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  • Review Article
  • Published:

The power and promise of population genomics: from genotyping to genome typing

Nature Reviews Genetics volume 4, pages 981–994 (2003)Cite this article

Key Points

  • Genome typing is the simultaneous genotyping of tens to hundreds of marker loci from across the genome in a single or few experiments (for example, by PCR amplification).

  • The population-genomics approach of genome typing followed by testing for outlier loci can identify candidate-selected loci and improve inferences about population demographic and evolutionary history.

  • Outlier loci (for example, loci with excessively high Fst, Fis or homozygosity excess) can severely bias estimates of population parameters (for example, Fst, migration rates (Nm), population size and phylogeny) if they are not identified and removed before parameter estimation.

  • Genome typing is becoming increasingly feasible, even in non-model taxa, thanks to new molecular techniques such as DArT (microarrays), gene-targeted AFLP and expressed sequence tag (EST) databases.

  • Improved statistical methodologies such as 'summary statistics' will facilitate analyses of large population-genomic data sets.

  • Statistical tests and software programs for detecting outlier loci and analysing population-genomic data are becoming increasingly available; nonetheless, the development and validation of tests and software is the greatest impediment to the advancement of population-genomic approaches.

  • The population-genomics paradigm can facilitate biodiversity conservation through rapid biodiversity screening, identifying appropriate populations for translocations (to rescue declining populations) and focusing conservation efforts on preserving processes of evolution (adaptive change).

  • This review focuses largely on non-model organisms, natural populations and biodiversity conservation, and therefore complements recent reviews of population-genomic approaches in medical genomics and pharmacogenomics in humans and model organisms.

Abstract

Population genomics has the potential to improve studies of evolutionary genetics, molecular ecology and conservation biology, by facilitating the identification of adaptive molecular variation and by improving the estimation of important parameters such as population size, migration rates and phylogenetic relationships. There has been much excitement in the recent literature about the identification of adaptive molecular variation using the population-genomic approach. However, the most useful contribution of the genomics model to population genetics will be improving inferences about population demography and evolutionary history.

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Figure 1: Flow chart of the four main steps in the population-genomic approach.
Figure 2: Identifying outlier behaviour.
Figure 3: Examples of outlier behaviour.
Figure 4: The effect of outlier loci on phylogenetic inference.

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References

  1. Black, W. C., Baer, C. F., Antolin, M. F. & DuTeau, N. M. Population genomics: genome-wide sampling of insect populations. Annu. Rev. Entomol. 46, 441–469 (2001). This article defines and lays the foundation for population genomics in terms of separating locus-specific effects versus genome-wide effects. It illustrates population-genomic concepts and principles through hypothetical examples and illustrations.

    CAS  PubMed  Google Scholar 

  2. Gulcher, J. & Stefansson, K. Population genomics: laying the groundwork for genetic disease modelling and targeting. Clin. Chem. Lab. Med. 36, 523–527 (1998).

    CAS  PubMed  Google Scholar 

  3. Goldstein, D. B. & Weale, M. E. Population genomics: linkage disequilibrium holds the key. Curr. Biol. 11, 576–579 (2001).

    Google Scholar 

  4. Jorde, L. B., Watkins, W. S. & Bamshad, M. J. Population genomics: a bridge from evolutionary history to genetic medicine. Hum. Mol. Genet. 10, 2199–2207 (2001).

    CAS  PubMed  Google Scholar 

  5. Gibson, G. & Mackay, T. F. C. Enabling population and quantitative genomics. Genet. Res. 80, 1–6 (2002).

    CAS  PubMed  Google Scholar 

  6. Wilding, C. S., Butlin, R. K. & Grahame, J. Differential gene exchange between parapatric morphs of Littorina saxatilis detected using AFLP markers. J. Evol. Biol. 14, 611–619 (2001). This article indicates that the F st -outlier-detection approach can work surprisingly well if applied to populations that span known selection gradients. It was the first to use AFLP markers, which is encouraging as these are the most readily available markers for genome-wide studies in non-model organisms. One particular strength of this study is the genotyping of replicate sets of populations that span the same kind of selection gradient in different distant geographic locations.

