Biases induced by using geography and environment to guide ex situ conservation

Abstract

Ex situ germplasm collections seek to conserve maximum genetic diversity in a small number of samples. Geographic and environmental information have long been treated as surrogate measures of genetic diversity, proposed to be useful for increasing allelic diversity of collections. We examine the effect of maximizing geographic and environmental diversity on the retention of distinct haplotype blocks in germplasm subsets, using three species with extensive genomewide genotypic data. We show that maximizing diversity in the surrogate measures produces subsets with uneven representation of haplotypic diversity across the genome. Some regions are well-conserved, exhibiting high haplotypic diversity, while others are poorly-conserved and contain significantly less haplotypic diversity than would be obtained via random sampling. In two of three species, poorly-conserved genomic regions were enriched in regulatory genes which, as a class, contribute to phenotypic variation. The specific genes affected varied by species but, overall, haplotypic diversity was poorly-conserved at genes controlling ~ 10% of major molecular functions and biological processes. While this study was limited to three exemplar species, we find little evidence to support continued use of geographic or environmental surrogates for ex situ conservation activities attempting to capture maximum genomewide allelic diversity. Although geographic and environmental diversity have proven to be reliable predictors of allele frequency differences and ecotypic differentiation across species ranges, they appear to be poor predictors of allelic diversity per se, offering little opportunity to enrich collections for haplotypic diversity overall, and ample opportunity to bias the conservation of important functional genetic variation. We propose a bioinformatic bridge between haplotypic diversity and the potential phenotypic diversity residing in collections using the Gene Ontology.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29

    CAS  Article  Google Scholar 

  2. Brown AHD (1989) Core collections: a practical approach to genetic resources management. Genome 31:818–824

    Article  Google Scholar 

  3. Brown AHD (1995) The core collection at the crossroads. In: Hodgkin T, Brown AHD, van Hintum TJL, Morales EAV (eds) Core collections of plant genetic resources. Wiley, Chichester, pp 55–76

    Google Scholar 

  4. Caballero A, García-Dorado A (2013) Allelic diversity and its implications for the rate of adaptation. Genetics 195:1373–1384

    Article  Google Scholar 

  5. Caballero A, Rodríguez-Ramilo ST (2010) A new method for the partition of allelic diversity within and between subpopulations. Conserv Genet 11:2219–2229

    Article  Google Scholar 

  6. Carroll SB (2008) Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell 134:25–36

    CAS  Article  Google Scholar 

  7. Davis MB, Shaw RG (2001) Range shifts and adaptive responses to Quaternary climate change. Science 292:673–679

    CAS  Article  Google Scholar 

  8. Diener AC, Ausubel FM (2005) Resistance to fusarium oxysporum 1, a dominant Arabidopsis disease-resistance gene, is not race specific. Genetics 171:305–321

    CAS  Article  Google Scholar 

  9. Diwan N, McIntosh MS, Bauchan GR (1995) Methods of developing a core collection of annual Medicago species. Theor Appl Genet 90:755–761

    CAS  Article  Google Scholar 

  10. Doebley J, Lukens L (1998) Transcriptional regulators and the evolution of plant form. Plant Cell 10:1075–1082

    CAS  Article  Google Scholar 

  11. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Ann Rev Plant Biol 54:357–374

    CAS  Article  Google Scholar 

  12. Frankel OH, Brown AHD (1984) Current plant genetic resources—a critical appraisal. In: Genetics: new frontiers, volume IV applied genetics, Proceedings of the XV congress of genetics. Oxford & IBH, New Delhi, pp 3–13

  13. Fraser DJ, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10:2741–2752

    CAS  Article  Google Scholar 

  14. Friedman C, Borlawsky T, Shagina L, Xing HR, Lussier YA (2006) Bio-Ontology and text: bridging the modeling gap. Bioinformatics 22:2421–2429

    CAS  Article  Google Scholar 

  15. Geraldes A, Farzaneh N, Grassa CJ, McKown AD, Guy RD, Mansfield SD, Douglas CJ, Cronk QCB (2014) Landscape genomics of Populus trichocarpa: the role of hybridization, limited gene flow, and natural selection in shaping patterns of population structure. Evolution 68:3260–3280

    Article  Google Scholar 

  16. Gillies SA, Futardo A, Henry RJ (2012) Gene expression in the developing aleurone and starchy endosperm of wheat. Plant Biotech J 10:668–679

    CAS  Article  Google Scholar 

  17. Gouesnard B, Bataillon TM, Decoux G, Rozale C, Schoen DJ, David JL (2001) MSTRAT: an algorithm for building germ plasm core collections by maximizing allelic or phenotypic richness. J Hered 92:93–94

    CAS  Article  Google Scholar 

  18. Gross BL, Volk GM, Richards CM, Reeves PA, Henk AD, Forsline PL, Szewc-McFadden A, Fazio G, Chao CT (2013) Diversity captured in the USDA-ARS national plant germplasm system apple core collection. J Am Soc Hort Sci 138:375–381

