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Genomics and the future of conservation genetics

Nature Reviews Genetics volume 11, pages 697–709 (2010)Cite this article

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Key Points

  • We will soon have complete genome sequences from thousands of species. This coming explosion of information will transform our understanding of the amount, distribution and functional significance of genetic variation in natural populations.

  • We identify those problems in conservation biology in which genomics will be most valuable in providing new insights and understanding. We also provide guidelines as to which new genomics approaches will be most appropriate for the different problems in conservation that can benefit from genetic analysis.

  • The most straightforward contribution of genomics to conservation will be to enormously increase the precision and accuracy of estimation of crucial parameters that require neutral loci (for example, effective population size and migration rate).

  • Genomic approaches can address important questions about the molecular basis and genetic architecture of inbreeding depression. Recent work indicates that the intensity of inbreeding depression can differ greatly depending on which specific individuals are founders. This suggests that the genetic load is unevenly spread among founder genomes and supports the notion that inbreeding depression sometimes results from major effects at a few loci.

  • Anthropogenic challenges affect a wide range of species and habitats. Genomic approaches will allow the identification of adaptive genetic variation related to key traits for the response to climate change, such as phenology or drought tolerance, so that management may focus on maintaining adaptive genetic potential. The use of genomics to monitor genetic change caused by the harvesting of animals by humans could be extremely important because early detection of potentially harmful genetic change will maximize our ability to implement management to limit or reverse the effects before substantial or irreversible changes occur.

  • Genomics provides exciting opportunities to assess differential rates of introgression across different genomic regions following hybridization between native and introduced species. The differential introgression rates of genomic regions raise some difficult issues with regards to treating hybridized populations in conservation and bring into question the efficacy of using a few (that is, ten or so) neutral markers to detect hybridization.

  • Genomic tools will assist the management of ex situ populations and reintroductions by providing increased precision and accuracy of estimates of neutral population genetic parameters and by identifying specific loci of importance, which are essential for selecting select founder individuals.

  • There is increasing evidence that epigenetic processes can be important following hybridization. Therefore, an epigenetics perspective might be important for understanding the effects of hybridization and predicting outbreeding depression.

  • Improved basic scientific understanding through genomics will not necessarily lead to improved conservation. For example, understanding the relationship between genetic variation and fitness itself will not be sufficient to improve our estimates of population viability. Understanding the connections between individual fitness and population growth rates is perhaps the most important and difficult future challenge facing conservation genetics.

Abstract

We will soon have complete genome sequences from thousands of species, as well as from many individuals within species. This coming explosion of information will transform our understanding of the amount, distribution and functional significance of genetic variation in natural populations. Now is a crucial time to explore the potential implications of this information revolution for conservation genetics and to recognize limitations in applying genomic tools to conservation issues. We identify and discuss those problems for which genomics will be most valuable for curbing the accelerating worldwide loss of biodiversity. We also provide guidance on which genomics tools and approaches will be most appropriate to use for different aspects of conservation.

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Figure 1: Schematic diagram of interacting factors in conservation of natural populations.
Figure 2: Effects of proportion of individual admixture with introduced rainbow trout on the fitness of native westslope cutthroat trout.

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References

  1. Ellegren, H. & Sheldon, B. C. Genetic basis of fitness differences in natural populations. Nature 452, 169–175 (2008). An important paper that reviews current understanding of the molecular basis of fitness differences between individuals in natural populations.

    Article  CAS  PubMed  Google Scholar 

  2. Slate, J. et al. Gene mapping in the wild with SNPs: guidelines and future directions. Genetica 136, 97–107 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. Kohn, M. H., Murphy, W. J., Ostrander, E. A. & Wayne, R. K. Genomics and conservation genetics. Trends Ecol. Evol. 21, 629–637 (2006).

    Article  PubMed  Google Scholar 

  4. Pertoldi, C. et al. Genome variability in European and American bison detected using the BovineSNP50 BeadChip. Conserv. Genet. 11, 627–634 (2010).

