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GENOMICS, MOLECULAR GENETICS & BIOTECHNOLOGY

Using Environmental Covariates to Explain Genotype x Environment and QTL x Environment Interactions for Agronomic Traits on Chromosome 3A of Wheat

^a Rice Exit. Station, Calif. Coop. Rice Res. Foundation, Biggs, CA 95917
^b Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583
^c Dep. of Biometry, Univ. of Nebraska, Lincoln, NE 68583
^d Departamento De Fitotecnia, Universidade Federal de Santa Maria, Centro de Ciências Rurais, Santa Maria, RS, Brazil 97105-900
^e Dep. of Natural Resources, Univ. of Nebraska, Lincoln, NE 68583
^f Dep. of Crop and Soil Sciences, Washington State Univ., Pullman, WA 99164
^g Dep. of Field Crops, Mustafa Kemal Univ., Antakya, Turkey 31034

^* Corresponding author (pbaenziger1{at}unl.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Genotype x environment interactions (GEI) and quantitative trait loci x environment interactions (QEI) are known to influence the expression of agronomic performance traits in wheat. The aim of this study was to provide biological and environmental explanations for large GEI and QEI known to affect the expression of genes on chromosome 3A of wheat (Triticum aestivum L.). Agronomic performance and molecular marker data available for a population of chromosome 3A recombinant inbred chromosome lines (RICLs-3A) in seven environments was used along with environmental covariate data to construct individual factorial regressions to explain GEI and QEI. Precipitation and temperature before anthesis had the greatest influence on agronomic performance traits for the RICLs-3A, and explained a sizeable portion of the total GEI for those traits. Individual molecular marker x environmental covariate interactions explained a large portion of the total marker x environment interactions for several agronomic traits. Seventy-six percent of the QEI for a major grain yield quantitative trait locus (QTL) was explained by the effect of temperature during preanthesis growth on dissimilar QTL genotype differentials across testing environments. Environmental covariates provided a strong basis for explaining QEI using marker x environmental covariate interactions.

Abbreviations: GEI, genotype x environment interaction • QEI, Quantitative trait locus x environment interaction • QTL, quantitative trait locus • RICLs, recombinant inbred chromosome lines

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
OUR CURRENT understanding of QTLs associated with agronomic performance in wheat and other crop plants is limited. The majority of studies investigating QTLs for agronomic traits indicate inconsistent QTL detection across different experiments, environments, and populations (Beavis and Keim, 1996; Paterson et al., 1991), although some report consistent QTL detection (Stuber et al., 1992). Beavis (1994) attributed inconsistency of QTL detection across populations to the effects of genetic background (i.e., epistasis), differences between sets of parents used to create mapping populations (i.e., segregation of different sets of QTLs), different levels of inbreeding present between populations, and effects of population size.

Inconsistent QTL detection across environments is also the result of QEI, which presumably represent the genetic factors underlying the GEI observed in line-based phenotypes (Beavis and Keim, 1996). With data collected from multiple location agronomic performance trials on a core set of genotypes, GEI can be detected by analysis of variance and various statistical procedures that measure genotype stability (Kang, 1993; Lin et al., 1986). To determine genetic factors responsible for GEI, QEI can be evaluated on the basis of agronomic data collected on a mapping population in multiple location trials and comparing QTL detection across environments by analysis of variance to test marker locus x environment interactions (Sari-Gorla et al., 1997), and by the regression of marker genotype mean on an environmental index to obtain and discern if their linear regression coefficients are significantly different (Campbell et al., 2003).

Methods used to detect GEI and QEI are useful to determine genetic factors associated with GEI, but provide no explanation of the environmental factors involved. If climatic data are available for precipitation, temperature, and solar radiation, Factorial Regression Models (van Eeuwijk et al., 1996) and Partial Least Squares Models (Aastveit and Martens, 1986) can be used to determine the degree to which each of these factors influence GEI and QEI (Crossa et al., 1999). Hence, just as molecular markers are commonly used to model the effects of chromosomal segments (QTLs) on a particular quantitative trait, climatic data can also be used to model particular aspects of the environment that contribute to the differential performance of genotypes across a range of testing environments. Using factorial regression models, Crossa et al. (1999) was the first to explain QEI and found that temperature differences across environments accounted for a large portion of the QEI detected in a tropical maize (Zea mays L.) mapping population.