    Google Scholar 

  19. Hanson JO, Rhodes JR, Riginos C, Fuller RA (2017) Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. Proc Natl Acad Sci 114:12755–12760

    CAS  Article  Google Scholar 

  20. Harrisson KA, Pavlova A, Telonis-Scott M, Sunnucks P (2014) Using genomics to characterize evolutionary potential for conservation of wild populations. Evol Appl 7:1008–1025

    Article  Google Scholar 

  21. Holbrook CC, Anderson WF, Pittman RN (1993) Selection of a core collection from the US germplasm collection of peanut. Crop Sci 33:859–861

    Article  Google Scholar 

  22. Jost L (2008) Gst and its relatives do not measure differentiation. Mol Ecol 17:4015–4026

    Article  Google Scholar 

  23. Kimura M, Crow JF (1964) The number of alleles that can be maintained in a finite population. Genetics 49:725–738

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Knowles LL, Carstens BC, Keat ML (2007) Coupling genetic and ecological-niche models to examine how past population distributions contribute to divergence. Curr Biol 17:940–946

    CAS  Article  Google Scholar 

  25. Konishi S, Izawa T, Lin SY, Ebana K, Fukuta Y, Sasaki T, Yano M (2006) An SNP caused loss of seed shattering during rice domestication. Science 312:1392–1396

    CAS  Article  Google Scholar 

  26. Lasky JR, Des Marais DL, McKay JK, Richards JH, Juenger TE, Keitt TH (2012) Characterizing genomic variation of Arabidopsis thaliana: the roles of geography and climate. Mol Ecol 21:5512–5529

    Article  Google Scholar 

  27. Lasky JR, Upadhyaya HD, Ramu P, Deshpande S, Hash CT, Bonnette J, Juenger TE, Hyma K, Acharya C, Mitchell SE, Buckler ES, Brenton Z, Kresovich S, Morris GP (2015) Genome–environment associations in sorghum landraces predict adaptive traits. Sci Adv 1:e1400218

    Article  Google Scholar 

  28. Le Rouzic A, Carlborg Ö (2008) Evolutionary potential of hidden genetic variation. Trends Ecol Evol 23:33–37

    Article  Google Scholar 

  29. Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673

    Article  Google Scholar 

  30. Leimu R, Fischer M (2008) A meta-analysis of local adaptation in plants. PLoS ONE 3:e4010

    Article  Google Scholar 

  31. Li W, Zhu Z, Chern M, Yin J, Yang C, Ran L, Cheng M, He M, Wang K, Wang J, Zhou X, Zhu X, Chen Z, Wang J, Zhao W, Ma B, Qin P, Chen W, Wang Y, Liu J, Wang W, Wu X, Li P, Wang J, Zhu L, Li S, Chen X (2017) A natural allele of a transcription factor in rice confers broadspectrum blast resistance. Cell 170:114–126

    CAS  Article  Google Scholar 

  32. Linhart YB, Grant MC (1996) Evolutionary significance of local genetic differentiation in plants. Ann Rev Ecol Syst 27:237–277

    Article  Google Scholar 

  33. Lu S, Zhao X, Hu Y, Liu S, Nan H, Li X, Fang C, Cao D, Shi X, Kong L, Su T, Zhang F, Li S, Wang Z, Yuan X, Cober ER, Weller JL, Liu B, Hou X, Tian Z, Kong F (2017) Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. Nat Genet 49:773–779

    CAS  Article  Google Scholar 

  34. Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621

    Article  Google Scholar 

  35. Mao H, Wang H, Liu S, Li Z, Yang X, Yan J, Li J, Tran L-SP, Qin F (2015) A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat Commun 6:8326

    CAS  Article  Google Scholar 

  36. McKhann HI, Camilleri C, Bérard A, Bataillon T, David JL, Reboud X, Le Corre V, Caloustian C, Gut IG, Brunel D (2004) Nested core collections maximizing genetic diversity in Arabidopsis thaliana. Plant J 38:193–202

    CAS  Article  Google Scholar 

  37. Meirmans PG (2012) The trouble with isolation by distance. Mol Ecol 21:2839–2846

    Article  Google Scholar 

  38. Neph S, Kuehn MS, Reynolds AP, Haugen E, Thurman RE, Johnson AK, Rynes E, Maurano MT, Vierstra J, Thomas S, Sandstrom R, Humbert R, Stamatoyannopoulos JA (2012) BEDOPS: high-performance genomic feature operations. Bioinformatics 28:1919–1920

    CAS  Article  Google Scholar 

  39. Paaby AB, Rockman MV (2014) Cryptic genetic variation: evolution’s hidden substrate. Nat Rev Genet 15:247–258

    CAS  Article  Google Scholar 

  40. Parra-Quijano M, Iriondo JM, Torres E (2012) Improving representativeness of genebank collections through species distribution models, gap analysis and ecogeographical maps. Biodivers Conserv 21:79–96

