Abstract
To select genotypes with stable growth in diverse environments, tree breeders use multisite trials to evaluate genotypic stability and adaptation. Since genotype-by-environment (G × E) interaction effects vary with age, most multisite trials focus only on site effects, ignoring age effects. Liriodendron tulipifera trees are valuable due to their rapid growth and high-quality wood. Currently, multisite trials involving L. tulipifera plants are rare and the lack of data on G × E interaction effects impedes its selection. In this study, to explore the better performance and G × E pattern of L. tulipifera across ages, the growth traits (tree height, H; and diameter at breast height, DBH) of trees that were of five consecutive ages and grown on progeny-testing plantations were studied for 27 open-pollinated families at three sites. The results showed that the heritability of DBH was greater than that of H at almost all ages, and the individual breeding value ranking differed across sites and ages. The additive genetic correlations (rA) between different site pairs were relatively small and varied with age, indicating an age trend for G × E, and showed a difference in traits. It was found that the absolute differences in some monthly average climatic indicators correlated with the G × E. Based on a comprehensive analysis considering stability and productivity, four elite families were identified. These results could aid in selecting stable, adaptable L. tulipifera genotypes and provide a reference for evaluating G × E interaction effects in multiage, multisite trials of other tree species.
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References
Apiolaza LA (2012) Basic density of radiata pine in New Zealand: genetic and environmental factors. Tree Genet Genome 8:87–96. https://doi.org/10.1007/s11295-011-0423-1
Arief VN, DeLacy IH, Jose C, Thomas P, Ravi S, Hans-J B, Tian T, Basford KE, Dieters MJ (2015) Evaluating testing strategies for plant breeding field trials: redesigning a CIMMYT international wheat nursery to provide extra genotype connection across cycles. Crop Sci 55:164–177. https://doi.org/10.2135/cropsci2014.06.0415
Arief VN, Desmae H, Hardner C, DeLacy IH, Gilmour A, Bull JK, Basford KE (2019) Utilization of multiyear plant breeding data to better predict genotype performance. Crop Sci 59:480–490. https://doi.org/10.2135/cropsci2018.03.0182
Bajgain P, Zhang X, Anderson JA (2020) Dominance and G×E interaction effects improve genomic prediction and genetic gain in intermediate wheatgrass (Thinopyrum intermedium). Plant Genome 13:e20012. https://doi.org/10.1002/tpg2.20012
Berlin M, Jansson G, Högberg K-A (2014) Genotype by environment interaction in the southern Swedish breeding population of Picea abies using new climatic indices. Scand J for Res 30:112–121. https://doi.org/10.1080/02827581.2014.978889
Bian L, Shi J, Zheng R, Chen J, Wu HX (2014) Genetic parameters and genotype–environment interactions of Chinese fir (Cunninghamia lanceolata) in Fujian Province. Can J for Res 44:582–592. https://doi.org/10.1139/cjfr-2013-0427
Burdon RD (1977) Genetic correlation as concept for studying genotype-environment interactions in forest breeding. Silvae Genet 26(5–6):168–175
Burdon RD, Li Y, Suontama M, Dungey HS (2017) Genotype × site × silviculture interactions in radiata pine: knowledge, working hypotheses and pointers for research. N Z J for Sci 47:6. https://doi.org/10.1186/s40490-017-0087-1
Chen ZQ, Harry BK, Wu HX (2017) Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden. Tree Genet Genome 13:25. https://doi.org/10.1007/s11295-017-1103-6
Chen J, Hao Z, Guang X et al (2019) Liriodendron genome sheds light on angiosperm phylogeny and species-pair differentiation. Nature Plants 5:18–25. https://doi.org/10.1038/s41477-018-0323-6
Colombari FJ, de Resende MD, de Morais OP, de Castro AP, Guimarães ÉP, Pereira JA, Utumi MM, Breseghello F (2013) Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica 192:117–129. https://doi.org/10.1007/s10681-013-0922-2
Cullis BR, Jefferson P, Thompson R, Smith AB (2014) Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme. Theor Appl Genet 127:2193–2210. https://doi.org/10.1007/s00122-014-2373-0
de Souza BM, Freitas MLM, Sebbenn AM, Gezan SA, Zanatto B, Zulian DF, Lopes MTG, Longui EL, Guerrini IA, de Aguiar AV (2020) Genotype-by-environment interaction in Corymbia citriodora (Hook.) K.D. Hill, & L.A.S. Johnson progeny test in Luiz Antonio, Brazil. For Ecol Manage 460. https://doi.org/10.1016/j.foreco.2019.117855
Des Marais DL, Hernandez KM, Juenger TE (2013) Genotype-by-environment interaction and plasticity: exploring genomic responses of plants to the abiotic environment. Annu Re Ecol Evol Syst 44:5–29. https://doi.org/10.1146/annurev-ecolsys-110512-135806