In wheat, chromosome 3A is known to contain QTLs for agronomic performance traits that are sensitive to different environmental conditions. Using a population of chromosome 3A recombinant inbred chromosome lines (RICLs-3A) evaluated in seven Nebraska environments, Campbell et al. (2003) detected significant QEI for grain yield, kernels per square meter, grain volume weight, plant height, 1000-kernel weight, spikes per square meter, and kernels per spike. A major QTL (QGyld.unl.3A.2) was detected for grain yield and kernels per square meter, but significant QEI was only detected for grain yield. This QEI was evident in the degree to which the allelic differential at the QTL varied among the environments for grain yield. However, the specific environmental cause(s) of this QEI displayed by QGyld.unl.3A.2 and QTLs for other traits were not known.

The objectives of this study were to use factorial regression models; agronomic and molecular genotype data; and the environmental covariates daily mean temperature, precipitation, and solar radiation recorded in each test environment to (i) detect which of these three environmental covariates may account for GEI by testing individual genotype x environmental covariate interactions and (ii) detect marker x environmental covariate interactions that provide explanations of variable QTL genotypic differences across environments. Agronomic and molecular data were used from a population of 95 RICLs-3A evaluated in agronomic performance trials previously conducted in seven environments in Nebraska (Campbell et al., 2003).

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Agronomic and Molecular Data
A population of 95 chromosome 3A recombinant inbred chromosome lines (RICLs-3A) of wheat was evaluated at Lincoln in 1999 to 2001, Mead in 2000 and 2001, and Sidney, NE, in 2000 and 2001 for a total of seven environments (Campbell et al., 2003). The RICLs-3A population was derived from a cross between cv. Cheyenne (CNN) and the chromosome substitution line Cheyenne (Wichita 3A) [CNN (WI3A)]. The environments represented diverse agroecological zones of wheat production in Nebraska (Peterson, 1992). Agronomic data were collected for grain yield, kernels per square meter, grain volume weight, plant height, 1000-kernel weight, spikes per square meter, and kernels per spike by means of a four-replicate, randomized complete block design in 1999 and four-replicate, incomplete block designs in 2000 and 2001.

The 95 RICLs-3A were genotyped by 20 markers consisting of 15 restriction fragment length polymorphisms (RFLP) and five microsatellite markers to construct a linkage map of chromosome 3A (Campbell et al., 2003). QTL analyses were conducted to identify chromosome 3A regions associated with each agronomic trait using the composite interval mapping (CIM) method as implemented by the QTL Cartographer 1.30 software (Basten et al., 2000). In addition, QEI were evaluated by testing individual marker GEI by means of analysis of variance (ANOVA) and comparing marker genotype regression coefficients calculated as the regression of marker genotype mean on an environmental index. Details of QTL and QEI detection are given in Campbell et al. (2003).

Environmental Data
Climatic data were kindly provided by the High Plains Regional Climate Center, University of Nebraska, from automated weather stations located in close proximity to each field trial. The dataset consisted of daily mean temperature, daily solar radiation, and daily precipitation from planting to crop maturity in each of the seven environments where the RICLs-3A were evaluated. Each location-year set of weather data was divided into three seasonal periods representing crop developmental phases: (i) seedling emergence to terminal spikelet initiation [the developmental phase where shoot elongation begins (Kirby, 1984)], (ii) terminal spikelet initiation to anthesis, and (iii) anthesis to physiological maturity. The daily mean temperature, the total accumulated solar radiation and the total accumulated precipitation for each phase of winter wheat development was calculated on the basis of the developmental model described by Streck et al. (2003a)( 2003b).

The developmental model, which is cultivar dependent, predicts the date of seedling emergence, terminal spikelet initiation, anthesis, and physiological maturity on the basis of cultivar planting date, site latitude (photoperiod), and daily temperature. Streck et al. (2003a)(2003b) predicted the developmental pattern of cv. Arapahoe, which was included as a check in all field trials conducted in this study. Since anthesis dates were measured for Arapahoe and the RICLs-3A in all seven environments, we could approximate to within about 7 d, the start and end dates of the three successive developmental phases. Mean daily temperature (T1, T2, T3, where T1 is for phase 1, etc.), total solar radiation (SR1, SR2, SR3), and total precipitation (P1, P2, P3) were summarized for each of the three developmental phases to total nine environmental covariates for subsequent analyses. Phenotypic correlations were calculated among the nine environmental covariates to identify highly associated covariates.

