Tree Genetics & Genomes

, Volume 8, Issue 6, pp 1307–1318 | Cite as

SNP markers trace familial linkages in a cloned population of Pinus taeda—prospects for genomic selection

  • Jaime Zapata-Valenzuela
  • Fikret IsikEmail author
  • Christian Maltecca
  • Jill Wegrzyn
  • David Neale
  • Steve McKeand
  • Ross Whetten
Original Paper


Advances in DNA sequencing technology have made possible the genotyping of thousands of single-nucleotide polymorphism (SNP) markers, and new methods of statistical analysis are emerging to apply these advances in plant breeding programs. We report the utility of markers for prediction of breeding values in a forest tree species using empirical genotype data (3,406 polymorphic SNP loci). A total of 526 Pinus taeda L. clones tested widely in field trials were phenotyped at age 5 years. Only 149 clones from 13 full-sib crosses were genotyped. Markers were fit simultaneously to predict marker additive and dominance effects. Subsets of the 149 genotyped clones were used to train a model using all markers. Cross-validation strategies were followed for the remaining subset of genotyped individuals. The accuracy of genomic estimated breeding values ranged from 0.61 to 0.83 for wood lignin and cellulose content, and from 0.30 to 0.68 for height and volume traits. The accuracies of predictions based on markers were comparable with the accuracies based on pedigree. Because of the small number of SNP markers used and the relatively small population size, we suggest that observed accuracies in this study trace familial linkage rather than historical linkage disequilibrium with trait loci. Prediction accuracies of models that use only a subset of markers were generally comparable with the accuracies of the models using all markers, regardless of whether markers are associated with the phenotype. The results suggest that using SNP loci for selection instead of phenotype is efficient under different relative lengths of the breeding cycle, which would allow cost-effective applications in tree breeding programs. Prospects for applications of genomic selection to P. taeda breeding are discussed.


Loblolly pine Marker-aided selection Quantitative genetics Genomic selection Marker–trait association 



The authors thank Plum Creek Timber Company, Inc., and CellFor, Inc., for the field measurement data; David Barker and Josh Steiger (NCSU Cooperative Tree Improvement Program) for collection of wood cores, preparation of samples, and collection of NIR spectra; and Dr. Gary Hodge (Camcore) for prediction of lignin and cellulose content. This work was supported by the Conifer Translational Genomics Network Coordinated Agricultural Project (USDA NRI #2007-02781 and AFRI #2009-01879), the NC State University Cooperative Tree Improvement Program, and the Department of Forestry and Environmental Resources at NCSU.