    CAS  Google Scholar 

  7. Albertson, R. C., Markert, J. A., Danley, P. D. & Kocher, T. D. Phylogeny of a rapidly evolving clade: the cichlid fishes of Lake Malawi, East Africa. Proc. Natl Acad. Sci. USA 96, 5107–5110 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hoh, J. & Ott, J. Mathematical multi-locus approaches to localizing complex human trait genes. Nature Rev. Genet. 4, 701–709 (2003).

    CAS  PubMed  Google Scholar 

  9. Wall, J. D. & Pritchard, J. K. Haplotype blocks and linkage disequilibrium in the human genome. Nature Rev. Genet. 4, 587–597 (2003).

    CAS  PubMed  Google Scholar 

  10. Cardon, L. R. & Bell, J. I. Association study designs for complex diseases. Nature Rev. Genet. 2, 91–99 (2001).

    CAS  PubMed  Google Scholar 

  11. Bamshad, M. & Wooding, S. P. Signatures of natural selection in the human genome. Nature Rev. Genet. 4, 99–111 (2003).

    CAS  PubMed  Google Scholar 

  12. Nielsen, R. Statistical tests of selective neutrality in the age of genomics. Heredity 86, 641–647 (2001).

    CAS  PubMed  Google Scholar 

  13. Schlötterer, C. A microsatellite-based multilocus screen for the identification of local selective sweeps. Genetics 160, 753–763 (2002).

    PubMed  PubMed Central  Google Scholar 

  14. Schlötterer, C. Hitchhiking mapping — functional genomics from the population genetics perspective. Trends Genet. 19, 32–38 (2003).

    PubMed  Google Scholar 

  15. Long, A. D. & Langley, C. H. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res. 9, 720–731 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Hardy, O. J. & Vekemans, X. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).

    Google Scholar 

  18. Manel, S., Schwartz, M., Luikart, G. & Taberlet, P. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18, 189–197 (2003). This article summarizes the statistical approaches that are available for relating spatial variation in population-genetic patterns to spatial variation in environmental patterns, This article and the population-genomic concepts discussed here show the feasibility of a 'landscape genomic' approach using association studies between the genome and environments.

    Google Scholar 

  19. Waples, R. S. Genetic methods for estimating the effective size of cetacean populations. Report of the International Whaling Commission (Special Issue) 13, 279–300 (1991).

    Google Scholar 

  20. Yang, Z. Likelihood and Bayes estimation of ancestral population size in hominoids using data from multiple loci. Genetics 162, 1811–1823 (2002).

    PubMed  PubMed Central  Google Scholar 

  21. Wiltshire, T. et al. Genome-wide single-nucleotide polymorphism analysis defining haplotype patterns in mouse. Proc. Natl Acad. Sci. USA 100, 3380–3385 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Endler, J. A. Natural Selection in the Wild (Princeton Univ. Press, Princeton, New Jersey, 1986).

    Google Scholar 

  23. Conner, J. K. How strong in natural selection? Trends Ecol. Evol. 5, 215–217 (2001).

    Google Scholar 

  24. Ungerer, M. C., Linder, C. R. & Rieseberg, L. H. Effects of genetic background on response to selection in experimental populations of Arabidopsis thaliana. Genetics 163, 277–286 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Olson, S. Seeking the signs of selection. Science 298, 1324–1325 (2002).

    CAS  PubMed  Google Scholar 

  26. Storz, J. F. & Nachman, M. W. Natural selection on protein polymorphism in the rodent genus Peromyscus: evidence from interlocus contrasts. Evolution (in the press). This paper quantifies the potential effects of outlier loci on parameter estimation. The authors suggest that outlier loci are rare within data sets but are fairly common across data sets. They also show that the same loci are often outliers across independent data sets (support for selection as the cause of outlier behaviour).

  27. Fay, J. C., Wyckoff, G. J. & Wu, C -I. Testing the neutral theory of molecular evolution with genomic data from Drosophila. Nature 415, 1024–1026 (2002).

    CAS  PubMed  Google Scholar 

  28. Taberlet, P., Waits, L. P. & Luikart, G. Noninvasive genetic sampling: look before you leap. Trends Ecol. Evol. 14, 321–325 (1999).

