Mapping quantitative trait loci for growth and wood property traits in Cryptomeria japonica across multiple environments

  • Hideki Mori
  • Saneyoshi Ueno
  • Tokuko Ujino-Ihara
  • Takeshi Fujiwara
  • Kana Yamashita
  • Seiichi Kanetani
  • Ryota Endo
  • Asako MatsumotoEmail author
  • Kentaro Uchiyama
  • Yukari Matsui
  • Takahiro Yoshida
  • Yoshimi Sakai
  • Yoshinari Moriguchi
  • Ryouichi Kusano
  • Yoshihiko Tsumura
Original Article
Part of the following topical collections:
  1. Complex Traits


Genomic regions which affected tree growth and wood property traits were investigated in the major plantation tree of Japan, Cryptomeria japonica, in three replicated common garden experiments planted in contrasting environments in Kyushu and Honshu, Japan. Phenotypic traits measured were stem diameter at breast height, tree height, wood strength (Young’s modulus), heartwood density, sapwood density, heartwood moisture content, and sapwood moisture content. Quantitative trait locus (QTL) analysis identified an average of 53 QTLs across the three environments. There were two QTLs which affected the same traits across all three environments. These stable QTLs were identified as being associated with sapwood density and Young’s modulus and explained 3.5–11.3% and 2.1–18.7% of the total genotypic variation, respectively. In contrast, the majority of QTLs detected were unique to only one environment, a finding which is consistent with QTL mapping studies of other forest trees, indicating a substantial contribution of environmental effects on the mapping progenies. Nonetheless, the two stable QTLs identified in this study could be important genomic regions to target for further research aimed at maximizing breeding efficiency and wood quality of C. japonica across wide environmental gradients.


Sugi QTLs Biomass Wood moisture content 



The authors thank Y. Komatsu and M. Kawasaki for technical assistance, and T. Takata, K. Ogata, S. Kobayashi, K. Arai, K. Nemoto, and M. Sugiyama for the maintenance of research materials. We also thank Dr. H. Iwata for his helpful advices regarding the data analyses, Dr. J. Worth for revising the manuscript, and Dr. K. Nanko for his comments on climate data of the study sites. This study was supported by the Research Grant #201421 of the Forestry and Forest Products Research Institute.

Data archiving statement

The information about all markers used for this study has been registered in DDBJ. Marker/position information from the linkage map are submitted to TreeGenes Database.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11295_2019_1346_MOESM1_ESM.pptx (851 kb)
Fig. S1 Climatic conditions in the three sites during the experimental years (2005-2017); a) Mean annual precipitation and temperature. b) Monthly variation of precipitation and temperature. (PPTX 850 kb)
11295_2019_1346_MOESM2_ESM.xlsx (13 kb)
ESM 2 (XLSX 12 kb)
11295_2019_1346_MOESM3_ESM.xlsx (15 kb)
ESM 3 (XLSX 14 kb)
11295_2019_1346_MOESM4_ESM.xlsx (19 kb)
ESM 4 (XLSX 19 kb)


