HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Yang, R.-C. Search for Related Content PubMed Articles by Yang, R.-C. Agricola Articles by Yang, R.-C. Related Collections Statistics Biometrics Plant and Environment Interactions

CROP BREEDING & GENETICS

Mixed-Model Analysis of Crossover Genotype–Environment Interactions

Alberta Agriculture, Food and Rural Development, no. 300, 7000-113 St., Edmonton, AB, Canada T6H 5T6, and Dep. of Agricultural, Food and Nutritional Science, Univ. of Alberta, Edmonton, AB, Canada T6G 2P5

^* Corresponding author (rong-cai.yang{at}ualberta.ca).

Genotype–environment interactions (GEI) are important in crop improvement if genotype ranks change across environments. Current tests for crossover (rank changing) interactions (COI) assume that effects are all fixed or all random. The objective of this study was to develop a new test for COI under the model with a mixture of fixed and random genotypic, environmental, and GEI effects. The key part of this new test is that the difference between a pair of genotypes at a random environment or the difference between a pair of environments for a random genotype involves the linear combinations (predictable functions) of both best linear unbiased estimates (BLUEs) of fixed effects and best linear unbiased predictors (BLUPs) of random effects. The predictable functions are used in the same way as the usual estimable functions for the fixed effects in hypothesis testing except that the BLUPs of random effects are adjusted by accounting for the uncertainty arising from the distributions of these effects. Strategies are proposed to implement the procedure using the SAS system. The procedure was used to analyze barley (Hordeum vulgare L.) and field pea (Pisum sativum L.) cultivar trials. The analyses show that treating random effects as fixed, as may happen with previous analysis procedures, results in detection of more COI than mixed- or random-effect models. Therefore, significant COI may be overemphasized when random GEI effects are treated as fixed.

Abbreviations: BLUE, best linear unbiased estimator • BLUP, best linear unbiased predictor • COI, crossover interactions • EBLUE, empirical best linear unbiased estimator • EBLUP, empirical best linear unbiased predictor • GEI, genotype–environment interactions • LR, likelihood ratio • MET, multiple-environment trial • ML, maximum likelihood • RCBD, randomized complete block design • REML, restricted maximum likelihood.

This article has been cited by other articles:

				J. Burgueno, J. Crossa, P. L. Cornelius, and R.-C. Yang Using Factor Analytic Models for Joining Environments and Genotypes without Crossover Genotype x Environment Interaction Crop Sci., July 1, 2008; 48(4): 1291 - 1305. [Abstract] [Full Text] [PDF]