    Google Scholar 

  29. Flint, J. et al. Minisatellite mutational processes reduce F-st estimates. Hum. Genet. 105, 567–576 (1999).

    CAS  PubMed  Google Scholar 

  30. Sunnucks, P. Efficient genetic markers for population biology. Trends Ecol. Evol. 15, 199–203 (2000).

    CAS  PubMed  Google Scholar 

  31. Ewens, W. J. The sampling theory of selectively neutral alleles. Theoret. Popul. Genet. 3, 87–112 (1972).

    CAS  Google Scholar 

  32. Watterson, G. A. The homozygosity test of neutrality. Genetics 88, 405–417 (1978).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Hedrick, P. W. in Genetics, Demography, and Viability of Fragmented Populations (eds Young, A. & Clarke, G.) 113–125 (Cambridge Univ. Press, Cambridge, UK, 2000).

    Google Scholar 

  34. Beaumont, M. A. & Nichols, R. A. Evaluating loci for use in the genetic analysis of population structure. Proc. R. Soc. Lond. B 263, 1619–1626 (1996). This paper improves and largely revives the Lewontin and Krakauer F st -outlier approach (reference 38) as a viable method for detecting loci that are candidate selected/adaptive. Real and simulated data (from non-equilibrium populations and various migration patterns) indicate that outliers can be reliably detected. A software program is made freely available to conduct the F st -outlier tests.

    Google Scholar 

  35. Akey, J. M., Zhang, G., Zhang, K., Jin, L. & Shriver, M. D. Interrogating a high-density SNP map for signatures of natural selection. Genome Res. 12, 1805–1814 (2002). This study describes the most extensive genome-wide sampling that has been done so far, which provides empirical distributions of F st for different genome regions (X chromosome, exons, introns and non-coding regions).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Payseur, B. A., Cutter, A. D. & Nachman, M. W. Searching for evidence of positive selection in the human genome using patterns of microsatellite variability. Mol. Biol. Evol. 7, 1143–1153 (2002).

    Google Scholar 

  37. Storz, J. F. & Beaumont, M. A. 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 (2002). The first extension of the single-locus homozygosity-excess test (by Ewens–Watterson, references 31 and 32) for use in a genome-wide approach.

    CAS  PubMed  Google Scholar 

  38. Lewontin, R. C. & Krakauer, J. K. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics 74, 175–195 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Vitalis, R., Dawson, K. & Boursot, P. Interpretation of variation across marker loci as evidence of selection. Genetics 158, 1811–1823 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Baer, C. F. Among-locus variation in Fst: fish, allozymes and the Lewontin–Krakauer test revisited. Genetics 152, 653–659 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Arnaud-Haond, S., Bonhomme, F. & Blanc, F. Large discrepancies in differentiation of allozymes, nuclear and mitochondrial DNA loci in recently founded Pacific populations of the pearl oyster Pinctada margeritifera. J. Evol. Biol. 16, 388–398 (2003).

    CAS  PubMed  Google Scholar 

  42. Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025–2035 (2002).

    PubMed  PubMed Central  Google Scholar 

  43. Landry, P. A., Koskinen, M. T. & Primmer, C. R. Deriving evolutionary relationships among populations using microsatellites and (δ-μ)2: all loci are equal, but some are more equal than others. Genetics 161, 1339–1347 (2002).

    PubMed  PubMed Central  Google Scholar 

  44. Allendorf, F. W. & Seeb, L. W. Concordance of genetic divergence among sockeye salmon populations at allozyme, nuclear DNA, and mitochondrial DNA markers. Evolution 54, 640–651 (2000). This article indicates that outlier loci, although rare within data sets, might be common across large data sets, and that outliers occur with any type of molecular marker. It emphasizes that it is more important to genotype many markers (and test for outliers) than to use a certain marker type when computing population-genetic parameters.

    CAS  PubMed  Google Scholar 

  45. Pogson, G. H., Mesa, K. A. & Boutilier, R. G. Genetic population structure and gene flow in the Atlantic cod: a comparison of allozyme and nuclear RFLP loci. Genetics 139, 375–385 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Reich, D. E. et al. Linkage disequilibrium in the human genome. Nature 411, 199–204 (2001).

    CAS  PubMed  Google Scholar 

  47. Carlson, C. S. et al. Additional SNPs and linkage-disequilibrium analysis in whole-genome association studies in humans. Nature Genet 33, 518–521 (2003).

    CAS  PubMed  Google Scholar 

  48. Whitlock, M. C. & McCauley, D. E. Indirect measures of gene flow and migration: FST ≠ (4Nm + 1). Heredity 82, 117–125 (1999).