  1. Bartholomé J, Salmon F, Vigneron P, Bouvet J-M, Plomion C, Gion J-M (2013) Plasticity of primary and secondary growth dynamics in Eucalyptus hybrids: a quantitative genetics and QTL mapping perspective. BMC Plant Biol 13:120CrossRefGoogle Scholar
  2. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48CrossRefGoogle Scholar
  3. Bdeir R, Muchero W, Yordanov Y, Tuskan GA, Busov V, Gailing O (2017) Quantitative trait locus mapping of Populus bark features and stem diameter. BMC Plant Biol 17:224CrossRefGoogle Scholar
  4. Berlin S, Hallingbäck HR, Beyer F, Nordh N-E, Weih M, Rönnberg-Wästljung A-C (2017) Genetics of phenotypic plasticity and biomass traits in hybrid willows across contrasting environments and years. Ann Bot 120:87–100CrossRefGoogle Scholar
  5. Bradshaw H, Stettler RF (1995) Molecular genetics of growth and development in Populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139:963–973PubMedGoogle Scholar
  6. 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 164:1537–1546PubMedPubMedCentralGoogle Scholar
  7. Butler D, Cullis BR, Gilmour A, Gogel B (2009) ASReml-R reference manual. The State of Queensland, Department of Primary Industries and Fisheries, BrisbaneGoogle Scholar
  8. Carolyn RA (2002) Genetics of eucalyptus wood properties. Ann For Sci 59:525–531CrossRefGoogle Scholar
  9. Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196CrossRefGoogle Scholar
  10. Costa e Silva J, Potts BM, Dutkowski GW (2006) Genotype by environment interaction for growth of Eucalyptus globulus in Australia. Tree Genet Genomes 2:61–75CrossRefGoogle Scholar
  11. Costa e Silva J, Hardner C, Tilyard P, Potts BM (2011) The effects of age and environment on the expression of inbreeding depression in Eucalyptus globulus. Heredity 107:50–60CrossRefGoogle Scholar
  12. Downes G, Harwood C, Washusen R, Ebdon N, Evans R, White D, Dumbrell I (2014) Wood properties of Eucalyptus globulus at three sites in Western Australia: effects of fertiliser and plantation stocking. Aust For 77:179–188CrossRefGoogle Scholar
  13. Freeman JS, Whittock SP, Potts BM, Vaillancourt RE (2009) QTL influencing growth and wood properties in Eucalyptus globulus. Tree Genet Genomes 5:713–722CrossRefGoogle Scholar
  14. Freeman JS, Potts BM, Downes GM, Pilbeam D, Thavamanikumar S, Vaillancourt RE (2013) Stability of quantitative trait loci for growth and wood properties across multiple pedigrees and environments in Eucalyptus globulus. New Phytol 198:1121–1134CrossRefGoogle Scholar
  15. Fujisawa Y, Ohta S, Nishimura K, Tajima M (1992) Wood characteristics and genetic variations in sugi (Cryptomeria japonica): clonal differences and correlations between locations of dynamic moduli of elasticity and diameter growths in plus-tree clones. (Cryptomeria japonica): clonal differences and correlations between locations of dynamic moduli of elasticity and diameter growths in plus-tree clones. Mokuzai Gakkaishi 38:638–644Google Scholar
  16. Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293CrossRefGoogle Scholar
  17. Grattapaglia D, Resende MD (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7(2):241–255CrossRefGoogle Scholar
  18. Grattapaglia D, Bertolucci FL, Penchel R, Sederoff RR (1996) Genetic mapping of quantitative trait loci controlling growth and wood quality traits in Eucalyptus grandis using a maternal half-sib family and RAPD markers. Genetics 144:1205–1214PubMedPubMedCentralGoogle Scholar
  19. Hiraoka Y, Fukatsu E, Mishima K, Hirao T, Teshima KM, Tamura M, Tsubomura M, Iki T, Kurita M, Takahashi M, Watanabe A (2018) Potential of genome-wide studies in unrelated plus trees of a coniferous species, Cryptomeria japonica (Japanese cedar). Front Plant Sci 9:1322CrossRefGoogle Scholar
  20. Housset JM, Nadeau S, Isabel N, Depardieu C, Duchesne I, Lenz P, Girardin MP (2018) Tree rings provide a new class of phenotypes for genetic associations that foster insights into adaptation of conifers to climate change. New Phytol 218:630–645CrossRefGoogle Scholar
  21. Iwata H, Minamikawa MF, Kajiya-Kanegae H, Ishimori M, Hayashi T (2016) Genomics-assisted breeding in fruit trees. Breed Sci 66(1):100–115CrossRefGoogle Scholar
  22. Jermstad KD, Bassoni DL, Jech KS, Ritchie GA, Wheeler NC, Neale DB (2003) Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas fir. III. Quantitative trait loci-by-environment interactions. Genetics 165:1489–1506PubMedPubMedCentralGoogle Scholar
