One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

Abstract

To improve multi-environmental trial (MET) analysis, a compound method—which combines factor analytic (FA) model with additive main effect and multiplicative interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplot—was conducted in this study. The diameter at breast height of 36 open-pollinated (OP) families of Pinus taeda at six sites in South China was used as a raw dataset. The best linear unbiased prediction (BLUP) data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data. The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot. BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method. AMMI analysis identified that two datasets had highly significant effects on the site, family, and their interactions, while BLUP data had a smaller residual error, but higher variation explaining ability and more credible stability than raw data. GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation, test-environment evaluation, and genotype evaluation. In addition, BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components. Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Keywords

Additive main effect and multiplicative interaction / Best linear unbiased prediction / GGE biplot / Genotype by environment interaction / Multi-environment trial

References

[1] Chen ZQ, Karlsson B, Wu H. Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden. Tree Genet Genomes, 2017, 13: 25.

[2] Costa e Silva J, Graudal L. Evaluation of an international series of Pinus kesiya provenance trials for growth and wood quality traits. For Ecol Manag, 2008, 255: 3477-3488.

[3] Costa e Silva J, Potts B, Dutkowski G. Genotype by environment interaction for growth of Eucalyptus globulus in Australia. Tree Genet Genomes, 2006, 2: 61-75.

[4] Costa ESJ, Dutkowski GW, Gilmour AR. Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can J For Res, 2001, 31(11): 1887-1893.

[5] Crossa J. Statistical analysis of multi-location trials. Adv Agron, 1990, 44: 55-85.

[6] Cullis BR, Jefferson P, Thompson R, Smith AB. 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, 2014, 127(10): 2193-2210.

[7] De Mendiburu F (2016) Agricolae: statistical procedures for agricultural research. R Package Version 1. pp 2–4

[8] Ding M, Wu HX. Application of GGE Biplot analysis to evaluate genotype (G), environment (E) and G × E interaction on Pinus radiata: a case of study. N Z J For Sci, 2008, 38(1): 132-142.

[9] Dutkowski GW (2005) Improved models for the prediction of breeding values in trees. Ph.D. Thesis. University of Tasmania, 79–107

[10] Dutkowski GW, Costa ESJ, Gilmour AR, Lopez GA. Spatial analysis methods for forest genetic trials. Can J For Res, 2002, 32(12): 2201-2214.

[11] Dutkowski GW, Costa ESJ, Gilmour AR, Wellendorf H, Aguiar A. Spatial analysis enhances modeling of a wide variety of traits in forest genetic trials. Can J For Res, 2006, 36(7): 1851-1870.

[12] Finlay KW, Wilkinson GN. The analysis of adaptation in a plant breeding programme. Aust J Agric Res, 1963, 14(6): 742-754.

[13] Frutos E, Galindo MP, Leiva V. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch Environ Res Risk Assess, 2014, 28(7): 1629-1641.

[14] Gauch HG, Zobel RW. Identifying mega-environments and targeting genotypes. Crop Sci, 1997, 37(2): 311-326.

[15] Gauch HG, Piepho HP, Annicchiarico P. Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci, 2008, 48(3): 866-889.

[16] Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml user guide release 4.0, 2016, Hemel: Vsn International Ltd.

[17] Ivković M, Gapare W, Yang H, Dutkowski G, Buxton P, Wu H. Pattern of genotype by environment interaction for radiata pine in southern Australia. Ann For Sci, 2015, 72: 391-401.

[18] Kelly AM, Smith AB, Eccleston JA, Cullis BR. The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci, 2007, 47(3): 1063-1070.

[19] Lin YZ. R and ASReml-R statistics, 2016, Beijing: China Forestry Publishing House 524 533

[20] Purchase JL (1997) Parametric analysis to described G × E interaction and yield stability in winter yield. Ph. D Thesis. Department of Agronomy, Faculty of Agriculture, University of Orange Free State, Bloemfontein, pp 4–83

[21] Sixto H, Salvia J, Barrio M, Ciria MP, Cañellas I. Genetic variation and genotype-environment interactions in short rotation Populus, plantations in southern Europe. New For, 2011, 42(2): 163-177.

[22] Smith A, Cullis B, Thompson R. Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics, 2001, 57(4): 1138-1147.

[23] Terrance ZY, Jayawickrama KJ. Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest. Tree Genet Genomes, 2008, 4(4): 677-692.

[24] Wang RH, Hu DH, Zheng HQ, Yan S, Wei RP. Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China. J For Res, 2016, 27(3): 659-664.

[25] Yan W. GGEbiplot-a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J, 2001, 93(5): 1111-1118.

[26] Yan W. Optimal use of biplots in analysis of multi-location variety test data. Acta Agron Sin, 2010, 36(11): 1805-1819.