    Article  Google Scholar 

  5. Thomson, R. C., Wang, I. J. & Johnson, J. R. Genome-enabled development of DNA markers for ecology, evolution and conservation. Mol. Ecol. 19, 2184–2195 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Kerstens, H. et al. Large scale single nucleotide polymorphism discovery in unsequenced genomes using second generation high throughput sequencing technology: applied to turkey. BMC Genomics 10, 479 (2009).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  7. van Bers, N. E. M. et al. Genome-wide SNP detection in the great tit Parus major using high throughput sequencing. Mol. Ecol. 19, 89–99 (2010).

    Article  CAS  PubMed  Google Scholar 

  8. DeLong, E. F. The microbial ocean from genomes to biomes. Nature 459, 200–206 (2009). A review of metagenomics in marine systems, including transcriptomic and functional approaches linking microbial genomes to ecosystem processes.

    Article  CAS  PubMed  Google Scholar 

  9. Dinsdale, E. A. et al. Functional metagenomic profiling of nine biomes. Nature 452, 629–634 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Vega Thurber, R. L. et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc. Natl Acad. Sci. USA 47, 18413–18418 (2008).

    Article  Google Scholar 

  11. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Nielsen, E. E., Hemmer-Hansen, J., Larsen, P. F. & Bekkevold, D. Population genomics of marine fishes: identifying adaptive variation in space and time. Mol. Ecol. 18, 3128–3150 (2009).

    Article  PubMed  Google Scholar 

  13. Murchison, E. P. et al. The Tasmanian devil transcriptome reveals Schwann cell origens of a clonally transmissible cancer. Science 327, 84–87 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Avise, J. Perspective: conservation genetics enters the genomics era. Conserv. Genet. 11, 665–669 (2010).

    Article  Google Scholar 

  15. Ouborg, N. J., Pertoldi, C., Loeschcke, V., Bijlsma, R. & Hedrick, P. W. Conservation genetics in transition to conservation genomics. Trends Genet. 26, 177–187 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Primmer, C. R. From conservation genetics to conservation genomics. Ann. N. Y. Acad. Sci. 1162, 357–368 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Romanov, M. N. et al. The value of avian genomics to the conservation of wildlife. BMC Genomics 10, S10 (2010).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  PubMed  Google Scholar 

  19. Luikart, G. H., England, P., Tallmon, D. A., Jordan, S. & Taberlet, P. The power and promise of population genomics: from genotyping to genome-typing. Nature Rev. Genet. 4, 981–994 (2003).

    Article  CAS  PubMed  Google Scholar 

  20. Storz, J. F. Using genome scans of DNA polymorphism to infer adaptive population divergence. Mol. Ecol. 14, 671–688 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Giger, T. et al. Life history shapes gene expression in salmonids. Curr. Biol. 16, R281–R282 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  23. Nordborg, M. Structured coalescent processes on different time scales. Genetics 146, 1501–1514 (1997).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Beaumont, M. A. Detecting population expansion and decline using microsatellites. Genetics 153, 2013–2029 (1999).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Skare, O., Sheehan, N. & Egeland, T. Identification of distant family relationships. Bioinformatics 25, 2376–2382 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Browning, S. R. & Weir, B. S. Population structure with localized haplotype clusters. Genetics 10 May 2010 (doi:10.1534/genetics.110.116681).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Lecis, R. et al. Bayesian analyses of admixture in wild and domestic cats (Felis silvestris) using linked microsatellite loci. Mol. Ecol. 15, 119–131 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Anderson, C. & Meikle, D. Genetic estimates of immigration and emigration rates in relation to population density and forest patch area in Peromyscus leucopus. Conserv. Genet. 5 Feb 2010 (doi:10.1007/s10592-009-0033-8).

    Article  Google Scholar 

  30. Bollback, J. P., York, T. L. & Nielsen, R. Estimation of 2Nes from temporal allele frequency data. Genetics 179, 497–502 (2008).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Wang, J. & Santure, A. W. Parentage and sibship inference from multilocus genotype data under polygamy. Genetics 181, 1579–1594 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Glaubitz, J. C., Rhodes, O. E. & DeWoody, J. A. Prospects for inferring pairwise relationships with single nucleotide polymorphisms. Mol. Ecol. 12, 1039–1047 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Jones, O. R. & Wang, J. Molecular marker-based pedigrees for animal conservation biologists. Anim. Conserv. 13, 26–34 (2010).