Diaz SI, de Oliveira HN, Bezerra LAF, Lobo RB (2011) Genotype by environment interaction in Nelore cattle from five Brazilian states. Genet Mol Biol 34:435–442. https://doi.org/10.1590/S1415-47572011005000024
Dieters MJ, White TL, Hodge GR (1995) Genetic parameter estimates for volume from full–sib tests of slash pine (Pines elliottii). Can J for Res 25:1397–1408. https://doi.org/10.1139/x95-152
Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40
El-Dien OG, Ratcliffe B, Klápště J, Chen C, Porth I, El-Kassaby YA (2015) Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16:1–16. https://doi.org/10.1186/s12864-015-1597-y
Evangelista JSPC, Alves RS, Peixoto MA, Resende MDVD, Teodoro PE, Silva FLD, Bhering LL (2021) Soybean productivity, stability, and adaptability through mixed model methodology. Ciên Rural 51(2):e20200406. https://doi.org/10.1590/0103-8478cr20200406
Frutos E, Galindo MP, Leiva V (2014) An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch Environ Res Risk Assess 28:1629–1641. https://doi.org/10.1007/s00477-013-0821-z
Gauch HG, Zobel RW (1996) AMMI analysis of yield trials. In: Kan MS, Gauch HG (ed) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 85–122. https://doi.org/10.1201/9781420049374.ch4
Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2008) ASReml user guide release 3.0. VSN International Ltd., Hemel Hempstead, UK. https://doi.org/www.vsni.co.uk
Gu Z, Roland E, Matthias S (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847. https://doi.org/10.1093/bioinformatics/btw313
Haleh H, Anders F, Johan K, Wu HX (2018) Estimation of genetic parameters, provenance performances, and genotype by environment interactions for growth and stiffness in lodgepole pine (Pinus contorta). Scand J for Res 34(5):1–11. https://doi.org/10.1080/02827581.2018.1542025
Hiraoka Y, Miura M, Fukatsu E, Iki T, Yamanobe T, Kurita M, Isoda K, Kubota M, Takahashi M (2019) Time trends of genetic parameters and genetic gains and optimum selection age for growth traits in sugi (Cryptomeria japonica) based on progeny tests conducted throughout Japan. J for Res 24:303–312. https://doi.org/10.1080/13416979.2019.1661068
Holland JB, Nyquist WE, Cervantes-Martinez CT (2010) Plant Breeding Reviews. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470650202.ch2
Isik F, Holland J, Maltecca C (2017) Genetic data analysis for plant and animal breeding. Springer International Publishing, Switzerland
Kusnandar D, Galwey NW, Hertzler GL, Butcher TB (1998) Age trends in variances and heritabilities for diameter and height in maritime pine (Pinus pinaster AIT.) in Western Australia. Silvae Genet 47(2–3):136–141
Lauer E, Sims A, McKeand S, Isik F (2021) Genetic parameters and genotype-by-environment interactions in regional progeny tests of Pinus taeda L. in the southern USA. For Sci 67(1):60–71. https://doi.org/10.1093/forsci/fxaa035
Li Z, Wang Z (2001) Variation and selection of interspecific hybrid in Liriodendron. J Northwest For Univ 16:5–9. https://doi.org/10.3969/j.issn.1001-7461.2001.02.002
Li H, Chen L, Liang C, Huang M (2005) A case study on provenance testing of tulip tree (Liriodendron spp.). China For Sci Technol 19:13–16. https://doi.org/10.3969/j.issn.1000-8101.2005.05.005
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 Genome 13:1–18. https://doi.org/10.1007/s11295-017-1144-x
Lin Y, Yang H, Ivković M, Gapare WJ, Matheson AC, Wu HX (2013) Effect of genotype by spacing interaction on radiata pine genetic parameters for height and diameter growth. For Ecol Manage 304:204–211. https://doi.org/10.1016/j.foreco.2013.05.015
Liu H, Shen X, Ceng Y (1991) Genetic variations in morphological and growth characteristics of Chinese, American tulip-tree and their hybrid. J Zhejiang For Sci Technol 11(5):18–22
Lu C, Li B, Zheng Y (2015) Analysis on differential expression of cold resistance related genes of Liriodendron chinense under low temperature stress. J Plant Resour Environ 24:25–31. https://doi.org/10.3969/j.issn.1674-7895.2015.03.04
Möhring J, Piepho HP (2009) Comparison of weighting in two-stage analysis in plant breeding trials. Crop Sci 49:1977–1988. https://doi.org/10.2135/cropsci2009.02.0083
Olivoto T, Nardino M (2020) MGIDI: towards an effective multivariate selection in biological experiments. Bioinformatics 37(10):1383–1389. https://doi.org/10.1093/bioinformatics/btaa981
Olivoto T, Lúcio ADC, Jarman S (2020) metan: an R package for multi-environment trial analysis. Methods Ecol Evol 11:783–789. https://doi.org/10.1111/2041-210X.13384
Piepho HP, Möhring J, Schulz-Streeck T, Ogutu JO (2012) A stage-wise approach for the analysis of multi environment trials. Biom J Biom Z 54:844–860. https://doi.org/10.1002/bimj.201100219
