Stability analysis and G*E interactions in plants

Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.

1. G*E INTERACTIONS AND STABILITY ANALYSIS- AMMI MODEL Submitted to DR.K.B.ESWARI, Associate professor, Dept of GPBR

2. The interaction between the genotype and environment that produces the phenotype is called as Genotype x Environmental Interaction. P = G + E + GE Genotypes respond differently across a range of environments i.e., the relative performance of varieties depends on the environment. INTRODUCTION

3. Environmental variable can be divided into 2 groups ( A llard and Bradshaw , 1964) 1.Predictable environment factor 2.Unpredictable factor Predictable factors include permanent features of environment which are under human control such as soil type, planting date, row spacing, rows of new trend application. Unpredictable factors are those which fluctuate inconsistently like rainfall, temperature, relative humidity not under control are called unpredictable environment conditions.

4. The environment refers to the external conditions that affect expression of genes of an individual genotype. Environment can be classified into two groups ( Comstock and Moll , 1963) a) macro environment b) micro environment TYPES OF INTERACTIONS

5. Refers to the environment with variables having large and easily recognisable effect. Main features: 1. The environment affects are easily detectable such as fertilizer doses, planting dates, spacing, irrigation schedules. 2. Macro environment is controlled by Predictable factors such as soil type, planting dates and close spacing. 3. It is under human control Macro environment

6. The environment of a single organism /genotype as opposed to that of another growing at the same time in almost the same place is referred to as micro environment. Main features: 1.The environment effects are not easily recognizable such as differences in humidity , temperature, etc.. at the same place. 2.Micro environment is governed by unpredictable factors like rain fall, temperature, relative humidity which fluctuate inconsistently. 3.It is not under human control. Micro environment

7. It refers to those changes in structure or function of an individual/population which lead to better survival in a given environment is known as adaptation. Main features: Adaptation favours those characters which are advantageous for survival and through which an individual acquires adaptive value or fitness. In the process of adaptation survival is the main concern. Natural selection plays an important role in the process of adaptation. Adaptation

8. Types of adaptation There are four types of adaptation 1.Specific genotypic adaptation 2.General genotypic adaptation 3.Specific population adaptation 4.General population adaptation Factors affecting adaptability: Heterogeneity. Heterozygosity . Genetic polymorphism. Mode of pollination .

9. Stability refers to the performance with respective changing environmental factors overtime within given location. selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Low magnitude of G.E interaction involves the consistent performance of a population over variable environments. Stability analysis

10. It consists of following steps: Location / environment wise analysis of variance. pooled analysis of variance for all the locations/ environments. If G.E interaction is found significant ,stability analysis can be carried out using one of the four methods: 1.Finlay and Wilkinson model (1963) 2.Eberhat and Russell model(1966) 3.Perkins and Jinks model(1968) 4.Freeman and Perkins model (1971)

11. Used two parameters 1)Mean performance over environments. 2)Regression performance in different environments. The following inferences can be drawn: 1)The regression coefficient of unity indicates average stability 2)If the regression coefficient is >1,it means below average stability 3) If the regression coefficient is <1,it means above average stability. 4) Regression coefficient of 0 would express absolute stability. FINLAY AND WILKINSON MODEL

12. MERITS Analysis of this model is simple. 2 parameters- mean yield over locations and regression coefficient are used to asses the phenotypic stability. DEMERITS The deviations from the regression line are not estimated which are important for the stability analysis. Greater emphasis is given on mean performance over environments than regression coefficients . MERITS AND DEMERITS

13. It is the most popular and useful model. In 1966 both made further improvement in stability analysis by partitioning the G.E interaction of each variety into 2 parts. one is slope of the regression line , second is deviation from regression line. In this model total variance is first divided into 2 components: -genotypes -environment plus interaction (E+G*E) The second component is further divided in to 3 components. Environment linear G.E linear Pooled deviations Sum of squares due to pooled deviations are further divided into sum of squares due to individual genotype. Eberhart and Russell model

14. This model consists of three parameters a) mean yield over locations b)regression coefficient =bi C)Deviation from regression =s²di Analysis of stability parameters is simple as compared to other models of stability analysis. The degree of freedom for environment is 1. It requires less area hence less expensive when compared to other models. It does not provide independent estimation for mean performance and environmental index Main features of this model

15. Source of variation Degrees of freedom Genotypes g-1 E+ G*E interaction g(e-1) environment (linear) 1 G.E linear g-1 pooled deviations g(e-2) genotype-1 e-2 genotype-2 Pooled error e-2 ge (r-1) Anova table

16. Merits: It measures three parameters of stability A=mean yield over environments B=regression coefficient C=deviation from regression line It provides more reliable information on stability than Finlay and Wilkinson model. Analysis is simple. Demerits: estimation of mean performance and environment index is not independent. There is a combined estimation of sum of squares of environment and interactions which is not proper. Eberhart and Russell (1956) defined stable variety as one with a regression coefficient of unity(b=1) and a minimum deviation from the regression lines(s²d=0).