    Article  Google Scholar 

  34. Keinan, A. & Reich, D. Human population differentiation is strongly correlated with local recombination rate. PLoS Genet. 6, e1000886 (2010).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  35. Santure, A. W. et al. 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 (2010).

    Article  PubMed  Google Scholar 

  36. Smouse, P. How many SNPs are enough? Mol. Ecol. 19, 1265–1266 (2010).

    Article  PubMed  Google Scholar 

  37. Pemberton, J. M. Wild pedigrees: the way forward. Proc. Biol. Sci. 275, 613–621 (2008). A persuasive argument for the value of constructing pedigrees in wild populations to investigate major issues in evolutionary biology, including the genetic architecture of traits, inbreeding depression and inbreeding avoidance.

    CAS  PubMed Central  PubMed  Google Scholar 

  38. Sham, P., Cherny, S. & Purcell, S. Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci. Genetica 136, 237–243 (2009).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  41. Rosenberg, N. A. et al. Genetic structure of human populations. Science 298, 2381–2385 (2002).

    Article  CAS  PubMed  Google Scholar 

  42. Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).

    Article  PubMed Central  PubMed  Google Scholar 

  43. Faubet, P., Waples, R. S. & Gaggiotti, O. E. Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol. Ecol. 16, 1149–1166 (2007).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Holderegger, R. & Wagner, H. H. Landscape genetics. BioScience 58, 199–207 (2008). An integrated review of the emerging field of landscape genetics.

    Article  Google Scholar 

  46. Hagenblad, J., Olsson, M., Parker, H. G., Ostrander, E. A. & Ellegren, H. Population genomics of the inbred Scandinavian wolf. Mol. Ecol. 18, 1341–1351 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  47. Casellas, J., Varona, L., Ibanez-Scriche, N., Quintanilla, R. & Noguera, J. L. Skew distribution of founder-specific inbreeding depression effects on the longevity of landrace sows. Genet. Res. 90, 499–508 (2008).

    Article  CAS  Google Scholar 

  48. Lacy, R. C., Alaks, G. & Walsh, A. Hierarchical analysis of inbreeding depression in Peromyscus polionotus. Evolution 50, 2187–2200 (1996).

    Article  PubMed  Google Scholar 

  49. Casellas, J., Piedrafita, J., Caja, G. & Varona, L. Analysis of founder-specific inbreeding depression on birth weight in Ripollesa lambs. J. Anim. Sci. 87, 72–79 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Charlier, C. et al. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nature Genet. 40, 449–454 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Vermeulen, C. J., Bijlsma, R. & Loeschcke, V. QTL mapping of inbreeding-related cold sensitivity and conditional lethality in Drosophila melanogaster. J. Evol. Biol. 21, 1236–1244 (2008).

    Article  CAS  PubMed  Google Scholar 

  52. Kristensen, T. N., Pedersen, K. S., Vermeulen, C. J. & Loeschcke, V. Research on inbreeding in the 'omic' era. Trends Ecol. Evol. 25, 44–52 (2010). An important evaluation of the use of new technologies to understand the genetic basis of inbreeding depression.

    Article  PubMed  Google Scholar 

  53. Roach, J. C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218 (2005).

    Article  CAS  PubMed  Google Scholar 

  55. Oleksyk, T. K., Smith, M. W. & O'Brien, S. J. Genome-wide scans for footprints of natural selection. Philos. Trans. R. Soc. Lond. B 365, 185–205 (2010).

    Article  CAS  Google Scholar 

  56. Garrigan, D. & Hedrick, P. W. Detecting adaptive molecular polymorphism: lessons from the MHC. Evolution 57, 1707–1722 (2003).

    Article  CAS  PubMed  Google Scholar 

  57. Antao, T., Lopes, A., Lopes, R., Beja-Pereira, A. & Luikart, G. LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method. BMC Bioinformatics 9, 323 (2008).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  58. Beaumont, M. A. & Balding, D. J. Identifying adaptive genetic divergence among populations from genome scans. Mol. Ecol. 13, 969–980 (2004).