  23. Kim D-W, Murphy G (2013) Forecasting air-drying rates of small Douglas-fir and hybrid poplar stacked logs in Oregon, USA. Int J For Eng 24:137–147Google Scholar
  24. Kuramoto N, Kondo T, Fujisawa Y, Nakata R, Hayashi E, Goto Y (2000) Detection of quantitative trait loci for wood strength in Cryptomeria japonica. Can J For Res 30:1525–1533CrossRefGoogle Scholar
  25. Lauri P, Havlík P, Kindermann G, Forsell N, Böttcher H, Obersteiner M (2014) Woody biomass energy potential in 2050. Energy Policy 66:19–31CrossRefGoogle Scholar
  26. Li Y, Suontama M, Burdon RD, Dungey HS (2017) Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application. Tree Genet Genomes 13:60CrossRefGoogle Scholar
  27. Moriguchi Y, Uchiyama K, Ueno S, Ujino-Ihara T, Matsumoto A, Iwai J, Miyajima D, Saito M, Sato M, Tsumura Y (2016) A high-density linkage map with 2560 markers and its application for the localization of the male-sterile genes ms3 and ms4 in Cryptomeria japonica D. Don. Tree Genet Genomes 12:57CrossRefGoogle Scholar
  28. Muchero W, Guo J, DiFazio SP, Chen J-G, Ranjan P, Slavov GT, Gunter LE, Jawdy S, Bryan AC, Sykes R, Ziebell A, Klápště J, Porth I, Skyba O, Unda F, El-Kassaby YA, Douglas CJ, Mansfield SD, Martin J, Schackwitz W, Evans LM, Czarnecki O, Tuskan GA (2015) High-resolution genetic mapping of allelic variants associated with cell wall chemistry in Populus. BMC Genomics 16:24CrossRefGoogle Scholar
  29. Muñoz F, Sanchez L (2019) breedR: statistical methods for forest genetic resources analysts. R package version 0.12–2. Accessed 25 Mar 2019
  30. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev Genet 12:111–122CrossRefGoogle Scholar
  31. Newton PF (2003) Systematic review of yield responses of four North American conifers to forest tree improvement practices. For Ecol Manag 172:29–51CrossRefGoogle Scholar
  32. Ohba K (1993) Clonal forestry with Sugi (Cryptomeria japonica). In: Ahuja M-R, Libby WJ (eds) Clonal forestry II: conservation and application. Springer-Verlag Berlin Heidelberg, Berlin, pp 66–90CrossRefGoogle Scholar
  33. Pelgas B, Bousquet J, Meirmans PG, Ritland K, Isabel N (2011) QTL mapping in white spruce: gene maps and genomic regions underlying adaptive traits across pedigrees, years and environments. BMC Genomics 12:145CrossRefGoogle Scholar
  34. Pot D, Rodrigues J-C, Rozenberg P, Chantre G, Tibbits J, Cahalan C, Pichavant F, Plomion C (2006) QTLs and candidate genes for wood properties in maritime pine (Pinus pinaster Ait.). Tree Genet Genomes 2:10–24CrossRefGoogle Scholar
  35. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  36. Rae AM, Pinel MPC, Bastien C, Sabatti M, Street NR, Tucker J, Dixon C, Marron N, Dillen SY, Taylor G (2008) QTL for yield in bioenergy Populus: identifying GxE interactions from growth at three contrasting sites. Tree Genet Genomes 4:97–112CrossRefGoogle Scholar
  37. Rae AM, Street NR, Robinson KM, Harris N, Taylor G (2009) Five QTL hotspots for yield in short rotation coppice bioenergy poplar: the poplar biomass loci. BMC Plant Biol 9:23CrossRefGoogle Scholar
  38. Raymond C (2011) Genotype by environment interactions for Pinus radiata in New South Wales, Australia. Tree Genet Genomes 7:819–833CrossRefGoogle Scholar
  39. Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624CrossRefGoogle Scholar
  40. Sun L, Wang Y, Yan X, Cheng T, Ma K, Yang W, Pan H, Zheng C, Zhu X, Wang J, Wu R, Zhang Q (2014) Genetic control of juvenile growth and botanical architecture in an ornamental woody plant, Prunus mume Sieb. Et Zucc. As revealed by a high-density linkage map. BMC Genet 15:S1CrossRefGoogle Scholar
  41. Tani N, Takahashi T, Iwata H, Mukai Y, Ujino-Ihara T, Matsumoto A, Yoshimura K, Yoshimaru H, Murai M, Nagasaka K et al (2003) A consensus linkage map for sugi (Cryptomeria japonica) from two pedigrees, based on microsatellites and expressed sequence tags. Genetics 165:1551–1568PubMedPubMedCentralGoogle Scholar
  42. Tassinari RR, Vilela RMD, Fonseca SF, Ferreira AC, Keiko TE, Bonfim S-JO, Dario G (2016) Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus. New Phytol 213:1287–1300Google Scholar
  43. Taylor J, Verbyla A (2011) R package wgaim: QTL analysis in bi-parental populations using linear mixed models. J Stat Softw 40:1–18CrossRefGoogle Scholar