  1. Bettinger P, Clutter M, Siry J, Kane M, Pait J (2009) Broad implications of southern United States pine clonal forestry on planning and management of forests. Int For Rev 11(3):331–345Google Scholar
  2. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633–2635PubMedCrossRefGoogle Scholar
  3. Brown GR, Bassoni DL, Gill GP, Fontana JR, Wheeler NC, Megraw RA, Davis MF, Sewell MM, Tuskan GA, Neale DB (2003) Identification of quantitative trait loci influencing wood property traits in loblolly pine (Pinus taeda L.). III. QTL verification and candidate gene mapping. Genetics 164:1537–1546PubMedGoogle Scholar
  4. Brown GR, Gill GP, Kuntz RJ, Langley CH, Neale DB (2004) Nucleotide diversity and linkage disequilibrium in loblolly pine. PNAS 101:15255–15260PubMedCrossRefGoogle Scholar
  5. Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 124:362–368PubMedCrossRefGoogle Scholar
  6. Cumbie WP (2010) Association genetics for growth, carbon isotope discrimination, and stem quality in loblolly pine. Dissertation, North Carolina State UniversityGoogle Scholar
  7. Cumbie WP, Eckert A, Wegrzyn J, Whetten R, Neale D, Goldfarb B (2011) Association genetics of carbon isotope discrimination, height and foliar nitrogen in a natural population of Pinus taeda L. Heredity. doi: 10.1038/hdy.2010.168
  8. Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, González-Martínez SC, Neale DB (2010) Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics 185(3):969–982PubMedCrossRefGoogle Scholar
  9. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Fourth edition. Longman Group, Ltd, Essex, p 464Google Scholar
  10. Garrick DJ, Taylor JF, Fernando RL (2009) Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol 41:55. doi: 10.1186/1297-9686-41-55 PubMedCrossRefGoogle Scholar
  11. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User. Guide release 3.0. VSN International Ltd., Hemel Hempstead, HP1 1ES, United KingdomGoogle Scholar
  12. Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domesticated animals and their use inbreeding programs. Nat Rev Genet 10:381–391PubMedCrossRefGoogle Scholar
  13. Goebel NB, Warner JR (1966) Total and bark volume tables for small diameter Loblolly, Shortleaf, and Virginia Pine in the upper South Carolina Piedmont. Forest Research Series No. 9, Clemson UniversityGoogle Scholar
  14. González-Martínez SC, Wheeler NC, Ersoz E, Nelson CD, Neale DB (2007) Association genetics in Pinus taeda L. I. Wood property traits. Genetics 175:399–499PubMedCrossRefGoogle Scholar
  15. González-Martínez SC, Huber D, Ersoz E, Davis JM, Neale DB (2008) Association genetics in Pinus taeda L. II. Carbon isotope discrimination. Heredity 101(1):19–26PubMedCrossRefGoogle Scholar
  16. Grattapaglia D, Resende MDV (2010) Genomic selection in forest tree breeding. Tree Genet Genome 7(2):241–255CrossRefGoogle Scholar
  17. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92(2):433–443PubMedCrossRefGoogle Scholar
  18. Hodge GR, Woodbridge WC (2010) Global near infrared models to predict lignin and cellulose content of pine wood. J Near Infrared Spectrosc 18:367–380CrossRefGoogle Scholar
  19. Isik F, Li B, Frampton J (2003) Estimates of additive, dominance and epistatic genetic variances from a clonally replicated test of loblolly pine. For Sci 49(1):77–88Google Scholar
  20. Isik F, Boos DD, Li B (2005) The distribution of genetic parameter estimates and confidence intervals from small disconnected diallels. Theor Appl Genet 110:1236–1243PubMedCrossRefGoogle Scholar
  21. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 9(2):166–177CrossRefGoogle Scholar
  22. Kaya Z, Neale DB, Sewell MM (1999) Identification of quantitative trait loci influencing annual height- and diameter-increment growth in loblolly pine (Pinus taeda L.). Theor Appl Genet 98(3/4):586–592CrossRefGoogle Scholar
  23. Knott SA, Neale DB, Sewell MM, Haley CS (1997) Multiple marker mapping of quantitative trait loci in an outbred pedigree of loblolly pine. Theor Appl Genet 94(6–7):810–820CrossRefGoogle Scholar
  24. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  25. Legarra A, Misztal I (2008) Technical note: computing strategies in genome-wide selection. J Dairy Sci 91:360–366PubMedCrossRefGoogle Scholar
  26. Legarra A, Robert-Granie C, Manfredi E, Elsen JM (2008) Performance of genomic selection in mice. Genetics 180:611–618PubMedCrossRefGoogle Scholar
  27. Liu Z, Seefried FR, Reinhardt F, Rensing S, Thaller G, Reents R (2011) Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genet Sel Evol 43:19PubMedCrossRefGoogle Scholar
  28. Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577PubMedCrossRefGoogle Scholar
  29. McKeand SE, Jokela EJ, Huber DA, Byram TD, Allen HL, Li B, Mullin TJ (2006) Performance of improved genotypes of loblolly pine across different soils, climates, and silvicultural inputs. For Ecol Manag 227:178–184CrossRefGoogle Scholar
  30. Meuwissen THE (2009) Accuracy of breeding values of unrelated individuals predicted by dense SNP genotyping. Genet Sel Evol 41:35PubMedCrossRefGoogle Scholar
  31. Meuwissen THE, Goddard ME (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631PubMedCrossRefGoogle Scholar
  32. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  33. Moser G, Khatkar MS, Hayes BJ, Raadsma HW (2010) Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet Sel Evol 42:37–52PubMedCrossRefGoogle Scholar
  34. Mrode RA (2005) Linear models for the prediction of animal breeding values. CAB International, WallingfordCrossRefGoogle Scholar
  35. Neale DB (2007) Genomics to tree breeding and forest health. Curr Opin Genet Dev 17:539–544PubMedCrossRefGoogle Scholar
  36. Neale DB, Savolainen O (2004) Association genetics of complex traits in conifers. Trends Plant Sci 9:325–330PubMedCrossRefGoogle Scholar
  37. Neale DB, Williams CG (1991) Restriction fragment length polymorphism mapping in conifers and applications to forest genetics and tree improvement. Can J For Res 21:545–554CrossRefGoogle Scholar
  38. Neale DB, Sewell MM, Brown GR (2002) Molecular dissection of the inheritance of wood property traits in loblolly pine. Ann For Sci 59:595–605CrossRefGoogle Scholar
  39. Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49:1165–1176CrossRefGoogle Scholar
  40. Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6(1):7–11Google Scholar
  41. Pyhäjärvi T, García-Gil MR, Knürr T, Mikkonen M, Wachowiak W, Savolainen O (2007) Demographic history has influenced nucleotide diversity in European Pinus sylvestris populations. Genetics 177:1713–1724PubMedCrossRefGoogle Scholar
  42. Quesada T, Gopal V, Cumbie WP, Eckert AJ, Wegrzyn JL, Neale DB, Goldfarb B, Huber DA, Casella G, Davis JM (2010) Association mapping of quantitative disease resistance in a natural population of loblolly pine (Pinus taeda L.). Genetics 186(2):677–686PubMedCrossRefGoogle Scholar
  43. Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2011) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. doi: 10.1111/j.1469-8137.2011.03895.x
  44. SAS Institute Inc (2010) SAS online doc 9.2. SAS Institute Inc, Cary, pp 2002–2005Google Scholar
  45. Solberg TR, Sonesson AK, Woolliams J, Meuwissen THE (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454PubMedCrossRefGoogle Scholar
  46. Strauss SH, Lande R, Namkoong G (1992) Limitations of molecular-marker-aided selection in forest tree breeding. Can J For Res 22:1050–1061CrossRefGoogle Scholar
  47. Sykes R, Li B, Isik F, Kadla J, Chang HM (2006) Genetic variation and genotype by environment interactions of juvenile wood chemical properties in Pinus taeda L. Ann For Sci 63:897–904CrossRefGoogle Scholar
  48. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel F (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92:16–24PubMedCrossRefGoogle Scholar
  49. Wegrzyn JL, Lee JM, Tearse BR, Neale DB (2008) TreeGenes: a forest tree genome database. Int J Plant Genom. doi: 10.1155/2008/412875
  50. White TL, Adams WT, Neale DB (2007) Forest genetics. CABI Publishing CAB International, CambridgeCrossRefGoogle Scholar
  51. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7):565–569PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Jaime Zapata-Valenzuela
    • 1
  • Fikret Isik
    • 1
    Email author
  • Christian Maltecca
    • 2
  • Jill Wegrzyn
    • 3
  • David Neale
    • 3
  • Steve McKeand
    • 1
  • Ross Whetten
    • 1
  1. 1.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Animal ScienceNorth Carolina State UniversityRaleighUSA
  3. 3.Department of Plant ScienceUniversity of California-DavisDavisUSA

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