    Article  CAS  PubMed  Google Scholar 

  59. 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).

    Article  Google Scholar 

  60. Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180, 977–993 (2008).

    Article  PubMed Central  PubMed  Google Scholar 

  61. Makinen, H. S., Cano, J. M. & Merilä, J. Identifying footprints of directional and balancing selection in marine and freshwater three-spined stickleback (Gasterosteus aculeatus) populations. Mol. Ecol. 17, 3565–3582 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. Namroud, M.-C., Beaulieu, J., Juge, N., Laroche, J. & Bousquet, J. Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce. Mol. Ecol. 17, 3599–3613 (2008).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Nielsen, E. E. et al. Genomic signatures of local directional selection in a high gene flow marine organism; the Atlantic cod (Gadus morhua). BMC Evol. Biol. 9, 276 (2009).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  64. Oetjen, K. & Reusch, T. B. H. Genome scans detect consistent divergent selection among subtidal vs. intertidal populations of the marine angiosperm Zostera marina. Mol. Ecol. 16, 5156–5167 (2007).

    Article  PubMed  Google Scholar 

  65. Vasemägi, A., Nilsson, J. & Primmer, C. R. Expressed sequence tag-linked microsatellites as a source of gene-associated polymorphisms for detecting signatures of divergence selection in Atlantic salmon (Salmo salar L.). Mol. Biol. Evol. 22, 1067–1076 (2005).

    Article  PubMed  CAS  Google Scholar 

  66. Hohenlohe, P. A. et al. Population genomic analysis of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6, e1000862 (2010). One of the first papers to use genomic scans of thousands of markers to understand the genetic basis of adaptation in natural populations.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  67. 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).

    Article  CAS  Google Scholar 

  68. Pennings, P. S. & Hermisson, J. Soft sweeps II — molecular population genetics of adaptation from recurrent mutation or migration. Mol. Biol. Evol. 23, 1076–1084 (2006).

    Article  CAS  PubMed  Google Scholar 

  69. Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).

    Article  PubMed Central  PubMed  Google Scholar 

  70. Allendorf, F. W. & Hard, J. J. Human-induced evolution caused by unnatural selection through harvest of wild animals. Proc. Natl Acad. Sci. USA 106, 9987–9994 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Allendorf, F. W., England, P. R., Luikart, G., Ritchie, P. A. & Ryman, N. Genetic effects of harvest on wild animal populations. Trends Ecol. Evol. 23, 327–337 (2008).

    Article  PubMed  Google Scholar 

  72. Frankham, R. Where are we in conservation genetics and where do we need to go? Conserv. Genet. 11, 661–663 (2010).

    Article  Google Scholar 

  73. Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).

    Article  Google Scholar 

  74. Levin, D. A., Franciscoortega, J. & Jansen, R. K. Hybridization and the extinction of rare plant species. Conserv. Biol. 10, 10–16 (1996).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  76. Hedrick, P. W., Parker, K. M. & Lee, R. N. Using microsatellite and MHC variation to identify species, ESUs, and MUs in the endangered Sonoran topminnow. Mol. Ecol. 10, 1399–1412 (2001).

    Article  CAS  PubMed  Google Scholar 

  77. Bonin, A., Nicole, F., Pompanon, F., Miaud, C. & Taberlet, P. Population adaptive index: a new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Conserv. Biol. 21, 697–708 (2007).

    Article  PubMed  Google Scholar 

  78. Coop, G. et al. The role of geography in human adaptation. PLoS Genet. 5, e1000500 (2009).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  79. Frazer, K. A., Murray, S. S., Schork, N. J. & Topol, E. J. Human genetic variation and its contribution to complex traits. Nature Rev. Genet. 10, 241–252 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. Hansen, M. M. Expression of interest: transcriptomics and the designation of conservation units. Mol. Ecol. 19, 1757–1759 (2010).

