Person:
Sukumaran, S.

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Sukumaran
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Sukumaran, S.

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Now showing 1 - 10 of 10
  • Capturing wheat phenotypes at the genome level
    (Frontiers Media S.A., 2022) Hussain, B.; Akpınar, B.A.; Alaux, M.; Algharib, A.M.; Sehgal, D.; Ali, Z.; Aradottir, G.I.; Batley, J.; Bellec, A.; Bentley, A.R.; Cagirici, H.B.; Cattivelli, L.; Choulet, F.; Cockram, J.; Desiderio, F.; Devaux, P.; Dogramaci, M.; Dorado, G.; Dreisigacker, S.; Edwards, D.; El Hassouni, K.; Eversole, K.; Fahima, T.; Figueroa, M.; Gálvez, S.; Gill, K.S.; Govta, L.; Gul, A.; Hensel, G.; Hernandez, P.; Crespo Herrera, L.A.; Ibrahim, A.M.H.; Kilian, B.; Korzun, V.; Krugman, T.; Yinghui Li; Shuyu Liu; Mahmoud, A.F.; Morgounov, A.; Muslu, T.; Naseer, F.; Ordon, F.; Paux, E.; Perovic, D.; Reddy, G.V.P.; Reif, J.C.; Reynolds, M.P.; Roychowdhury, R.; Rudd, J.C.; Sen, T.Z.; Sukumaran, S.; Bahar Sogutmaz Ozdemir; Tiwari, V.K.; Ullah, N.; Unver, T.; Yazar, S.; Appels, R.; Budak, H.
    Publication
  • Correction to: Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat (Euphytica, (2017), 213, 257, 10.1007/s10681-017-2040-z)
    (Springer, 2018) 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.; Hakim M.A.; 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.; Sohail, Q.; Mujahid, M.Y.; Ahmad, G.; Khan, A.J.; Mahboob 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.
    Publication
  • Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat
    (Nature Publishing Group, 2022) Fei He; Wei Wang; Rutter, W.B.; Jordan, K.; Jie Ren; Taagen, E.; DeWitt, N.; Sehgal, D.; Sukumaran, S.; Dreisigacker, S.; Reynolds, M.P.; Halder, J.; Sehgal, S.K.; Shuyu Liu; Jianli Chen; Fritz, A.; Cook, J.; Brown-Guedira, G.; Pumphrey, M.; Carter, A.; Sorrells, M.E.; Dubcovsky, J.; Hayden, M.; Akhunova, A.; Morrell, P.L.; Szabo, L.J.; Rouse, M.N.; Akhunov, E.
    Publication
  • Wheat genomics and breeding: bridging the gap
    (CABI, 2021) Hussain, B.; Akpınar, B.A.; Alaux, M.; Algharib, A.M.; Sehgal, D.; Ali, Z.; Appels, R.; Aradottir, G.I.; Batley, J.; Bellec, A.; Bentley, A.R.; Cagirici, H.B.; Cattivelli, L.; Choulet, F.; Cockram, J.; Desiderio, F.; Devaux, P.; Dogramaci, M.; Dorado, G.; Dreisigacker, S.; Edwards, D.; El Hassouni, K.; Eversole, K.; Fahima, T.; Figueroa, M.; Gálvez, S.; Gill, K.S.; Govta, L.; Gul, A.; Hensel, G.; Hernandez, P.; Crespo Herrera, L.A.; Ibrahim, A.M.H.; Kilian, B.; Korzun, V.; Krugman, T.; Yinghui Li; Shuyu Liu; Mahmoud, A.F.; Morgounov, A.; Muslu, T.; Naseer, F.; Ordon, F.; Paux, E.; Perovic, D.; Reddy, G.V.P.; Reif, J.C.; Reynolds, M.P.; Roychowdhury, R.; Rudd, J.C.; Sen, T.Z.; Sukumaran, S.; Tiwari, V.K.; Ullah, N.; Unver, T.; Yazar, S.; Budak, H.
    Publication
  • Effect of flowering time-related genes on biomass, harvest index, and grain yield in CIMMYT elite spring bread wheat
    (MDPI, 2021) Dreisigacker, S.; Burgueño, J.; Pacheco Gil, Rosa Angela; Molero, G.; Sukumaran, S.; Rivera-Amado, C.; Reynolds, M.P.; Griffiths, S.
    Publication
  • Identifying quantitative trait loci for lodging-associated traits in the wheat doubled-haploid population Avalon × Cadenza
    (CSSA, 2021) Piñera Chavez, F.J; Berry, P.M.; Foulkes, J.; Sukumaran, S.; Reynolds, M.P.
    Publication
  • AP02 - Exploring genetic diversity for harvest index and identifying improved selection approaches
    (IWYP, 2019) Rivera-Amado, C.; Molero, G.; Piñera Chavez, F.J; Sukumaran, S.; Gimeno, J.; Reynolds, M.P.
    Publication
  • Incorporating complex physiological traits into wheat breeding pipelines
    (CIMMYT, 2018) Molero, G.; Piñera Chavez, F.J; Rivera-Amado, C.; Gimeno, J.; Pinto Espinosa, F.; Sukumaran, S.; Saint Pierre, C.; Reynolds, M.P.
    Publication
  • Increasing genetic gains in wheat through physiological genetics and breeding
    (CIMMYT, [2016]) Sukumaran, S.; Reynolds, M.P.; Crossa, J.; Lopes, M.; Jarquin, D.; Dreisigacker, S.; Molero, G.; Pinto Espinosa, F.; Piñera Chavez, F.J
    In order to meet future wheat demand it is necessary to increase yield potential and develop stress adapted genotypes. To do so, research and breeding is conducted at CIMMYT through the International Wheat Yield Partnership (IWYP) platform combining physiology, genetics, and breeding. Physiological breeding focuses on understanding the physiology and genetics of key traits and conducting complementary crosses among them based on conceptual models to utilize the diversity present in the CIMMYT germplasm. Physiological breeding combined with genetic approaches (GWAS, QTLs, Genomic Selection) are used in the program to achieve genetic gains. (Reynolds and Langridge 2016 Current opinion in plant biology).
    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