    PubMed  Google Scholar 

  49. Nachman, M. W. Single nucleotide polymorphism and recombination rate in humans. Trends Genet. 17, 481–485 (2001).

    CAS  PubMed  Google Scholar 

  50. Hughes, A. L. Adaptive Evolution of Genes and Genomes (Oxford Univ. Press, New York and Oxford, 1999).

    Google Scholar 

  51. Wu, C -I. The genic view of the process of speciation. J. Evol. Biol. 14, 851–865 (2001).

    Google Scholar 

  52. Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 51, 238–254 (2002).

    PubMed  Google Scholar 

  53. Crandall, K. A., Bininda-Emonds, O. R. P., Mace, G. M. & Wayne, R. K. Considering evolutionary processes in conservation biology. Trends Ecol. Evol. 15, 290–295 (2000).

    CAS  PubMed  Google Scholar 

  54. Vrijenhoek, R. C. & Leberg, L. P. Let's not throw the baby out with the bathwater: a comment on management for MHC diversity in captive populations. Cons. Biol. 5, 252–253 (1991).

    Google Scholar 

  55. Lacy, R. C. Should we select genetic alleles in our conservation breeding programs? Zoo Biol. 19, 279–282 (2000).

    Google Scholar 

  56. Wilson, E. O. The encyclopaedia of life. Trends Ecol. Evol. 18, 77–80 (2003).

    Google Scholar 

  57. Ronquist, F. & Gardenfors, U. Taxonomy and biodiversity inventories: time to deliver. Trends Ecol. Evol. 18, 269–270 (2003).

    Google Scholar 

  58. Baker, S. C., Dalebout, M. L., Lavery, S. & Ross, H. A. DNA-surveillance: applied molecular taxonomy for species conservation and discovery. Trends Ecol. Evol. 18, 271–272 (2003).

    Google Scholar 

  59. Blaxter, M. & Floyd, R. Molecular taxonomics for biodiversity surveys: already a reality. Trends Ecol. Evol. 18, 268–269 (2003).

    Google Scholar 

  60. DeLong, E. F. Microbial population genomics and ecology. Curr. Opin. Microbiol. 5, 520–524 (2002).

    PubMed  Google Scholar 

  61. Kohn, M. H. et al. Locus-specific genetic differentiation among warfarin resistant rat populations. Genetics 164, 1055–1070 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Kohn, M. H., Pelz, H -J. & Wayne, R. K. Natural selection mapping of the warfarin-resistance gene. Proc. Natl Acad. Sci. USA 97, 7911–7915 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure II. Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Vos, P. et al. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 23, 4407–4414 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Jaccoud, D., Peng, K., Feinstein, D. & Kilian, A. Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res. 29, 25 (2001). This paper described the DArT approach, which promises to increase the number of RFLP-like markers that can be genotyped in a single PCR by an order of magnitude. The technique uses microarray hybridization, which increases speed and reduces cost.

    Google Scholar 

  66. Young, W. P., Schupp, J. M. & Keim, P. DNA methylation and AFLP marker distribution in soybean. Theor. Appl. Genet. 99, 785–792 (1999).

    CAS  Google Scholar 

  67. Lindner, K. R. et al. Gene-centromere mapping of 312 loci in pink salmon by half-tetrad analysis. Genome 43, 538–549 (2000).

    CAS  PubMed  Google Scholar 

  68. Skot, L., Sackville, H., Mizen, S., Chorlton, K. H. & Thomas, I. D. Molecular genecology of temperature response in Lolium perenne. 2. association of AFLP markers with ecogeography. Mol. Ecol. 11, 1865–1875 (2002).

    CAS  PubMed  Google Scholar 

  69. Wang, Z., Baker, A. J., Hill, G. & Edwards, S. V. Reconciling actual and inferred population histories in the house finch (Carpodacus mexicanus) by AFLP analysis. Evolution (in the press).

  70. van der Wurff, A., Chan, Y., van Straalen, N. & Schouten, J. TE–AFLP: combining rapidity and robustness in DNA fingerprinting. Nucleic Acids Res. 28, 105 (2000).