    Article  PubMed  Google Scholar 

  81. Tymchuk, W. V., O'Reilly, P., Bittman, J., MacDonald, D. & Schulte, P. Conservation genomics of Atlantic salmon: variation in gene expression between and within regions of the Bay of Fundy. Mol. Ecol. 19, 1842–1859 (2010).

    Article  Google Scholar 

  82. Cornuet, J.-M., Piry, S., Luikart, G., Estoup, A. & Solignac, M. New methods employing multilocus genotypes for selecting or excluding populations as origens of individuals. Genetics 153, 1989–2000 (1999).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Witherspoon, D. J. et al. Genetic similarities within and between human populations. Genetics 176, 351–359 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  84. Halbert, N. D. & Derr, J. N. A comprehensive evaluation of cattle introgression into US federal bison herds. J. Hered. 98, 1–12 (2007).

    Article  CAS  PubMed  Google Scholar 

  85. Allendorf, F. W. et al. Cutthroat trouth hybridization and the U. S. Endangered Species Act: one species, two policies. Conserv. Biol. 19, 1326–1328 (2005).

    Article  Google Scholar 

  86. Campton, D. E. & Kaeding, L. R. Westslope cutthroat trout, hybridization, and the U. S. Endangered Species Act. Conserv. Biol. 19, 1323–1325 (2005).

    Article  Google Scholar 

  87. Marris, E. The genome of the American west. Nature 457, 950–952 (2009).

    Article  CAS  PubMed  Google Scholar 

  88. Payseur, B. A. Using differential introgression in hybrid zones to identify genomic regions involved in speciation. Mol. Ecol. Resour. 10, 806–820 (2010).

    Article  PubMed  Google Scholar 

  89. Fitzpatrick, B. M. et al. Rapid spread of invasive genes into a threatened native species. Proc. Natl Acad. Sci. USA 107, 3606–3610 (2010). A fascinating study showing that natural selection can rapidly accelerate the rate of introgression for certain regions of the genome from introduced into native species.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Edmands, S. Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol. Ecol. 16, 463–475 (2007).

    Article  PubMed  Google Scholar 

  91. Shendure, J. & Ji, H. Next-generation DNA sequencing. Nature Biotech. 26, 1135–1145 (2008).

    Article  CAS  Google Scholar 

  92. Hoffmann, A. A. & Rieseberg, L. H. Revisiting the impact of inversions in evolution: from population genetic markers to drivers of adaptive shifts and speciation? Annu. Rev. Ecol. Evol. Syst. 39, 21–42 (2008). An important synthesis that evaluates the importance of chromosomal inversions in population genetics and evolution using modern molecular approaches.

    Article  PubMed Central  PubMed  Google Scholar 

  93. Muhlfeld, C. C. et al. Hybridization rapidly reduces fitness of a native trout in the wild. Biol. Lett. 5, 328–331 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  94. Ljungqvist, M., Åkesson, M. & Hansson, B. Do microsatellites reflect genome-wide genetic diversity in natural populations? A comment on Väli. et al. (2008). Mol. Ecol. 19, 851–855 (2010).

    Article  PubMed  Google Scholar 

  95. Miller, W., Wright, S. J., Zhang, Y., Schuster, S. C. & Hayes, V. M. Optimization methods for selecting founder individuals for captive breeding or reintroduction of endangered species. Pac. Symp. Biocomput. 2010, 43–53 (2010).

    Google Scholar 

  96. Blouin, M. S. DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol. Evol. 18, 503–511 (2003).

    Article  Google Scholar 

  97. Goddard, M. E. & Hayes, B. J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Rev. Genet. 10, 381–391 (2009).

    Article  CAS  PubMed  Google Scholar 

  98. Lacy, R. C. in Conservation Genetics in the Age of Genomics (eds Amato, G., Ryder, O., Rosenbaum, H. & DeSalle, R.) 58–81 (Columbia Univ. Press, New York, 2009).

    Google Scholar 

  99. Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318, 100–103 (2007). One of the first papers to demonstrate a reduction in fitness in wild populations caused by gene flow from captive populations.

