International Journal of Agriculture and Crop Sciences. Available online at www.ijagcs.com IJACS/2012/4-6/ 317-324. ISSN 2227-670X ©2012 IJACS Journal
Chromosomal localization of QTLs controlling genotype × environment Interactions in barley Ezatollah Farshadfar*, Mahdi Geravandi, Zahra Vaisi Department of Agronomy and Plant Breeding, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran * Corresponding author email:
[email protected] ABSTRACT: In order to evaluate the grain yield stability of wheat-barley disomic addition lines and locate the QTLs controlling static and dynamic phenotypic stability in barley, 7 disomic adition lines (DALs) of barley (Hordeum vulgare L., 2n = 2x = 14, cv. Betzes) in the genetic background of bread wheat (Triticum aestivum L., 2n = 6x = 42, cv. Chinese spring = CS) along with thier parents were sudied in five environments using a randomized complete block design (RCBD) with three replications during 20072009. Combined analysis of variance indicated significant genotype-by-environment interactions for grain yield. Multivariate and univariate stability parameters such as: additive main effect and multiplicative interactions (AMMI), regression coefficients (bi), deviations from regression ( ), environmental variance ( ), superiority index (Pi), Wricke’s ecovalence ( ), coefficient of variation (CVi), geometric adaptability index (GAI), AMMI stability value (ASV), yield stability index (YSI) and a simple graphical method indicated that most of the QTLs controlling adaptation in barley are located on chromosomes 2H, 4H, 5H and 6H. The genes controlling dynamic stability are located on the chromosome 3H, while the genes controlling static stability are located on the chromosomes 2H, 4H and 5H. Therfore, it can be concluded that the QTLs controlling yield stability in barley are dispersed in the whole genome of barley. Key words: wheat-barley disomic addition lines, phenotypic stability, QTLs. INTRODUCTION Drought stress is one of the most important stresses in agriculture limiting crop productivity and improving yield under drought is a major objective of plant breeding worldwide. Identification of suprior genotypes in drought prone environments is reduced because of complexity of genotype-by-environment interactions (Cattivelli et al., 2008; Chenu et al., 2011). The different relative performance of genotypes in different environments known as genotypeby-environment interactions. If the rank of genotypes studing in different environment are highly different, the genotype-by-environment interactions become a most important challenging issue of the plant breeding programs (Mohammadi and Amri, 2008; Mohammadi and Nader Mahmoodi, 2008). Static stability defined by the variance of a specific genotype across environments and dynamic stability represented by a stable yield response across environments. Unlike static stability, estimation of dynamic stability depends on the investigated genotypes. Those stability parameters that were not related to grain yield, such as deviations from regression ( ), Wricke’s ecovalence ( ) and AMMI stability value (ASV) (Wricke, 1962; Eberhart and Russell, 1966; Purchase et al., 2000) suggested as measures of static stability, while geometric adaptability index (GAI) and superiority index (Pi) (Lin and Binns, 1988; Mohammadi and Amri, 2008) that were correlated to mean grain yield defined as measures of dynamic stability (Mohammadi and Amri, 2008). Kozak (2010) used and environmental variance ( ) (Lin et al., 1986) for measurement of dynamic and static stability, respectively. However the improvement of crops productivity under stressed conditions requires genotypes with good stress tolerance and yield stability (Mohammadi and Amri, 2011). Visualization and understanding of the genetic structure of phenotypic stability are essential for plant
Intl J Agri Crop Sci. Vol., 4 (6), 317-324, 2012
breeders for improvement of grain yield stability and management of adaptation genes. Wheat-barley disomic addition lines (DALs) have been developed through wide hybridization between the hexaploid (2n = 6x = 42) wheat cultivar Chinese Spring (CS) and the diploid (2n = 2x = 14) barley cultivar Betzes. A single pair chromosomes from doner parent (barley, cv. Betzes) have been added to full chromosome set of recipient parent (Wheat, cv. CS). Each DALs contains the full complement of wheat chromosomes and a single homeologous chromosome pair from barley. They are useful genetic resources for various studies. DALs have made it possible to locate the gene controlling traits from the donor chromosome in the recipient genetic background on the basis of presence/absence of the genes on the chromosomes added to the recipient genome. Evaluation of DALs under a range of different environments may help plant breeder to find genes that are useful for making wheat adaptable to unpredictable conditions (Cho et al., 2006; Farshadfar et al., 2011; Farshadfar 2011; Farshadfar et al., 2012). Various methods such as regression coefficient, sum of squared deviations from regression, stability variance, coefficient of determination, coefficient of variability, additive main effects and multiplicative interaction (AMMI) and GGE-biplot analysis have been used to analysis of genotype-by-environment interaction (Finlay and Wilkinson, 1963; Eberhart and Russel, 1966; Shukla, 1972; Pinthus, 1973; Francis and Kanneberg, 1978; Gauch and Zobel, 1988; Yan et al., 2000). Kozak (2010) sugested three plots namely regular performance plot (RPP), Environment-centered performance plot (ECPP) and Environment-standardized performance plot (ESPP) as a simplest method for analysis of both static and dynamic yield stability in a set genotypes evaluted in a