    Google Scholar 

  71. van Tienderen, P., de Haan, A., van der Linden, C . & Vosman, B. Biodiversity assessment using markers for ecologically important traits. Trends Ecol. Evol. 17, 577–582 (2002). Gene-targeted AFLP and other methods for identifying adaptive genes (mainly in agricultural species) are described in this paper.

    Google Scholar 

  72. Waugh, R. et al. Genetic distribution of Bare-1-like retrotransposable elements in the barley genome revealed by sequence-specific amplification polymorphisms (S-SAP). Mol. Gen. Genet. 253, 687–694 (1997).

    CAS  PubMed  Google Scholar 

  73. Ramensky, V., Bork, P. & Sunyaev, S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 30, 3894–3900 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Batley, J., Barker, G., O'Sullivan, H., Edwards, K. J. & Edwards, D. Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tags. Plant Physiol. 132, 84–91 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Davey, G. C., Caplice, N. C., Martin, S. A. & Powell, R. A survey of genes expressed in the Atlantic salmon as identified by expressed sequence tags. Gene 363, 121–130 (2001).

    Google Scholar 

  76. Everitt, R. et al. RED: the analysis, management of and dissemination of expressed sequence tags. Bioinformatics 18, 1692–1693 (2002).

    CAS  PubMed  Google Scholar 

  77. Chen, J. W. et al. A microsphere-based assay for multiplexed single nucleotide polymorphism analysis using single base chain extension. Genome Res. 10, 549–557 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Kennedy, G. et al. Large-scale genotyping of complex DNA. Nature Biotechnol. 2, 1233–1237 (2003).

    Google Scholar 

  79. Kuhner, M. K., Beerli, P., Yamato, J. & Felsenstein, J. Usefulness of single nucleotide polymorphism data for estimating population parameters. Genetics 156, 439–447 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Wakeley, J., Nielsen, R., Liu-Cordero, S. N. & Ardlie, K. The discovery of single-nucleotide polymorphisms — and inferences about human demographic history. J. Hum. Genet. 69, 1332–1347 (2001).

    CAS  Google Scholar 

  81. Brumfield, R. T., Beerli, P., Nickerson, D. A. & Edwards, S. V. The utility of single nucleotide polymorphisms in inferences of population history. Trends Ecol. Evol. 18, 249–256 (2003).

    Google Scholar 

  82. Akey, et al. The effect of single nucleotide polymorphism identification strategies on estimates of linkage disequilibrium. Mol. Biol. Evol. 20, 232–242 (2003).

    CAS  PubMed  Google Scholar 

  83. Nielsen, R. & Signorovitch, J. Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium. Theor. Popul. Biol. 63, 245–255 (2003).

    PubMed  Google Scholar 

  84. Clark, A. et al. Linkage disequilibrium and inference of ancestral recombination in 538 single-nucleotide polymorphism clusters across the human genome. Am. J. Hum. Genet. 73, 285–300 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Schmid, K. et al. Large-scale identification and analysis of genome-wide single-nucleotide polymorphisms for mapping in Arabidopsis thaliana. Genome Res. 13,1250–1257 (2003).

    PubMed  PubMed Central  Google Scholar 

  86. Paetkau, D., Slade, R., Burden, M. & Estoup, A. Direct, real-time estimation of migration rates using assignment methods: a simulation-based exploration of accuracy and power. Mol. Ecol. (in the press).

  87. Banks, M. A., Eichert, W. & Olsen, J. B. Which genetic loci have greater population assignment power? Bioinformatics 19, 1436–1438 (2003).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Manel, S., Berthier, P. & Luikart, G. Detecting wildlife poaching: identifying the origin of individuals using Bayesian assignment tests and multi-locus genotypes. Cons. Biol. 16, 650–657 (2002).

    Google Scholar 

  90. Maudet, C. & Taberlet, P. Holstein's milk detection in cheeses inferred from melanocortin receptor 1 (MC1R) gene polymorphism. J. Dairy Sci. 85, 707–715 (2002).

    CAS  PubMed  Google Scholar 

  91. Pletcher, S. D. & Stumpf P. H. Population genomics: ageing by association. Curr. Biol. 12, 328–330 (2002). This study is an example of how genes cause similar fitness effects in different taxa (humans and mice). This indicates that genes with known adaptive/fitness effects from one species can be used in another species as 'strong candidate genes' in population-genomics association studies.