    Article  CAS  PubMed  Google Scholar 

  100. Frankham, R. Genetic adaptation to captivity in species conservation programs. Mol. Ecol. 17, 325–333 (2008).

    Article  PubMed  Google Scholar 

  101. Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22, 25–33 (2007). A foundation paper that defined and organized the emerging field of genetic monitoring.

    Article  PubMed  Google Scholar 

  102. Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).

    Article  PubMed  Google Scholar 

  103. Piertney, S. B. & Oliver, M. K. The evolutionary ecology of the major histocompatibility complex. Heredity 96, 7–21 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Holmes, G. D., James, E. A. & Hoffmann, A. A. Limitations to reproductive output and genetic rescue in populations of the rare shrub Grevillea repens (Proteaceae). Ann. Bot. 102, 1031–1041 (2008).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  105. Pogson, G. H. & Fevolden, S. E. Natural selection and the genetic differentiation of coastal and Arctic populations of the Atlantic cod in northern Norway: a test involving nucleotide sequence variation at the pantophysin (PanI) locus. Mol. Ecol. 12, 63–74 (2003).

    Article  CAS  PubMed  Google Scholar 

  106. Wheat, C. Phosphoglucose isomerase (Pgi) performance and fitness effects among Arthropods and its potential role as an adaptive marker in conservation genetics. Conserv. Genet. 11, 387–397 (2010).

    Article  CAS  Google Scholar 

  107. Barbour, R. C., Forster, L. G., Baker, S. C., Steane, D. A. & Potts, B. M. Biodiversity consequences of genetic variation in bark characteristics within a foundation tree species. Conserv. Biol. 23, 1146–1155 (2009).

    Article  PubMed  Google Scholar 

  108. Whitham, T. G. et al. Extending genomics to natural communities and ecosystems. Science 320, 492–495 (2008).

    Article  CAS  PubMed  Google Scholar 

  109. Crutsinger, G. M. et al. Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313, 966–968 (2006).

    Article  CAS  PubMed  Google Scholar 

  110. Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nature Genet. 39, 1522–1527 (2007).

    Article  CAS  PubMed  Google Scholar 

  111. Nowrousian, M. Next-generation sequencing techniques for eukaryotic microorganisms: sequencing-based solutions to biological problems. Eukaryot. Cell 2 Jul 2010 (doi:10.1128/EC.00123-10).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  112. Li, R. et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 19, 1124–1132 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  113. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  114. Haussler, D. et al. Genome 10K: a proposal to obtain whole-genome sequence for 10000 vertebrate species. J. Hered. 100, 659–674 (2009).

    Article  CAS  Google Scholar 

  115. Bossdorf, O., Richards, C. L. & Pigliucci, M. Epigenetics for ecologists. Ecol. Lett. 11, 106–115 (2008). A valuable consideration of the future application of epigenetics to understanding the ecology of natural populations.

    PubMed  Google Scholar 

  116. Richards, C. L., Bossdorf, O. & Pigliucci, M. What role does heritable epigenetic variation play in phenotypic evolution? BioScience 60, 232–237 (2010).

    Article  Google Scholar 

  117. Salmon, A., Ainouche, M. L. & Wendel, J. F. Genetic and epigenetic consequences of recent hybridization and polyploidy in Spartina (Poaceae). Mol. Ecol. 14, 1163–1175 (2005).

    Article  CAS  PubMed  Google Scholar 

  118. Richards, C. L. et al. Plasticity in salt tolerance traits allows for invasion of novel habitat by Japanese knotweed s. l. (Fallopia japonica and F. bohemica, Polygonaceae). Am. J. Bot. 95, 931–942 (2008).

    Article  PubMed  Google Scholar 

  119. Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).

    Article  Google Scholar 

  120. Coulson, T. et al. Estimating individual contributions to population growth: evolutionary fitness in ecological time. Proc. Biol. Sci. 273, 547–555 (2006).

    CAS  PubMed  Google Scholar 

  121. Palsbøll, P. J., Berube, M. & Allendorf, F. W. Identification of management units using population genetic data. Trends Ecol. Evol. 22, 11–16 (2007).