  44. Thavamanikumar S, McManus LJ, Ades PK, Bossinger G, Stackpole DJ, Kerr R, Hadjigol S, Freeman JS, Vaillancourt RE, Zhu P et al (2014) Association mapping for wood quality and growth traits in Eucalyptus globulus ssp. globulus Labill identifies nine stable marker-trait associations for seven traits. Tree Genet Genomes 10:1661–1678CrossRefGoogle Scholar
  45. Thumma BR, Baltunis BS, Bell JC, Emebiri LC, Moran GF, Southerton SG (2010) Quantitative trait locus (QTL) analysis of growth and vegetative propagation traits in Eucalyptus nitens full-sib families. Tree Genet Genomes 6:877–889CrossRefGoogle Scholar
  46. Tsumura Y (2011) Cryptomeria. In: Kole C (ed) Wild crop relatives: genomic and breeding resources: forest trees. Springer-Verlag, Berlin Heidelberg, Berlin, pp 49–63CrossRefGoogle Scholar
  47. Tsumura Y, Kado T, Takahashi T, Tani N, Ujino-Ihara T, Iwata H (2007) Genome scan to detect genetic structure and adaptive genes of natural populations of Cryptomeria japonica. Genetics 176:2393–2403CrossRefGoogle Scholar
  48. Tsushima S, Koga S, Oda K, Shiraishi S (2005) Growth and wood properties of sugi (Cryptomeria japonica) cultivars planted in the Kyushu region. Journal of the Japan Wood Research Society 51:394–401CrossRefGoogle Scholar
  49. Uchiyama K, Iwata H, Moriguchi Y, Ujino-Ihara T, Ueno S, Taguchi Y, Tsubomura M, Mishima K, Iki T, Watanabe A, Futamura N, Shinohara K, Tsumura Y (2013) Demonstration of genome-wide association studies for identifying markers for wood property and male strobili traits in Cryptomeria japonica. PLoS One 8:e79866CrossRefGoogle Scholar
  50. Ueno S, Moriguchi Y, Uchiyama K, Ujino-Ihara T, Futamura N, Sakurai T, Shinohara K, Tsumura Y (2012) A second generation framework for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica. BMC Genomics 13:136CrossRefGoogle Scholar
  51. Ujino-Ihara T, Iwata H, Taguchi Y, Tsumura Y (2012) Identification of QTLs associated with male strobilus abundance in Cryptomeria japonica. Tree Genet Genomes 8:1319–1329CrossRefGoogle Scholar
  52. Ukrainetz NK, Ritland K, Mansfield SD (2008) Identification of quantitative trait loci for wood quality and growth across eight full-sib coastal Douglas-fir families. Tree Genet Genomes 4:159–170CrossRefGoogle Scholar
  53. van Ooijen J (2006) JoinMap 4, software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen 33 (10.1371)Google Scholar
  54. Verbyla AP, Cullis BR (2012) Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theor Appl Genet 125:933–953CrossRefGoogle Scholar
  55. Verbyla AP, Cullis BR, Thompson R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theor Appl Genet 116:95–111CrossRefGoogle Scholar
  56. Yang H, Liu T, Xu B, Liu C, Zhao F, Huang S (2015) QTL detection for growth and form traits in three full-sib pedigrees of Pinus elliottii var. elliottii x P. caribaea var. hondurensis hybrids. Tree Genet Genomes 11:130CrossRefGoogle Scholar
  57. Yoshimaru H, Ohba K, Tsurumi K, Tomaru N, Murai M, Mukai Y, Suyama Y, Tsumura Y, Kawahara T, Sakamaki Y (1998) Detection of quantitative trait loci for juvenile growth, flower bearing and rooting ability based on a linkage map of sugi (Cryptomeria japonica D. Don). Theor Appl Genet 97:45–50CrossRefGoogle Scholar
  58. Zobel BJ, Jett JB (1995) Genetics of wood production. Springer Science & Business Media, BerlinCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Hideki Mori
    • 1
  • Saneyoshi Ueno
    • 1
  • Tokuko Ujino-Ihara
    • 1
  • Takeshi Fujiwara
    • 2
  • Kana Yamashita
    • 1
  • Seiichi Kanetani
    • 3
  • Ryota Endo
    • 4
  • Asako Matsumoto
    • 1
    Email author
  • Kentaro Uchiyama
    • 1
  • Yukari Matsui
    • 5
  • Takahiro Yoshida
    • 1
  • Yoshimi Sakai
    • 3
  • Yoshinari Moriguchi
    • 6
  • Ryouichi Kusano
    • 5
  • Yoshihiko Tsumura
    • 1
    • 7
  1. 1.Forestry and Forest Products Research InstituteTsukubaJapan
  2. 2.Forest Bio-Research Center, Forestry and Forest Products Research InstituteHitachiJapan
  3. 3.Kyushu Research Center, Forestry and Forest Products Research InstituteKumamotoJapan
  4. 4.Forestry Research Institute, Chiba Prefectural Agriculture and Forestry Research CenterSammuJapan
  5. 5.Kumamoto Prefecture Forestry Research CenterKumamotoJapan
  6. 6.Graduate School of Science and TechnologyNiigata UniversityNishi-kuJapan
  7. 7.Faculty of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

Personalised recommendations