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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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2015 - 201932020 - 20211
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Author: Lopes, M. ×

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Now showing 1 - 5 of 5
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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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    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
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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.
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    Phenotyping for breeding and physiological pre-breeding
    (CIMMYT, 2016) Reynolds, M.P.; Molero, G.; Pinto Espinosa, F.; Rivera-Amado, C.; Piñera Chavez, F.J; Sukumaran, S.; Lopes, M.; Saint Pierre, C.
    Publication
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    Genome-wide association study for adaptation to agronomic plant density: a component of high yield potential in spring wheat
    (Crop Science Society of America (CSSA), 2015) Sukumaran, S.; Reynolds, M.P.; Lopes, M.; Crossa, J.
    Previous research has shown that progress in genetic yield potential is associated with adaptation to agronomic planting density, though its genetic basis has not been addressed before. In the current study, a wheat (Triticum aestivum L.) association mapping initiative (WAMI) panel of 287 elite lines was assessed for the effects of plant density on grain yield (YLD), 1000-kernel weight (TKW), and grain number (GNO) in yield plots consisting of four evenly spaced rows. The YLD and GNO of inner (high plant density) rows compared with outer rows (low plant density) indicated a consistent pattern: genotypes that performed best under intense competition (inner rows) responded less to reduced competition (outer rows) while being generally the best performers on aggregate (inner plus outer rows). However, TKW was not affected by plant density. To identify the genetic loci, an adaptation to density index (ADi) was computed as the scaled difference in trait values between inner and outer rows. Results on biplot analysis indicated that ADi was correlated with YLD in high-yielding environments, suggesting that it is a component of high yield potential. Genotyping of the WAMI panel was done through 90K Illumina Bead single nucleotide polymorphism (SNP) array. Association mapping employed using 18,104 SNP markers for ADi identified a major locus in chromosome 3B at 71 cM that explained 11.4% variation in ADi for YLD and GNO. Functional marker for ADi will enable identification of the trait in early generations— not otherwise possible in spaced plants typical of pedigree breeding approach—and to select parents for hybrid development.
    Publication