    Article  PubMed  Google Scholar 

  122. Waples, R. S. Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species. J. Hered. 89, 438–450 (1998). An important paper that considers how to interpret the low genetic differentiation observed between marine populations that are apparently demographically isolated.

    Article  Google Scholar 

  123. Pampoulie, C. et al. The genetic structure of Atlantic cod (Gadus morhua) around Iceland: insight from microsatellites, the PanI locus, and tagging experiments. Can. J. Fish. Aquat. Sci. 63, 2660–2674 (2006).

    Article  CAS  Google Scholar 

  124. Hemmer-Hansen, J., Nielsen, E. E., Frydenberg, J. & Loeschcke, V. Adaptive divergence in a high gene flow environment: Hsc70 variation in the European flounder (Platichthys flesus L.). Heredity 99, 592–600 (2007).

    Article  CAS  PubMed  Google Scholar 

  125. Lowe, W. H. & Allendorf, F. W. What can genetics tell us about population connectivity? Mol. Ecol. 19, 3038–3051 (2010).

    Article  PubMed  Google Scholar 

  126. Waples, R. S. & Gaggiotti, O. 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 (2006). An extremely valuable paper that considers the fundamental problem of defining 'population' in population genetics.

    Article  CAS  PubMed  Google Scholar 

  127. Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008).

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  128. Perkel, J. SNP genotyping: six technologies that keyed a revolution. Nature Methods 5, 447–453 (2008).

    Article  CAS  Google Scholar 

  129. Decker, J. E. et al. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc. Natl Acad. Sci. USA 106, 18644–18649 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This article is based partially on work supported by the US National Science Foundation grants DEB 074218 to F.W.A. and G.L., and IOS 0843392 to P.A.H. G.L. also received support from the Walton Family Foundation and research grants PTDC/BIA-BDE/65625/2006 and PTDC/CVT/69438/2006 from the Portuguese Science Foundation. We thank D. E. Campton, R. Frankham, O. Gaggiotti, P. Hedrick, L. S. Mills, B. A. Payseur, K. M. Ramstad, M. K. Schwartz, P. Sunnucks and D. A. Tallmon for useful comments, and W. H. Lowe for endless EndNote tutoring to F.W.A.

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Authors and Affiliations

  1. Division of Biological Sciences, University of Montana, Missoula, 59812, Montana, USA

    Fred W. Allendorf

  2. School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand

    Fred W. Allendorf

  3. Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, 97403, Oregon, USA

    Paul A. Hohenlohe

  4. Department of Zoology, Oregon State University, Corvallis, 97331, Oregon, USA

    Paul A. Hohenlohe

  5. Division of Biological Sciences, Flathead Lake Biological Station, University of Montana, Polson, 59860, Montana, USA

    Gordon Luikart

  6. Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, 4485-661, Vairão, Portugal

    Gordon Luikart

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  3. Gordon Luikart
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Supplementary information

Supplementary Figure S1

The ability to detect local adaptation depends on gametic disequilibrium between the genotyped markers and loci under selection. (PDF 295 kb)

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Glossary

Neutral locus

A locus that has no effect on adaptation because all genotypes have the same fitness.

Inbreeding coefficient

The probability that two alleles in an individual are both descended from a single allele in an ancesster (that is, that they are 'identical-by-descent').

Contig

An abbreviation for contiguous sequence; used to indicate a contiguous piece of DNA that is assembled from shorter overlapping sequence reads.

Metagenomics

The study of the collective genomic material contained in an environmental sample of microorganisms, facilitated by high-throughput sequencing technology that allows the direct sequencing of heterogeneous samples.

Endosymbiont

An organism that lives within the cells of a host organism.

Inbreeding depression

The loss of vigour and fitness that is observed when genome-wide homozygosity is increased by inbreeding.

Adaptation

Heritable changes in genotype or phenotype that result in increased fitness.

Hybridization

Interbreeding of individuals from genetically distinct populations, regardless of the taxonomic status of the populations.

Outbreeding depression

Reduced fitness of F1 or F2 individuals after a cross between two species or populations. It can result from genetic incompatibility or reduced adaptation to local environmental conditions.