    Google Scholar 

  92. Yeh, F. C., Yang, R -C., Boyle, T. B. J., Ye, Z -H. & Mao, J. X. POPGENE, the user-friendly shareware for population genetic analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Canada. [online], (cited 20 October 2003), <http://www.ualberta.ca/~fyeh/faq.htm> (1997).

  93. Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes. Genetics 131, 479–491 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Lancaster, A., Nelson, M. P., Single, R. M., Meyer, D. & Thomson, G. in Pac. Symp. Biocomput. 2003 (eds Altman, R. B. et al.) 514–525 (World Scientific, Singapore, 2002).

    Google Scholar 

  95. Cooper, G. et al. An empirical estimate of the δ-μ genetic distance for 213 human microsatellite markers. Am. J. Hum. Genet. 6, 1125–1133 (1999).

    Google Scholar 

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Acknowledgements

We thank F. Allendorf, M. Beaumont, T. Mitchell-Olds, K. Schmidt, P. Sunnucks and three anonymous reviewers for providing references, discussions and helpful comments. W. Amos and J. W. Grahame provided unpublished data and correspondence. S.J. and D.T. were funded by the United States National Science Foundation. G.L, P.R.E and P.T. were supported in part by the European Union ('Econogene' project).

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  1. Laboratoire d'Ecologie Alpine, Génomique des Populations et Biodiversit´, CNRS UMR 5553, Université Joseph Fourier, B.P. 53, Grenoble, F-38041 Cedex 9, France

    Gordon Luikart, Phillip R. England, David Tallmon, Steve Jordan & Pierre Taberlet

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Related links

Related links

Databases

LocusLink

MC1R

Further information

DetSel software (Renaud Vitalis's web site)

Fdist software (Mark Beaumont's web site)

GeneClass2

Illumina

LECA web site

Neutrallelix

PyPop software

Zhenshan Wang's web site

Glossary

RANDOM GENETIC DRIFT

Random fluctuations in allele frequencies between generations owing to sampling effects. It increases as the effective population size decreases.

GENE FLOW

The movement of genes among populations. Often expressed as the proportion of gene copies (or breeding individuals) that are immigrants from a different population.

BOTTLENECK

A marked reduction in population size that often results in the loss of genetic variation and more frequent matings among closely related individuals.

MICROEVOLUTION

Evolutionary processes or changes over relatively short time periods — such as change in allele frequencies, genotypic composition or gene expression — within or between populations.

F ST

The most widely used index of genetic divergence between populations. A standardized measure of the distribution of genetic variation between populations on a scale between 0 (identical allele frequencies in populations) and 1 (populations fixed for different alleles).

NEUTRAL LOCI

Loci that are not evolving directly in response to selection, the dynamics of which are controlled mainly by genetic drift and migration. These loci can, however, be influenced by selection on nearby (linked) loci.

OUTLIER LOCI

Genome locations (or markers or base pairs) that show behaviour or patterns of variation that are extremely divergent from the rest of the genome (locus-specific effects), as revealed by simulations or statistical tests.

SELECTION SIGNATURE

The molecular footprint of a selection event from the recent past (for example, an excess of rare alleles at a locus relative to the abundance of rare alleles at loci across the rest of the genome).

POPULATION PARAMETERS

Parameters that characterize populations such as gene flow, migration rates, effective size, change in size, relatedness and phylogeny.

SELECTIVE SWEEP

The increase in frequency of an allele (and closely linked chromosomal segments) that is caused by selection for the allele. Sweeps initially reduce variation and subsequently lead to a local excess of rare alleles (homozygosity excess) as new unique mutations accumulate.

AMPLIFIED FRAGMENT LENGTH POLYMORPHISM

(AFLP). A DNA fragment-length polymorphism that is revealed by a PCR-based DNA fingerprinting technique that generates dozens of polymorphic marker bands (presence or absence of a restriction enzyme site) in a single gel lane. The marker bands are usually dominant in that we generally cannot see the difference between a heterozygote and homozygote.

EFFECTIVE POPULATION SIZE

(Ne). Roughly the number of breeding individuals that produce offspring that live to reproductive age. It influences the rate of loss of genetic variation, the efficiency of natural selection, and the accumulation of beneficial and deleterious mutations. It is frequently much smaller than the number of individuals in a population.

