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Sukumaran, S.

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Sukumaran
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Sukumaran, S.
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0000-0003-4088-4624

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2017 - 201922020 - 20211
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AGROVOC: GENOTYPE ENVIRONMENT INTERACTION ×

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Now showing 1 - 3 of 3
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    An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops
    (Cell Press, 2021) Xianran Li; Tingting Guo; Jinyu Wang; Bekele, W.A.; Sukumaran, S.; Vanous, A.E.; McNellie, J.P.; Cortes, L.T.; Lopes, M.; Lamkey, K.R.; Westgate, M.E.; McKay, J.K.; Archontoulis, S.V.; Reynolds, M.P.; Tinker, N. A.; Schnable, P.S.; Jianming Yu
    Publication
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    Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
    (Crop Science Society of America (CSSA), 2017) Sukumaran, S.; Crossa, J.; Jarquin, D.; Reynolds, M.P.
    Genotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G × E using the pedigree-derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree-based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site-year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual- and combined-population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G × E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual-population prediction analyses, but not for combined-population prediction analyses. Our results indicate pedigree-based reaction norm models with G × E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.
    Publication
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    Genomic prediction with pedigree and genotype X environment interaction in spring wheat grown in South and West Asia, North Africa, and Mexico
    (Genetics Society of America, 2017) Sukumaran, S.; Crossa, J.; Jarquin, D.; Lopes, M.; Reynolds, M.P.
    Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype × environment (G×E) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.
    Publication