range of environments. Kozak (2010) studied six soybean genotypes in eight environments and concluded that RPP in comparision to ECPP and ESPP is suitable for studying genotype stability in a static sense and provides more information about environments. ECPP was useful for presenting genotype stability in a dynamic sense and the information provided by ESPP was poor in the both static and dynamic senses. Grain yield stability analysis in different crops was studied by many authors using parametric and nonparametric methods. However, little is known about the chromosomal location of genes controlling grain yield stability and effects of added chromosomes on yield stability in wheat-barley disomic addition lines. The objective of the present investigation was chromosomal localization of the genes controlling static and dynamic yield stability in a full set of wheat-barley disomic addition lines using parametric (univariate and multivariate) and a simple graphical method. MATERIALS AND METHODS Genetic material and experimental design The plant materials of this research were 7 disomic adition lines (DALs) of barley (Hordeum vulgare L., 2n = 2x = 14, cv. Betzes) in the genetic background of bread wheat (Triticum aestivum L., 2n = 6x = 42, cv. Chinese spring = CS) along with thier donor (barley, cv. Betzes) and recipient (bread wheat, cv. CS) parents. The DALs were named as 1H to 7H indicating added chromosomes of barley (1H to 7H) into the genetic background of CS, respectively. The genetic materials of this research were produced and supplied by Dr. Taher from ICARDA. The expriments were carried out using a randomized complete block design with three replications under two rain-fed and irrigated conditions. The genotypes were evaluated in the exprimental field of Razi univerity, Kermanshah, Iran (47°20'N latitude, 34°20'E longitude and 1351m altitude), during three cropping seasons (2007-2009). Each plot consisted of 3 rows with 1 m in length and 20 cm row spacing. Seed sowing was done by hand. The rain-fed expriments received no water but irrigated expriments were irrigated 3 times after heading. At the maturity time, after seperation of border effects the midele row of each plots harvested and grain yied were measured. Data analysis Combined analysis of variance and mean comparision were performrd using SAS 9.1 software. The genotypes considered as fixed factors and environments considered as random factors. Principal component analysis was performed using Statgraphics software. Additive main effects and multiplicative interactions (AMMI) analysis was performrd using CropStat 7.2 and GenStat (discovery Edition 4) softwares. Excel software was used for data pereparations, drawing of graphes and calculation of stability parameters, regression coefficients (bi), deviations from regression ( ), Environmental variance ( ), superiority index (Pi), Wricke’s ecovalence ( ), coefficient of variation (CVi), geometric adaptability index (GAI) and AMMI stability value (ASV) (Wricke 1962; Eberhart and Russell, 1966; Francis and Kannenberg, 1978; Lin et al., 1986; Lin and Binns, 1988; Purchase et al., 2000; Mohammadi and Amri, 2008).
Intl J Agri Crop Sci. Vol., 4 (6), 317-324, 2012
For drawing the regular performance plot (RPP), environmental mean yields over all genotypes that ordered by an increasing mean yield showed in horizontal axis and genotypes mean yield over environments that ordred by decreasing mean yield showed on vertical axis (Kozak, 2010). In the environment-centered performance plot (ECPP), environmental mean yields over all genotypes that ordered by an increasing mean yield showed in horizontal axis and environment centered mean yield that was calculated based on following formulla was showed in vertical axis (Kozak, 2010): In this equation
, Yge and
e are
the environmental centered mean yield, yield value for the gth genotype
in the eth environment and mean of grain yield across all genotypes in the eth environment, respectively. RESULTS AND DISCUSSION Combined analysis of variance (ANOVA) for grain yield of disomic addition lines (DALs) indicated that genotype-by-environment interactions (GEI) was the most important source of grain yield variation (Table 1). The contribution of variation caused by the GEI (37.41%) was larger than the other sourses such as genotypes and environments. This result showed that DALs had different yield performance across environments because of GEI. The presence of significant GE interaction complicates the selection of desirable genotypes because of its effects on the reduction of the association between genotypic and phenotypic values (Jalata, 2011). Based on combined ANOVA effect of genotypes on grain yield variation was not significant, while results of ANOVA for each environments separately (not shown) revealed that in 2 environments yield performance of DALs were significantly different. Based on the results of mean comparision using Duncan's Multiple Range Test (DMRT) yield performance of investigated genotypes was significant (Table 2). Mean of grain yield is a first parameter for evaluating of genotypes (Mohammadi and Amri, 2008). Chinese Spring variety (CS) had higher mean yield over environments. The mean yield of DALs varied from 45.89 g (addition line 2H) to 66.58 g (addition line 3H) and 59.75 g (addition line 4H). It is concluded that chromosome 3H carry the genes controlling grain yield. Farshadfar et al. (2011) previously showed that most of the quantitative trait loci (QTLs) involved in controlling phenotypic stability and yield in barley are located on chromosomes 3H and 4H. Table 1. Combined analysis of variance and AMMI analysis for grain yield in wheat-barley disomic adition lines over 5 environments S.V.