GENOME TYPING

The simultaneous genotyping of hundreds of loci from across the genome, which ideally includes mapped loci and different classes of loci such as allozymes, microsatellites and AFLPs, or synonymous (non-coding) and non-synonymous nucleotide polymorphisms.

SEMI-MODEL SPECIES

Species that are not as extensively studied as classical model systems such as mice, Arabidopsis and Drosophila, but for which large data sets and effective genomic tools are beginning to be developed.

EXPRESSED SEQUENCE TAGS

(ESTs). Short DNA sequences (several hundred base pairs) that are produced by reverse transcription of mRNA into DNA. ESTs are cDNAs that consist of exons and the sequences that flank exons. The sequencing of ESTs allows rapid identification ('tagging') of genes and can expedite DNA marker (SNP) development in coding genes.

COMPARATIVE ANCHOR-TAG SEQUENCES

(CATS). Exon sequences that are conserved across taxa allowing the design of primers that amplify in divergent species (for example, across mammal orders). CATS-like primers speed the discovery of SNPs (in exons or introns) and comparative genome mapping across taxa.

EXON-PRIMED INTRON-CROSSING PCR

(EPIC-PCR). EPIC primers are designed in conserved exons and amplify intron sequences that are generally more polymorphic than exons, which are therefore useful for the development of SNP or RFLP markers.

HAPLOTYPE BLOCKS

Long stretches (tens of megabases) along a chromosome that have low recombination rates (and relatively few haplotypes). Adjacent blocks are separated by recombination hot spots (short regions with high recombination rates).

HARDY–WEINBERG

A law or model in which allele and genotype frequencies will reach equilibrium in one generation and remain constant from generation to generation in large random-mating populations with no mutation, migration or selection.

HOMOZYGOSITY EXCESS

A higher Hardy–Weinberg equilibrium homozygosity than that which is expected in a population at mutation–drift equilibrium with the same observed number of alleles. This is not an excess of homozygotes (deviation from Hardy–Weinberg proportions).

SELECTION COEFFICIENTS

The average proportional reduction in fitness of one genotype relative to another owing to selection (designated by 's').

ADMIXED

(Hybridized). An admixed population contains hybrids or offspring of individuals originating from genetically divergent parental populations.

CLINE

A gradient of variation across space. It usually refers to increased differences among populations in the frequency of an allele or trait with increased geographic distance.

EMPIRICAL DISTRIBUTION

The distribution of a test statistic (for example, Fst or Fis) that is computed from observed data obtained from hundreds of loci sampled genome-wide.

NULL DISTRIBUTION

(Neutral distribution). The distribution (or range) of values across which we expect to observe the value of the test statistic if the null hypothesis is true (for example, neutrality). When conducting a standard t-test, t is the test statistic and the null distribution is the normal (Gaussian) distribution with t degrees of freedom.

SUMMARY STATISTIC

A parameter estimate (such as Fst or Fis) that quantifies attributes of the data sampled from a population of interest.

BAYESIAN

A framework of statistical inference in which previous beliefs (or data) and likelihoods are combined to estimate a parameter of interest given the observed data.

LIKELIHOOD-BASED TEST

Statistical tests that consider how likely the data are given an assumed model.

MARKOV CHAIN MONTE CARLO

(MCMC). A simulation-based computational technique for the numerical calculation of likelihoods.

MOLECULAR ADAPTATION

Genetic change (for example, allele frequency shift or amino-acid substitution) in response to natural selection.

PHYLOGEOGRAPHY

The study of the geographic distribution of phylogenetic lineages, usually within species and to reconstruct the origins and diffusion of lineages.

MORPHOTYPES

Distinctive phenotypes. Organisms that are classified together on the basis of similar physical features without knowledge of their genetic relationships.

COALESCENT

Relating to the mathematical and statistical properties of genealogies. A modelling framework in which two DNA sequence lineages converge in a common ancestral sequence, going backwards in time.

BOOTSTRAP

A statistical approach that is often used to generate confidence intervals (measures of variation) around parameter estimates in which the data are re-sampled repeatedly (with replacement) using computer Monte Carlo simulations.

TEST STATISTIC

The summary value (often a summary statistic) of a data set that is compared with a statistical distribution to determine whether the data set differs from that expected under a null hypothesis.

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Luikart, G., England, P., Tallmon, D. et al. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet 4, 981–994 (2003). https://doi.org/10.1038/nrg1226

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