17. In this model total variance is first divided into 3 components. 1)genotypes 2)environments 3)genotypes x environment G-E variance is sub divided into a) heterogeneity due to regression b) sum of square due to remainder This model is less expensive than Freeman and Perkins. It requires less area for experimentation. The degree of freedom for environment is e-2. Analysis is more difficult than Eberhart and Russell model. It does not provide independent estimation of mean performance and environmental index. PERKINS AND JINKS MODEL

18. Source of variation Degrees of freedom Genotypes g-1 Environment e-1 Genotype x environment (g-1)(e-1) Heterogeneity among regressions g-1 Remainder (g-1)(e-2) Error ge (r-1) Anova table

19. In this model total variance is first divided into 3 components. 1)Genotypes 2)environment 3) G*E The environmental s.s is sub divided into 2 components a) combined regression b) residual 1 The interaction variance is also subdivided into two parts a)homogeneity of regression b) residual 2 This model also includes 3 parameters like Eberhart and Russell model and provides independent estimation of mean performance and environmental index. The degree of freedom for environment is e-2 like perkins and jinks model. Analysis of this model is more difficult and expensive as compared to earlier two models. FREEMAN AND PERKINS MODEL

20. Source of variation Degrees of freedom Genotypes g-1 Environment e-1 Combined regression 1 residual (1) e-2 Interaction( GxE ) (g-1)(e-2) Heterogeneity of regressions g-1 residual (2) error (g-1)(e-2) ge (r-1) Anova table

21. AMMI is a combination of ANOVA for the main effects of the genotypes and the environment together with principal components analysis of the genotype-environment interaction. Method for analyzing GEI to identify patterns of interaction and reduce background noise. May provide more reliable estimates of genotype performance than the mean across sites. Biplots help to visualize relationships among genotypes and environments; show both main and interaction effects. AMMI Model

22. Y ijl =  + G i + E j + (  k  ik  jk ) + e ijl Where, Y ij is the observed mean yield of the i th genotype in j th environment μ is the general mean G i and E j represent the effects of the genotype and environment λ k is the singular value of the k th axis in the PCA α ik is the eigenvector of the i th genotype for the k th axis γ jk is the eigenvector of the j th environment for the k th axis n is the number of principal components in the model e ij is the average of the corresponding random errors AMMI Model

23. source df SS MS F TOTAL ( ger - 1) Treatment ( ge -1) Genotype (g -1) Environment (e-1) Interaction IPCA 1 IPCA 2 Residual (g-1) (e-1) blocks (r-1) error (r-1) ( ge -1) Analysis of variance for stability – AMMI Model

24. Principal components usually the first principal component (CP1) represents responses of the genotypes that are proportional to the environments, which are associated with the GxE interaction without change of the range . The second principal component ( CP2) provides information about cultivation locations that are not proportional to the environments, indicating that those are responsible of the GxE crossover interaction .

25. Biplot allows the observation in the same graph of the genotypes ( points) and the environments (vectors ), and (2) the exploration of patterns attributable to the effects of GxE interaction. In the biplot , the angles between the vectors that represent genotypes and environments show the interaction, and the distances from the origin indicate the degree of interaction that the genotypes show throughout the environments or vice versa . Graphical representation of numerical results often allows a straight forward interpretation of GEI. BIPLOTS

26. General interpretation genotypes that occur close to particular environments on the IPCA2 vs IPCA1 biplot show specific adaptation to those environments a genotype that falls near the center of the biplot (small IPCA1 and IPCA2 values) may have broader adaptation Interpretation

27. How many IPCAs (interaction principal component axes) are needed to adequately explain patterns in the data? Rule of thumb - discard higher order IPCAs until total SS due to discarded IPCA's ~ SSE. Usually need only the first 2 PC axes to adequately explain the data (IPCA1 and IPCA2). This model is referred to as AMMI2. Approach is most useful when G x location effects are more important than G x year effects

28. Name of the journal – Journal of radiation research Year of publishing – 2014 Authors of the research paper - Anowara Akter1*, Jamil Hassan M1, Umma Kulsum M1, Islam MR1, Kamal Hossain1 and Mamunur Rahman M2* 1Plant Breeding Division, Bangladesh Rice Research Institute, Bangladesh 2Senior Scientific Officer, Farm Management Division, Bangladesh AMMI Biplot Analysis for Stability of Grain Yield in Hybrid Rice ( Oryza sativa L.)