    Article  Google Scholar 

  41. Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855

    Article  Google Scholar 

  42. Reeves PA, Richards CM (2017) Capturing haplotypes in germplasm core collections using bioinformatics. Genet Resour Crop Evol. https://doi.org/10.1007/s10722-017-0549-6

    Article  Google Scholar 

  43. Reeves PA, Panella LW, Richards CM (2012) Retention of agronomically important variation in germplasm core collections: implications for allele mining. Theor Appl Genet 124:1155–1171

    Article  Google Scholar 

  44. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219–1228

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Ruan Y-L, Llewellyn DJ, Furbank RT (2003) Suppression of sucrose synthase gene expression represses cotton fiber cell initiation, elongation, and seed development. Plant Cell 15:952–964

    CAS  Article  Google Scholar 

  46. Sam LT, Mendonça EA, Li J, Blake J, Friedman C, Lussier YA (2009) PhenoGO: an integrated resource for the multiscale mining of clinical and biological data. BMC Bioinform 10:S8. https://doi.org/10.1186/1471-2105-10-S2-S8

    Article  Google Scholar 

  47. Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644

    CAS  Article  Google Scholar 

  48. Schoen DJ, Brown AHD (1995) Maximising genetic diversity in core collections of wild relatives of crop species. In: Hodgkin T, Brown AHD, van Hintum TJL, Morales EAV (eds) Core collections of plant genetic resources. Wiley, Chichester, pp 55–76

    Google Scholar 

  49. Sork VL, Nason J, Campbell DR, Fernandez JF (1999) Landscape approaches to historical and contemporary gene flow in plants. Trends Ecol Evol 14:219–224

    CAS  Article  Google Scholar 

  50. Studer A, Zhao Q, Ross-Ibarra J, Doebley J (2011) Identification of a functional transposon insertion in the maize domestication gene tb1. Nat Genet 43:1160–1163

    CAS  Article  Google Scholar 

  51. Taketa S, Amano S, Tsujino Y, Sato T, Saisho D, Kakeda K, Nomura M, Suzuki T, Matsumoto T, Sato K, Kanamori H, Kawasaki S, Takeda K (2008) Barley grain with adhering hulls is controlled by an ERF family transcription factor gene regulating a lipid biosynthesis pathway. Proc Natl Acad Sci 105:4062–4067

    CAS  Article  Google Scholar 

  52. Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 22:1063–1066

    Article  Google Scholar 

  53. The Gene Ontology Consortium (2017) Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res 45:D331–D338

    Article  Google Scholar 

  54. Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a ground nut core collection using taxonomical, geographical and morphological descriptors. Genet Resour Crop Evol 50:139–148

    CAS  Article  Google Scholar 

  55. Vekemans X, Hardy OJ (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Mol Ecol 13:921–935

    CAS  Article  Google Scholar 

  56. Wall JD, Pritchard JK (2003) Haplotype blocks and linkage dis-equilibrium in the human genome. Nat Rev Genet 4:587–597

    CAS  Article  Google Scholar 

  57. Wang H, Nussbaum-Wagler T, Li B, Zhao Q, Vigouroux Y, Faller M, Bomblies-Yant K, Lukens L, Doebley J (2005) The origin of the naked grains of maize. Nature 436:714–719

    CAS  Article  Google Scholar 

  58. Waples RS (1995) Evolutionarily significant units and the conservation of biological diversity under the endangered species act. Am Fish Soc Symp 17:8–27

    Google Scholar 

  59. Wright S (1943) Isolation by distance. Genetics 28:114–138

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Kelly Robbins for helpful comments on the manuscript. This research used resources provided by the SCINet project of the USDA Agricultural Research Service, ARS Project Number 0500-00093-001-00-D.

Funding

This study was supported through funds provided to the National Laboratory for Genetic Resources Preservation, Plant Preservation Research Unit by USDA-ARS National Program 301.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Patrick A. Reeves.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1. Schematic representation of quantization scheme to convert continuous geographical and environmental variables into categorical data upon which diversity maximization can be conducted. For simplicity, a hypothetical sample of 5 populations from coastal Italy is shown, with geographic data (latitude and longitude) as the target for quantization. Supplementary Figure 2. Biased representation of biological processes and molecular functions in well-conserved genomic regions. The length of bars represents the ratio of the observed frequency of a term to its expected frequency using the plant GOslim ontology. The X axis is scaled as log base 2 to display folddifference between observed and expected values fairly. Values above one indicate enrichment of GO terms in regions of the genome where haplotypic variation was elevated. The 10 most biased GO terms are shown. For each species, the top chart shows GO term representation bias in geographic subsets; bottom, environmental subsets. (PDF 113 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Reeves, P.A., Richards, C.M. Biases induced by using geography and environment to guide ex situ conservation. Conserv Genet 19, 1281–1293 (2018). https://doi.org/10.1007/s10592-018-1098-z

Download citation

Keywords

  • Gene ontology
  • Genetic diversity
  • Genomewide SNP
  • Haplotype block
  • Phenotype