“Biometrical insights into identification of stable genotypes in Plant Breeding”

                                              

         Ultimate activity of plant breeding is to evaluate the best genotype, be it a pureline or hybrid, in multi-location trial with a strong assumption that the best genotype recommended for use as cultivar will perform better in target locations across years. To ensure this hypothesis, the best genotype should be evaluated across representative locations and years. Identification of appropriate locations requires aproporiate evaluation of best genotypes for Genotype by location and year interaction. Environments are defined as set of nongenetic factors like rainfall, sunlight etc., to which plants are exposed. Genotype by Environment interaction (GEI) is defined as differential responses of genotypes to varying environments. Four patterns of GEI can be deciphered, which include non-cross over and cross over interactions. Genotypes possessing phenotypic plasticity tend to perform similarly across environments and are considered stable.

    Pooled ANOVA, which combines information across environments, is statistical tool to detect GEI. Many biometrical approaches have been employed to assess stability of genotypes, which are broadly classified as basic univariate parameters, regression-based parameters, and multivariate models like Additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment interaction (GGE). Univariate and regression-based models focus at selecting stable genotypes across environments, thereby overlooking specific adaptation of genotypes. In contrast, multivariate models tend identify specific adaptation patterns associated with genotype and particular environment(s), Both multivariate models employ principal component analysis (PCA), which is a dimensionality reduction technique. GGE is majorly a graphical approach, which subjects environmental centred data to PCA, thus partitioning confounded effects of Genotype and interaction. On the other hand, AMMI delineates the data into genotype, environment and interaction components, subjecting the latter to decomposition.

        Besides these, Best Linear Unbiased Prediction (BLUP) based and combination of AMMI and BLUP based parameters like Weighted average of absolute scores (WAASB) have been made available in statistical software. However, selecting genotypes based on stability alone would result in reduced resource use efficiency in productive environments, hence alternative approach of simultaneous selection for yield and stability was conducted in finger millet, where indices constructed from non-parametric, parametric and BLUP based statistics were compared for their efficiency.

        Choosing appropriate genotype for a single or group of environments, depends on the type of repeatable GEI underscoring the phenotypic expression of the trait under consideration. Accordingly, decision must be made in recommending cultivars, by employing appropriate statistical models for enhancing overall crop productivity.

References:

1. ABOUGHADAREH, P.A., KHALILI, M., POCZAI, P. AND OLIVOTO, T., 2022, Stability indices to deciphering the genotype-by-environment interaction (GEI) effect: An applicable review for use in plant breeding programs. Plants. 11(3):414. 

2. ANURADHA, N., PATRO, T.S., SINGAMSETTI, A., RANI, S.Y., TRIVENI, U., KUMARI, N.A., GOVANAKOPPA, N., PATHY, T.L. AND TONAPI, V.A., 2022, Comparative study of AMMI-and BLUP-based simultaneous selection for grain yield and stability of finger millet [Eleusine coracana (L.) Gaertn.] genotypes. Front. plant sci. 12:786839. 

3. BERNARDO, R., 2002, Breeding for quantitative traits in plants. Woodbury: Stemma press.

4. GAUCH, H. G., 2013, A simple protocol for AMMI analysis of yield trials. Crop Sci. 53(5):1860-9.

5. YAN, W., KANG, M. S., MA, B., WOODS, S., CORNELIUS, P. L., 2007, GGE biplot vs. AMMI analysis of genotype‐by‐environment data. Crop sci. 47(2):643-53.