df
SS
MS
SS explained %
Environments
4
20292
5073
8.40
Error1
10
19859
1986
8.22
Genotypes
8
25297
3262
10.47
Interactions
32
85531
2673**
35.41
Error2
80
90543
1132
37.48
Treatments
44
131120
2980**
54.28
Total
134
241522
1802
100
IPCA1
11
66752
6068**
27.63 (78.04%)
IPCA2
9
11382
1265
4.71 (13.30%)
Residuals
12
7397
616
3.06
** significant at 1% level of probability. AMMI analysis indicated that first IPCA was significant (P < 0.01). The IPCA1 and IPCA2 accounted for 78.04% and 13.30% of the GE interaction, respectively. The first two IPCAs accounted for a total 91.40% of the interaction (Table 1). However, based on these results most information can be graphically displayed using IPCA1 and IPCA2 biplot. The genotypic IPCA scores in the AMMI analysis considered as indicators of the yield stability (Purchase et al., 2000). The lowest IPCA1 score was observed for 5H, 1H and 2H. Based on IPCA1 scores, these genotypes identified as the most stable genotypes. The AMMI stability value (ASV) is based on the IPCA1 and IPCA2 scores. The genotypes with lower ASV value would be considered as stable. The ASV confirms the results of IPCA1. Between those selected genotypes 1H
Intl J Agri Crop Sci. Vol., 4 (6), 317-324, 2012 had the highest yield performance. According to ASV and IPCA1 scores, CS followed by 3H and 7H were unstable and had the highest yield. They were adapted to specific environments. In AMMI2 biplot (Figure 1), environments with long vectors had a great influence in determination of GEI and environments with short vectors contributed less to GEI. The angles between the environmental vectors in the biplot represent the phenotypic correlation between the environments (Mohammadi et al., 2011). Environments 2 and 5 were the irrigated conditions and environments 1, 3 and 4 were the rain-fed environments. In the AMMI2 biplot, environments 2 and 5 nearly correlated with each other and genotypes 3H was adapted to environment 5, while addition lines 4H, 5H and 6H were adapted to environments 4 and 5. The Betzes variety (No. 8) nearly located in the center of the biplot, therefore this genotypes was the stable with low yield performance (Figure 1 and Table 2). Based on AMMI2 biplot genotypes 1H, 2H and 7H were adapted to environments 1, 3 and 4 (rain-fed environments). Table 2. Mean of grain yield and 8 parametric stability measures of DALs across 5 environments. Genotypes 1H 2H 3H 4H 5H 6H 7H Betzes CS
Grain Yield Mean* Rank b 49.01 7 b 45.89 9 b 66.58 2 b 59.72 3 b 47.08 8 b 50.98 6 b 53.17 5 b 53.43 4 a 92.23 1
bi 0.98 0.31 -0.78 1.16 0.98 1.39 -0.06 0.57 4.43
444.40 306.61 936.50 382.44 300.36 257.17 758.49 268.17 1604.5
515.23 246.46 817.81 543.14 407.08 558.99 569.71 262.73 4895.3
Pi 3191.75 3516.25 2913.26 2154.97 2882.37 2565.44 3419.89 2875.16 857.44
1333.46 1273.40 5200.37 1168.68 901.23 889.51 3130.6 941.66 13671.0
CVi 46.31 34.34 42.95 39.02 42.85 46.38 44.88 30.33 75.86
GAI 44.22 43.84 65.58 56.53 43.14 46.69 47.23 51.14 70.49
IPCA1 -0.66 0.66 5.61 -1.61 0.64 -1.53 4.17 2.16 -9.44
AMMI IPCA2 4.10 3.42 -2.10 -2.29 -3.57 -2.34 2.22 0.28 0.28
ASV 5.64 5.18 32.98 9.74 5.20 9.32 24.60 12.67 55.40
YSI 10 10 10 8 10 10 12 10 10
G×E% 4.67 4.46 18.24 4.09 3.16 3.11 10.98 3.30 47.95
* The mean values followed by common letters are not significant at 5% level of probability.
Yield stability index (YSI) proposed by Farshadfar (2011) incorporates both stability and yield performance in one criteria. For calculation of YSI, ranks of the genotypes based on mean yields over environments added to ranks of the genotypes based on ASV. The genotypes with low YSI would be considered as high yielding and stable genotypes. The lowest YSI belonged to addition line 4H, hence QTLs controlling simultaneously yield and stability are located on chromosome 4H (Table 2). Regression coefficient (bi) and variance of deviations from regression ( ) suggested by Eberhart and Russell (1966). Those genotypes with regression coefficients >1 would be adapted to favorable conditions, those with regression coefficients