Partitioning GEI
RICLs-3A LSMEANS were calculated for grain yield, kernels per square meter, grain volume weight, plant height, 1000-kernel weight, spikes per square meter, and kernels per spike in each environment from an ANOVA. A data table containing RICLs-3A LSMEANS in each environment, the nine environmental covariates, and molecular marker data coded as 1 (WI allele) and –1 (CNN allele) were used to conduct factorial regressions similar to those described by Crossa et al. (1999). Essentially, factorial regression models represent analyses of covariance that use genotypic covariates (e.g., individual genotype trait data, molecular marker data, etc.) and environmental covariates (e.g., precipitation, temperature, etc.) along with combinations of genotypic covariate x environmental covariate interactions to partition GEI. For the factorial regression models, Type I sums of squares were calculated using PROC GLM (SAS, 1999) then multiplied by the number of replications of each RICLs-3A genotype to obtain adjusted sums of squares that were tested for significance using an F-test and the experimental error estimated from the combined analysis of variance. The factorial regression model including environmental covariates is

where E is the expected value, y_ijk is the trait response for the ith RICL-3A genotype, in the jth environment in the kth block, µ is the grand mean, G_i is the main effect of the ith RICLs-3A genotype, E_j is the main effect of the jth environment, C_jl is the lth environmental covariate (C) in the jth environment, b_cil is the slope of the ith genotype for the lth covariate and GE_ij is the residual GEI remaining. The factorial regression model including marker x environmental covariate interactions is

where M_ilC_jn is the product of the lth marker covariate (M) of the ith RICLs-3A genotype with the nth environmental covariate in the jth environment, b_mcln is the slope for the M_ilC_jn term and other terms are as previously defined. For more detailed description of factorial regression models, see Vargas et al. (1999) and Crossa et al. (1999). The significance of genotype x environmental covariate and marker x environmental covariate interactions were tested independently to avoid the difficulty of building a multiple regression using highly correlated molecular marker data. Crossa et al. (1999) described methods of incorporating highly correlated molecular markers in a multiple regression model of GEI using stepwise selection, which include the use of partial least squares models to create combinations of correlated variables to include in multiple regression models as latent variables. However, biological interpretations of latent or hypothetical variables contributing to GEI can be difficult, as conclusions on the effects of individual measured variables are not always apparent.

Initially in this study, each genotype x environmental covariate interaction for a given trait was independently tested for significance by means of the first equation noted above. Environmental covariates for the first two developmental phases were used to partition GEI for kernels per square meter, plant height, spikes per square meter, and kernels per spike, since it was known that variable environmental conditions during preanthesis growth affected these traits (Donmez et al., 2001). Environmental covariates for the second two developmental phases were used to partition GEI for grain volume weight and 1000-kernel weight since it was known these traits were most influenced by environmental conditions during reproductive and grain filling phases of growth (Donmez et al., 2001). Environmental data from all three developmental phases were used to partition GEI for grain yield because of the complex nature of grain yield and its dependence on other agronomic traits, each of which are affected during different phases of development (McCaster et al., 1994).

After identifying significant genotype x environmental covariate interactions explaining the GEI for each trait, marker x environmental covariate interactions were independently tested for significance by means of the second equation above. The molecular markers identified by Campbell et al. (2003) displaying significant interactions with the environment were used in this analysis. Marker x environmental covariate factorial regressions identified the response of genes on specific chromosomal regions of 3A to specific environmental covariates, which presumably identified specific environmental conditions associated with the expression of a particular QTL.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Environmental Covariates
Environmental covariate data and RICLs-3A mean values for agronomic traits are summarized in Table 1. Variation across environments was evident for all of the environmental covariates. All of the agronomic traits showed a wide range of mean values for RICLs-3A across environments, for example grain yield ranged from 1.90 to 4.65 Mg ha^–1. Correlation analyses among environmental covariates indicated significant associations between T1 and P1 (P = 0.030, r = –0.80), T1 and T2 (P = 0.003, r = –0.93), P1 and SR1 (P = 0.017, r = 0.84), and T2 and SR2 (P = 0.013, r = –0.86). Although these few significant correlations among environmental covariates were detected, multicollinearity among environmental covariates was avoided by conducting factorial regressions for each environmental covariate individually.

View larger version (27K): [in this window] [in a new window]   Fig. 1. Differences in the grain yield differential between the ‘Wichita’ and ‘Cheyenne’ genotypes at Xbarc67 (Wichita genotype–Cheyenne genotype) across environments differing for (a.) mean daily temperature during vegetative growth (T1) and (b.) mean daily temperature during reproductive growth (T2).

View larger version (22K): [in this window] [in a new window]   Fig. 2. Differences in the number of kernels per square meter differential between the ‘Wichita’ and ‘Cheyenne’ genotypes at Xbcd1555 (Wichita genotype–Cheyenne genotype) across environments differing for total precipitation accumulated during reproductive growth (P2).

