. Conserv Genet 10, 1255 (2009). https://doi.org/10.1007/s10592-008-9693-z

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