29. Genotype x environment interaction and stability performance were investigated on grain yield with 12 rice genotypes in five environments. The ANOVA for grain yield revealed highly significant (P<0.01) for genotypes, environments and their interactions. The significant interaction indicated that the genotypes respond differently across the different environments. Abstract

30. The AMMI model is a hybrid model involving both additive and multiplicative components of two way data structure which enabled a breeder to get precise prediction on genotypic potentiality and environmental influences on it. AMMI uses ordinary ANOVA to analyze the main effects (additive part) and PCA to analyze the non additive residual left over by the ANOVA . The main objectives of the present study are to identify more high yielding stable promising hybrids and to determine the areas where rice hybrids would be adapted by AMMI model. INTRODUCTION

31. The experiments were conducted at five districts namely Gazipur (E1), Comilla (E2), Barisal (E3), Rangpur (E4) and Jessore (E5) representing five different agro-ecological zones (AEZ) of Bangladesh. Twelve genotypes consisting of 3 advanced lines (BRRI 1A/ BRRI 827R (G1), IR58025A/ BRRI 10R (G2) and BRRI 10A/ BRRI 10R (G3)), 6 released hybrids (BRRI hybrid dhan1(G4), Tea (G5), Mayna (G6),Richer (G7), Heera-2 (G8) and Heeta 99-5 (G9)), and 3 inbred check varieties (BRRI dhan31 (G10), BRRI dhan33 (G11) and BRRI dhan39(G12)) were used as experimental materials. MATERIALS AND METHODS

32. The experiments were carried out in a randomized complete block design (RCBD), with 3 replications. 21 days old seedlings were transplanted in 20 square meter plot using single seedling per hill at a spacing of 20 cm×15cm. Fertilizers were applied @ 150:100:70:60:10 kg/ha Urea, TSP,MP, gypsum and ZnSO4, respectively. Standard agronomic practices were followed and plant protection measures were taken as required. The grain yield data for 12 genotypes in 5 environments were subjected to AMMI analysis of variance using statistical analysis package software Cropstat version 6.1

33. A NOV A

34. Figure 1: AMMI 1 Biplot for grain yield (tha-1) of 12 rice genotypes (G) and five environments (E) using genotypic and environmental scores.

35. Figure 2: AMMI 2 Biplot for grain yield (tha-1) showing the interaction of IPCA2 against IPCA1 scores of 12 rice genotypes (G) in five environments (E).

36. The mean grain yield value of genotypes averaged over environments indicated that G3 had the highest (5.99tha-1) and G12 the lowest yield (3.19 tha-1), respectively. It is noted that the variety G3 showed higher grain yield than all other varieties over all the environments. The genotypes (G1), (G2), (G3) and (G4) were hardly affected by the G x E interaction and thus would perform well across a wide range of environments. Results and Discussion

37. Name of the journal;- Advances in Biological Research Year of publishing;- 2009 Authors of the research paper;- A. Anandan , R. Eswaran , T. Sabesan and M. Prakash . Department of Agricultural Botany, Faculty of Agriculture, Annamalai University, T.N. Additive Main Effects and Multiplicative Interactions Analysis of Yield Performances in Rice Genotypes under Coastal Saline Environments

38. Abstract : The objective of the present investigation was to analyse the pattern of Genotype x Environment (G x E) interaction for grain yield of 46 genotypes by Additive Main effects and Multiplicative Interaction (AMMI) model using the data generated from three saline stress environments of east coastal region of Tamil Nadu, India. The results showed highly significant genotypic and G x E interaction. The G x E interaction influenced the relative ranking of the genotypes across saline stress environment condition.

39. The developed cultivars should adapt to a wide range of target environments, is the eventual goal of plant breeders. Hence, pattern of response of genotypes is studied by testing genotypes in different environments to study G X E interaction. AMMI offers on appropriate first statistical analysis of yield trials that may have a G x E interaction . The objectives of this study were to assess the extent of G x E interaction and to select the stable genotypes of rice INTRODUCTION

40. 46 rice genotypes from different parts of India were evaluated at Plant Breeding Research Farm, Faculty of Agriculture, Annamalai University, Annamalai,East coastal region of Tamil Nadu, India. With soil pH of 8 to 8.5 and EC of 2.51 to 2.8 dSm . The each genotype was evaluated in three seasons viz., E1 ( Kharif , 2006), E2 ( Kharif , 2007) and E3 (Rabi, 2007). For all trials, the design used was RCBD with three replications. The plot had 10sq.m with spacing of 20 cm between environments and rows and 20 cm between plants. Management practices were uniformly adapted to all seasons as per the recommendation for rice in the irrigated condition. MATERIALS AND METHODS

41. source df SS MS F Treatments 137 905.60 6.61 216.78** Genotypes 45 865.30 19.23 630.56** Environments 2 36.50 18.24 5.43** Interactions 90 3.90 0.04 1.42* IPCA 1 46 2.90 0.06 2.04** Residuals 44 1.00 0.02 0.77 Error 270 8.20 0.03 ANOVA TABLE

42. BIPLOT G26

43. The genotypes which had IPCA score nearest to zero are G24, G26, G27, G32 G34, G35, G39 and G45. Among the above mentioned stable genotypes, G45, G26, G27 G35 and G34 exhibited above average grain yield and indicated that these genotypes were well adaptable to saline environment condition RESULTS AND DISCUSSION

44. SUBMITTED BY B.RAchAnA RAM/16-45 DEP A RTMENT OF GENETICS AND PLANT BREEDING