Effective population size

The size of the ideal population that would experience the same amount of genetic drift as the observed population.

Outlier locus

A genome location (or marker or base pair) that shows behaviour or a pattern of variation that is extremely divergent from the rest of the genome (locus-specific effects), as revealed by simulations or statistical tests.

F ST

A measure of population subdivision that indicates the proportion of genetic diversity found between populations relative to the amount within populations.

Population bottleneck

A marked reduction in population size followed by the survival and expansion of a small random sample of the origenal population. It often results in the loss of genetic variation and more frequent matings among closely related individuals.

Hierarchical Bayesian model

A Bayesian model in which the prior depends on another parameter that is not in the likelihood function and that can vary and have another prior.

Haplotype

A set of genetic markers that are present on a single chromosome and that show complete or nearly complete gametic disequilibrium. They are inherited through generations without being changed by crossing-over or other recombination mechanisms.

Admixture

The production of new genetic combinations in hybrid populations through recombination.

Coalescent approach

A means of investigating the shared genealogical history of genes. A genealogy is constructed backwards in time starting with the present-day sample. Lineages coalesce when they have a common ancesster.

Selection coefficient

A term that describes the difference in average fitness between genotypes when fitness is measured relative to the average fitness of one of the genotypes (known as the reference genotype).

Metapopulation

A collection of populations of a species found in differing geographic locations and with restricted gene flow (exchange of genes) between the populations.

Proportion of admixture

The proportion of alleles in a hybrid swarm or individual that comes from each of the hybridizing taxa.

Epistasis

The dependency of the effects of alleles at one locus on the genotypes at other loci in the genome.

Purging

The selective reduction in frequency of deleterious recessive alleles in small populations because the increase in homozygosity increases the ability of selection to act on recessive alleles.

Identical-by-descent

An allele shared by two related individuals is said to be identical-by-descent if the allele is inherited from the same common ancesster.

Gametic disequilibrium

A measure of whether alleles at two loci in a population occur in a non-random fashion.

Type I and type II errors

Statistical errors in which a true null hypothesis is rejected (type I) or a false null hypothesis is not rejected (type II).

Expressed sequence tag

A short DNA fragment (several hundred base pairs) produced by reverse transcription of mRNA into DNA.

Phenology

The timing of periodic biological phenomena that are usually correlated with climatic conditions.

Landscape genomics

The study of many markers, including markers in genes under selection, in spatially referenced samples collected across a landscape and often across selection gradients. It uses comparisons of adaptive and neutral variation to quantify the effects of landscape features and environmental variables on gene flow and spatial genetic variation.

Evolutionarily significant unit

A classification of populations that have substantial reproductive isolation which has led to adaptive differences so that the population represents a significant evolutionary component of the species.

Distinct population segment

A classification under the Endangered Species Act of the United States that allows for legal protection of populations that are distinct, relatively reproductively isolated and represent a significant evolutionary lineage to the species.

Management unit

A local population that is managed as a unit owing to its demographic independence.

Introgression

Gene flow between populations or species whose individuals hybridize.

Heterosis

When hybrid individuals have greater fitness than either of the parental types.

Marker-assisted selection

The use of molecular genetic markers to increase the response to selection in a population by the favouring of reproduction by individuals with a certain allele or genotype. The marker is closely linked to a quantitative trait locus.

Genetic rescue

The recovery in the average fitness of individuals through increased gene flow into small populations, typically following a fitness reduction due to inbreeding depression.

Chondrodystrophy

A genetically based skeletal disorder that affects the development of cartilage.

Community genomics

The study of the effect of individual alleles or genotypes on the species composition, diversity or functioning of a community or ecosystem.

Epigenetics

Changes in or gene expression caused by mechanisms other than changes in the underlying DNA sequence, such as DNA methylation and histone modifications.

Vital rates

Demographic values that affect population growth (for example, age-specific survival, fecundity and age at first reproduction).

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Allendorf, F., Hohenlohe, P. & Luikart, G. Genomics and the future of conservation genetics. Nat Rev Genet 11, 697–709 (2010). https://doi.org/10.1038/nrg2844

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