Person: Sukumaran, S.
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Sukumaran
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S.
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Sukumaran, S.
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0000-0003-4088-4624 3 results
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- Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat(Springer, 2017) Reynolds, M.P.; Pask, A.; Hoppitt, W.J.E.; Sonder, K.; Sukumaran, S.; Molero, G.; Saint Pierre, C.; Payne, T.S.; Singh, R.P.; Braun, H.J.; González, F.G.; Terrile, I.I.; Barma, N.C.D.; Abdul Hakim, M.; He Zhonghu; Zheru Fan; Novoselovic, D.; Maghraby, M.; Gad, K.I.M.; Galal, E.G.; Hagras, A.; Mohamed M. Mohamed; Morad, A.F.A.; Kumar, U.; Singh, G.P.; Naik, R.; Kalappanavar, I.K.; Biradar, S.; Prasad, S.V.S.; Chatrath, R.; Sharma, I.; Panchabhai, K.; Sohu, V.S.; Gurvinder Singh Mavi; Mishra, V.K.; Balasubramaniam, A.; Jalal Kamali, M.R.; Khodarahmi, M.; Dastfal, M.; Tabib Ghaffary, S.M.; Jafarby, J.; Nikzad, A.R.; Moghaddam, H.A.; Hassan Ghojogh; Mehraban, A.; Solís Moya, E.; Camacho Casas, M.A.; Figueroa, P.; Ireta Moreno, J.; Alvarado Padilla, J.I.; Borbón Gracia, A.; Torres, A.; Quiche, YN.; Upadhyay, S.R.; Pandey, D.; Imtiaz, M.; Rehman, M.U.; Hussain, M.; Ud-din, R.; Qamar, M.; Muhammad Kundi; Mujahid, M.Y.; Ahmad, G.; Khan, A.J.; Mehboob Ali Sial; Mustatea, P.; Well, E. von; Ncala, M.; Groot, S. de; Hussein, A.H.A.; Tahir, I.S.A.; Idris, A.A.M.; Elamein, H.M.M.; Yann Manes; Joshi, A.K.To accelerate genetic gains in breeding, physiological trait (PT) characterization of candidate parents can help make more strategic crosses, increasing the probability of accumulating favorable alleles compared to crossing relatively uncharacterized lines. In this study, crosses were designed to complement “source” with “sink” traits, where at least one parent was selected for favorable expression of biomass and/or radiation use efficiency—source—and the other for sink-related traits like harvest-index, kernel weight and grains per spike. Female parents were selected from among genetic resources—including landraces and products of wide-crossing (i.e. synthetic wheat)—that had been evaluated in Mexico at high yield potential or under heat stress, while elite lines were used as males. Progeny of crosses were advanced to the F4 generation within Mexico, and F4-derived F5 and F6 generations were yield tested to populate four international nurseries, targeted to high yield environments (2nd and 3rd WYCYT) for yield potential, and heat stressed environments (2nd and 4th SATYN) for climate resilience, respectively. Each nursery was grown as multi-location yield trials. Genetic gains were achieved in both temperate and hot environments, with most new PT-derived lines expressing superior yield and biomass compared to local checks at almost all international sites. Furthermore, the tendency across all four nurseries indicated either the superiority of the best new PT lines compared with the CIMMYT elite checks, or the superiority of all new PT lines as a group compared with all checks, and in some cases, both. Results support—in a realistic breeding context—the hypothesis that yield and radiation use efficiency can be increased by improving source:sink balance, and validate the feasibility of incorporating exotic germplasm into mainstream breeding efforts to accelerate genetic gains for yield potential and climate resilience.
Publication - 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 - 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.
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