Grain Volume Weight
For grain volume weight, marker x environmental covariate interactions represented interactions between the chromosomal region between Xcdo549 and Xbarc57 and around Xbcd1380 and the environmental covariates SR2, P2, and T3. The most significant marker x environmental covariate interaction (Xbcd1380 x SR2) explained less than 1% of the GEI sums of squares (Table 3). The Xbcd1380 x SR2 interaction did not explain a large portion of the total Xbcd1380 x environment interaction (16.5%), and the residual Xbcd1380 x environment interaction remained significant (P < 0.01, Table 4). Sensitivity of the effect of the region around Xbcd1380 corresponded to the QTL for grain volume weight detected by Campbell et al. (2003), which also displayed significant QEI. Plotting SR2 values against RICLs-3A genotypes at Xbcd1380 across environments indicated differences between CNN (WI3A) and CNN genotypes were greater in environments with higher amounts of solar radiation from terminal spikelet initiation to anthesis. Hence, the effect of this QTL was greater with increased solar radiation in the reproductive phase, which probably provided energy for production of stored assimilates and healthier plants to fill grain after anthesis.

Plant Height, 1000-Kernel Weight, Spikes per Square Meter, and Kernels per Spike
Marker x environmental covariate interactions detected for plant height, 1000-kernel weight, spikes per square meter, and kernels per spike were explained by marker interactions with SR1 for plant height, SR3 for 1000-kernel weight, T1 and P1 for spikes per square meter, and T1, P1, T2, and SR1 for kernels per spike. Overall marker x environmental covariate interactions did not explain a large percentage of the GEI sums of squares, approximately 2% for plant height and kernels per spike and less than 1% for 1000-kernel weight and spikes per square meter (Table 3). However, the most significant marker x environmental covariate interaction detected for each trait explained a large portion of the total marker x environment interaction, with the remaining marker x environment residual being nonsignificant (Table 4). This indicated environmental covariates used in this study explained the total marker x environment interactions for plant height (73.3%), 1000-kernel weight (62.5%), spikes per square meter (41.9%), and kernels per spike (66.6%) very well, but simple linear trends were not evident.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This experiment, conducted across a range of Nebraska environments, showed that large GEI and QEI associated with traits for agronomic performance on chromosome 3A of wheat can be partially explained from agronomic data, environmental covariates, and molecular markers. Selection of environmental covariates and how best to summarize them for use in studies designed to study GEI and QEI is not trivial. A phenological basis for categorizing environmental data, as used in this study, provided a minimal grouping of environmental datasets representative of the test-site growing conditions.

Individual genotype x environmental covariate interactions explained 12 to 35% of the total GEI sums of squares for agronomic traits, while individual marker x environmental covariate interactions explained a very small percentage (<3%) of the total GEI for agronomic traits. Differences in precipitation amounts during reproductive growth and mean daily temperature during vegetative and reproductive growth were most associated with RICLs-3A agronomic performance disparity across environments, which is in agreement with a previous report that indicated high yield potential for winter wheat crops relied heavily on temperature and precipitation before anthesis in the Great Plains (Thompson, 1962).

Overall, while marker x environmental covariate interactions explained a small percentage of the total GEI for agronomic performance traits, this study illustrates that a large percentage of environmental interactions for a particular QTL can be explained with environmental covariates and factorial regressions that lead to biological explanations of QTL expression differences across a range of environments. Identification of significant GEI and QEI alone (changes in rank or magnitude) do not provide a framework for understanding the biology of the interaction, and only allow one to conclude the presence of an interaction. In contrast, our ability to explain a large portion of the identified, significant Xbarc67 x environment interaction for grain yield with an individual Xbarc67 x T2 interaction indicates that differences in the Xbarc67 genotype differential across environments for grain yield is dependent on mean daily temperature during reproductive growth (terminal spikelet initiation to anthesis). Identifying improved approaches and methodologies to summarize environmental data that best represent critical developmental and physiological time periods during the growing season will lead to better explanations of specific environmental factors responsible for GEI and QEI.

	ACKNOWLEDGMENTS

 
We thank the High Plains Regional Climate Center, University of Nebraska for providing climatic data. We also are grateful to Dr. Jim Specht and Dr. David Baltensberger for critically reviewing the manuscript.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Nebraska Agricultural Research Division, Journal Series No. 14053.

Received for publication April 14, 2003.

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TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Campbell, B. T. Articles by Erayman, M. Search for Related Content PubMed Articles by Campbell, B. T. Articles by Erayman, M. Agricola Articles by Campbell, B. T. Articles by Erayman, M. Related Collections Cell Biology & Molecular Genetics Crop Genetics Plant and Environment Interactions