Raymond CA (2011) Genotype by environment interactions for Pinus radiata in New South Wales, Australia. Tree Genet Genomes 7:819–833. https://doi.org/10.1007/s11295-011-0376-4
Resende MD (2007) Matemática e estatística na análise de experimentos e no melhoramento genético. Embrapa Florestas, Colombo
Roth BE, Jokela EJ, Martin TA, Huber DA, White TL (2007) Genotype × environment interactions in selected loblolly and slash pine plantations in the southeastern United States. For Ecol Manage 238:175–188. https://doi.org/10.1016/j.foreco.2006.10.010
Rweyongeza DM (2011) Pattern of genotype-environment interaction in Picea glauca (Moench) Voss in Alberta, Canada. Ann for Sci 68:245–253. https://doi.org/10.1007/s13595-011-0032-z
Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237–245. https://doi.org/10.1038/hdy.1972.87
Sierra-Lucero V, Huber DA, McKeand SE, White TL, Rockwood DL (2003) Genotype-by-environment interaction and development considerations for families from Florida provenances of loblolly pine. For Genet 10(2):85–92
Stackpole DJ, Vaillancourt RE, de Aguigar M, Potts BM (2010) Age trends in genetic parameters for growth and wood density in Eucalyptus globulus. Tree Genet Genome 6:179–193. https://doi.org/10.1007/s11295-009-0239-4
Team R Core (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria
Wang Z (2003) The review and outlook on hybridization in tulip tree breeding in China. J Nanjing for Univ (natural Sciences Edition) 27:76–78. https://doi.org/10.3969/j.issn.1000-2006.2003.03.019
White TL, Adams WT, Neale DB (2007) Forest genetics. CAB International, Cambridge
Wu HX, Eldridge KG, Matheson AC, Powell MP, McRae TA (2007) Achievement in forest tree improvement in Australia and New Zealand 8. Successful introduction and breeding of radiata pine to Australia. Aust for 70:215–225. https://doi.org/10.1080/00049158.2007.10675023
Wu HX, Ker R, Chen ZQ, Ivkovic M (2021) Balancing breeding for growth and fecundity in radiata pine (Pinus radiata D. Don) breeding programme. Evol Appl 14:834–846. https://doi.org/10.1111/eva.13164
Xia H, Si W, Hao Z, Zhong W, Zhu S, Tu Z, Zhang C, Li H (2021) Dynamic changes in the genetic parameters of growth traits with age and their associations with heterosis in hybrid Liriodendron. Tree Genet Genome 17. https://doi.org/10.1007/s11295-021-01504-z
Yan W (2001) GGEbiplot-a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J 93:1111–1118. https://doi.org/10.2134/agronj2001.9351111x
Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:641–653. https://doi.org/10.2135/cropsci2006.06.0374
Yuan C, Zhang Z, Jin G, Zheng Y, Zhou Z, Sun L, Tong H (2021) Genetic parameters and genotype by environment interactions influencing growth and productivity in Masson pine in east and central China. For Ecol Manage 487:118991. https://doi.org/10.1016/j.foreco.2021.118991
Zas R, Merlo E, Díaz R, Fernández-López J (2003) Stability across sites of Douglas-fir provenances in northern Spain. For Genet 10(1):71–82
Zhang W, Hu J, Yang Y, Lin Y (2018) One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis. J for Res 31:123–130. https://doi.org/10.1007/s11676-018-0846-8
Zhang H, Zhou X, Gu W, Wang L, Li W, Gao Y, Wu L, Guo X, Tigabu M, Xia D, Chiang VL, Yang C, Zhao X (2021) Genetic stability of Larix olgensis provenances planted in different sites in northeast China. For Ecol Manage 485:118988. https://doi.org/10.1016/j.foreco.2021.118988
Zhao X, Xia H, Wang X, Wang C, Liang D, Li K, Liu G (2015) Variance and stability analyses of growth characters in half-sib Betula platyphylla families at three different sites in China. Euphytica 208:173–186. https://doi.org/10.1007/s10681-015-1617-7
Zhou M, Chen P, Shang X, Yang W, Fang S (2021) Genotype-environment interactions for tree growth and leaf phytochemical content of Cyclocarya paliurus (Batal.) Iljinskaja. Forests 12 (6):735. https://doi.org/10.3390/f12060735
Acknowledgements
We thank the National Natural Science Foundation of China (31770718, 31470660) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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HX, LCY, ZHT, CGZ, ZYH and WPZ participated in collecting and recording raw data. HX, LCY and ZHT arranged and reorganized the data. HX completed the data analysis and wrote the paper. HGL conceived the project; guided the experimental design and progeny tests; gave comments on data analysis; and revised the manuscript. All authors read and approved the final manuscript.
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Xia, H., Yang, L., Tu, Z. et al. Growth performance and G × E interactions of Liriodendron tulipifera half-sib families across ages in eastern China. Eur J Forest Res 141, 1089–1103 (2022). https://doi.org/10.1007/s10342-022-01494-0
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DOI: https://doi.org/10.1007/s10342-022-01494-0