Comparison of weighting in two-stage analysis of plant breeding trials

Crop Science

Volumn 49, Issue 6, 2009, Pages 1977-1988

  Mohring J ^a   Piepho, H P ^a  

^a UNIVERSITY OF HOHENHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 70749085124     PISSN: 0011183X     EISSN: None     Source Type: Journal    
DOI: 10.2135/cropsci2009.02.0083     Document Type: Article

References (32)

1. Bernardo, R. 1992. Weighted vs. unweighted mean performance of varieties across environments. Crop Sci. 32:490-492.
2. Bernardo, R. 1994. Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci. 34:20-25.
3. Calińiski, T., S. Czajka, Z. Kaczmarek, P. Krajewski, and W. Pilarczyk. 2005. Analyzing multi-environment variety trials using randomization-derived mixed models. Biometrics 61:448-455.
4. Cullis, B.R., F.M. Thomson, J.A. Fischer, A.R. Gilmour, and R. Thompson. 1996. The analysis of the NSW wheat variety database. I. Modelling trial error variance. Theor. Appl. Genet. 92:21-27.
5. Edwards, J.W., and J.L. Jannink. 2006. Bayesian modeling of heterogeneous error and genotype × environment interaction variances. Crop Sci. 46:820-833.
6. Frensham, A., B.R. Cullis, and A. Verbyla. 1997. Genotype by environment variance heterogeneity in a two-stage analysis. Biometrics 53:1373-1383.
7. Gilmour, A.R., B.R. Cullis, B.J. Gogel, S.J. Welham, and R. Thompson. 2005. ASReml user guide, release 2.0. VSN Int., Hemel Hempstead, UK.
8. Gilmour, A.R., B.R. Cullis, and A.P. Verbyla. 1997. Accounting for natural and extraneous variation in the analysis of fi eld experiments. J. Agric. Biol. Environ. Stat. 2:269-293.
9. Gilmour, A.R., R. Thompson, and B.R. Cullis. 1995. Average Information REML: An effi cient algorithm for variance parameter estimation in linear mixed models. Biometrics 51:1440-1450.
10. Hardy, O.J., and X. Vekemans. 2002. SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population level. Mol. Ecol. Notes 2:618-620.
11. Hühn, M. 1997. Weighted means are unnecessary in cultivar performance trials. Crop Sci. 37:1745-1750.
12. John, N.A., and E.R. Williams. 1995. Cyclic and computer generated designs. 2nd ed. Chapman and Hall, London.
13. Little, R.J.A., and R.D. Rubin. 2002. Statistical analysis with missing data. Second ed. John Wiley & Sons, New York.
14. Patterson, H.D. 1997. Analysis of series of variety trials. p. 139-161. In R.A. Kempton and P.N. Fox (ed.) Statistical methods for plant variety evaluation. Chapman and Hall, London, UK.
15. Patterson, H.D., and R. Thompson. 1971. Recovery of interblock information when block sizes are unequal. Biometrika 58:545-554.
16. Piepho, H.P. 1997. Analyzing genotype-environment data by mixed models with multiplicative eff ects. Biometrics 53:761-766.
17. Piepho, H.P. 1998. Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures. Theor. Appl. Genet. 97:195-201.
18. Piepho, H.P., and V. Michel. 2000. Überlegungen zur regionalen Auswertung von Landessortenversuchen. Informatik. Biometrie und Epidemiologie in Medizin und Biologie 31:123-136.
19. Piepho, H.P., and J. Möhring. 2006. Selection in cultivar trials-Is it ignorable? Crop Sci. 146:193-202.
20. Piepho, H.P., and J. Möhring. 2007. On weighting in two-stage analyses of series of experiments. Biuletyn Oceny Odmian 32:109-121.
21. Piepho, H.P., J. Möhring, A.E. Melchinger, and A. Büchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209-228.
22. Piepho, H.P., and E.R. Williams. 2006. A comparison of experimental designs for selection in breeding trials with nested treatment structure. Theor. Appl. Genet. 113:1505-1513.
23. Piepho, H.P., E.R. Williams, and M. Fleck. 2006. A note on the analysis of designed experiments with complex treatment structure. HortScience 41:446-452.
24. Robert-Granie, C., B. Bonaiti, D. Boichard, and A. Barbat. 1999. Accounting for variance heterogeneity in French dairy cattle genetic evaluation. Livest. Prod. Sci. 60:343-357.
25. Shukla, G.K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237-245.
26. Smith, A.B., B.R. Cullis, and R. Thompson. 2005. The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. J. Agric. Sci. 143:449-462.
27. Smith, A.B., B.R. Cullis, and A.R. Gilmour. 2001a. Analysing variety by environment data using multiplicative mixed models and adjustments for spatial fi eld trend. Biometrics 57:1138-1147.
28. Smith, A.B., B.R. Cullis, and A.R. Gilmour. 2001b. The analysis of crop variety evaluation data in Australia. Australian and New Zealand Journal of Statistics 43:129-145.
29. Stram, D.O., and J.W. Lee. 1994. Variance components testing in the longitudinal mixed eff ects model. Biometrics 50:1171-1177.
30. Van Houwelingen, H.C., L.R. Arends, and T. Stijnen. 2002. Advanced methods in meta-analysis: Multivariate approach and meta-regression. Stat. Med. 21:589-624.
31. Wricke, G. 1962. Über eine Methode zur Erfassung der ökologischen Streubreite. Zeitschrift für Pfl anzenzüchtung 47:92-96.
32. Yu, J., G. Pressoir, W.H. Briggs, I.V. Bi, M. Yamasaki, J.F. Doebley, M.D. McMullen, B.S. Gaut, D.M. Nielsen, J.B. Holland, S. Kresovich, and E.S. Buckler. 2006. A unifi ed mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38:203-208.