Ecological genomics of local adaptation

Journal name:
Nature Reviews Genetics
Volume:
14,
Pages:
807–820
Year published:
DOI:
doi:10.1038/nrg3522
Published online

Abstract

It is increasingly important to improve our understanding of the genetic basis of local adaptation because of its relevance to climate change, crop and animal production, and conservation of genetic resources. Phenotypic patterns that are generated by spatially varying selection have long been observed, and both genetic mapping and field experiments provided initial insights into the genetic architecture of adaptive traits. Genomic tools are now allowing genome-wide studies, and recent theoretical advances can help to design research strategies that combine genomics and field experiments to examine the genetics of local adaptation. These advances are also allowing research in non-model species, the adaptation patterns of which may differ from those of traditional model species.

At a glance

Figures

  1. Defining local adaptation.
    Figure 1: Defining local adaptation.

    Fitness comparisons between populations are shown. In reciprocal transplant experiments there is a pattern in which each locally adapted population in its native site has higher fitness than any other population in the same site1, 3 (part a). Fitness comparisons at individual genetic loci are shown in parts b and c. Antagonistic pleiotropy at the single-locus level: each allele at the shown quantitative trait locus (QTL) has the highest flowering probability at its home site in a Boechera stricta reciprocal transplant experiment that was carried out in Montana (M) and Colorado (C), USA80 (part b). Conditional neutrality is illustrated for two QTLs affecting the same fitness trait (part c). The local allele at one QTL (locus 1; shown on the left) shows fitness advantages at site A relative to the non-native allele, whereas the alleles do not differ in their fitness at the other site (site B). At the other QTL (locus 2; shown on the right), the native allele shows fitness advantage relative to the non-native allele at site B, whereas at site A the alleles do not differ in their fitness effects174. N, north; S, south. Part b is modified, with permission, from Ref. 80 © (2013) John Wiley & Sons, Inc. Part c is modified, with permission, from Ref. 174 © (2012) New Phytologist Trust.

  2. Effect sizes of alleles from association or QTL mapping studies.
    Figure 2: Effect sizes of alleles from association or QTL mapping studies.

    a | The distribution of the quantitative trait locus (QTL) effect size (d; the difference between effects of the alleles in the two populations that are adapting to different environments) is expected to evolve with time under migration–selection balance. Three different numbers of elapsed generations (t) are shown41. Evolution towards fewer loci with larger-effect-size alleles is expected under divergence41. b | Effect sizes for flowering time alleles of maize in a large experiment that involves the QTL analysis of many different crosses are shown. These alleles can either decrease or increase the time of female flowering, but nearly all of them affect flowering time by less than 1.0 day105. c | The gamma-shaped effect-size distribution of three-spined stickleback body-size QTLs from an interpopulation cross shows moderate effects between Japanese and North American populations. Effect size was measured as the difference between two homozygotes175. Effect sizes for QTLs are shown in green and the effect size of the sex-determining region is shown in red for comparison. Part a is modified, with permission, from Ref. 41 © (2011) John Wiley & Sons, Inc. Part b is modified, with permission, from Ref. 105 © (2009) American Association for the Advancement of Science. Part c is modified, with permission, from Ref. 175 © (2008) John Wiley & Sons, Inc.

  3. Evidence for local adaptation in lateral plate numbers in three-spined sticklebacks using different approaches.
    Figure 3: Evidence for local adaptation in lateral plate numbers in three-spined sticklebacks using different approaches.

    a | Increased divergence is seen at the ectodysplasin (EDA) locus among marine and freshwater populations of sticklebacks from a resequencing approach32. b | Experimental evidence shows allele frequency changes at the EDA locus in replicate freshwater colonizations of sticklebacks in different ponds176. c | Evidence shows adaptive differentiation both in lateral plate numbers and in underlying alleles at the EDA locus in a high gene-flow marine environment, the Gulf of Finland in the Baltic Sea177. The inner pie chart shows the frequencies of EDA alleles that are associated with low numbers of lateral plates (light blue sectors) and high numbers of lateral plates (dark blue sectors) in the samples. The dark red area in the outer ring (where applicable) indicates the actual average number of lateral plates (expressed as the proportion of 25 lateral plates). d | The evidence for repeated selective sweeps in the EDA locus in global pairwise populations is obtained from data in Ref. 136. EDA allele frequencies (shown in pie charts) and dominant lateral plate phenotypes (represented by the fish symbols) are given for each pair of freshwater and marine populations. The frequencies of the EDA allele for full-plate morph (dark blue sectors) and low-plate morph (light blue sectors) are shown. The asterisks (*) indicate cases with evidence for selection on EDA markers. Part a is modified, with permission, from Ref. 32 © (2012) Macmillan Publishers Ltd. All rights reserved. Part b is modified, with permission, from Ref. 176 © (2008) American Association for the Advancement of Science. Part c is modified, with permission, from Ref. 177 © (2013) John Wiley & Sons, Inc. Part d is modified, with permission, from Ref. 136 © (2011) John Wiley & Sons, Inc.

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Affiliations

  1. Department of Biology and Biocenter Oulu, University of Oulu, FIN-90014 Oulu, Finland.

    • Outi Savolainen
  2. Department of Ecology and Genetics, Evolutionary Biology Center, University of Uppsala, SE-75236 Uppsala, Sweden.

    • Martin Lascoux
  3. Ecological Genetics Research Unit, Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland.

    • Juha Merilä

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The authors declare no competing interests.

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  • Outi Savolainen

    Outi Savolainen is Professor of Genetics at the University of Oulu, Finland. She received her Ph.D. in Genetics from the University of California at Davis, USA. She is interested in evolution and population genetics of adaptation, especially adaptation of plant populations. Outi Savolainen's homepage.

  • Martin Lascoux

    Martin Lascoux is Professor of Population Genetics at Uppsala University, Sweden. He received his Ph.D. from Université Paris-Sud, Orsay, France, under the supervision of Antoine Kremer. He is broadly interested in population genetics and works mostly on plants.

  • Juha Merilä

    Juha Merilä is Professor of Population Genetics at the University of Helsinki, Finland. He received his Ph.D. in Animal Ecology from Uppsala University, Sweden, under the supervision of Mats Björklund and Staffan Ulfstrand. He is broadly interested in evolutionary biology and genetics. Juha Merilä's homepage.

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