Testing for nonlinear genotype x environment interactions

被引:1
作者
Yang, Rong-Cai [1 ,2 ]
机构
[1] Alberta Agr & Forestry, Crop Genet Sect, Edmonton, AB T6H 5T6, Canada
[2] Univ Alberta, Dept Agr Food & Nutr Sci, Edmonton, AB T6G 2P5, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
MODEL; ADAPTATION; PLASTICITY;
D O I
10.1002/csc2.20268
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
The responses of different genotypes to an environmental gradient are often nonlinear and nonparallel. Current tests for differential genotypic responses are based largely on linear regression models (stability analysis) or on evaluations of all quadruples for crossover interactions (COIs) from a two-way genotype x environment (G x E) table if the environments are unquantified. The objective of this study was to develop a new statistical analysis for comparing nonlinear genotypic response curves over an environmental gradient. We first conducted an investigation to find the points where the two nonparallel curves intersected. If the intersection points lie within the attainable environmental range, the two nonparallel curves involve COI; if the points lie at the boundaries or outside the attainable range, the nonparallel curves do not involve COI. We then developed statistical tests for comparing a full and a reduced model describing the two nonparallel curves. The tests were used to analyze a wheat (Triticum aestivum L.) germination test (WGT) data under the reciprocal of a linear function and a barley (Hordeum vulgare L.) cultivar trial (BCT) data under Cauchy function. The WGT analysis shows that at least one pair of cultivars involve COI over the temperature gradient, which went undetected by a previous test based on all possible quadruples. The BCT analysis revealed that 56% of 780 possible pairs of 40 genotypes differ significantly from each other, providing more insights into the patterns of complex G x E interactions. Our analysis is therefore a viable alternative to the existing procedures.
引用
收藏
页码:3127 / 3140
页数:14
相关论文
共 33 条
[1]   Nonlinear Regression Models and Applications in Agricultural Research [J].
Archontoulis, Sotirios V. ;
Miguez, Fernando E. .
AGRONOMY JOURNAL, 2015, 107 (02) :786-798
[2]  
AZZALINI A, 1984, J ROY STAT SOC B MET, V46, P335
[3]  
Baker R.J., 1987, P 2 INT C QUANT GEN, P492
[4]   TESTS FOR CROSSOVER GENOTYPE-ENVIRONMENTAL INTERACTIONS [J].
BAKER, RJ .
CANADIAN JOURNAL OF PLANT SCIENCE, 1988, 68 (02) :405-410
[5]   AIC under the framework of least squares estimation [J].
Banks, H. T. ;
Joyner, Michele L. .
APPLIED MATHEMATICS LETTERS, 2017, 74 :33-45
[6]   Using factor analytic models for joining environments and genotypes without crossover genotype x environment interaction [J].
Burgueno, Juan ;
Crossa, Jose ;
Cornelius, Paul L. ;
Yang, Rong-Cai .
CROP SCIENCE, 2008, 48 (04) :1291-1305
[7]  
Burnham KP., 2002, Model selection and multi-model inference: apractical information-theoretic approach, V2, DOI [10.1007/b97636, DOI 10.1007/B97636]
[8]   USING THE SHIFTED MULTIPLICATIVE MODEL TO SEARCH FOR SEPARABILITY IN CROP CULTIVAR TRIALS [J].
CORNELIUS, PL ;
SEYEDSADR, M ;
CROSSA, J .
THEORETICAL AND APPLIED GENETICS, 1992, 84 (1-2) :161-172
[9]   Analysis of Genotype x Environment Interaction (G x E) Using SAS Programming [J].
Dia, Mahendra ;
Wehner, Todd C. ;
Arellano, Consuelo .
AGRONOMY JOURNAL, 2016, 108 (05) :1838-1852
[10]   STABILITY PARAMETERS FOR COMPARING VARIETIES [J].
EBERHART, SA ;
RUSSELL, WA .
CROP SCIENCE, 1966, 6 (01) :36-&