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 6 results
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- Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments(Oxford University Press, 2022) Pengcheng Hu; Chapman, S.; Sukumaran, S.; Reynolds, M.P.; Bangyou Zheng
Publication - Performance of spring wheat derived from physiological strategic crossing under Mexican growing environments(CGIAR, 2019) Piñera Chavez, F.J; Alvarado Padilla, J.I.; Ireta Moreno, J.; Macías-Cervantes, J.; Chavez-Villalba, G.; Flores, D.; Solís Moya, E.; Sukumaran, S.; Molero, G.; Reynolds, M.P.
Publication - Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations(Higher Education Press, 2019) Caiyun Liu; Pinto Espinosa, F.; Cossani, C.M.; Sukumaran, S.; Reynolds, M.P.The application of spectral reflectance indices (SRIs) as proxies to screen for yield potential (YP) and heat stress (HS) is emerging in crop breeding programs. Thus, a comparison of SRIs and their associations with grain yield (GY) under YP and HS conditions is important. In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative (WAMI) population comprising 287 elite lines under YP and HS conditions. Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions, respectively. We identified enhanced vegetation index (EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI) were the common SRIs under booting and heading stages in HS. Genomewide association study (GWAS) using 18704 single nucleotide polymorphisms (SNPs) from Illumina iSelect 90K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively. Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1B (60–62 cM), 3A (15, 85–90, 101– 105 cM), 3B (132–134 cM), 4A (47–51 cM), 4B (71– 75 cM), 5A (43–49, 56–60, 89–93 cM), 5B (124–125 cM), 6A (80–85 cM), and 6B (57–59, 71 cM). Among them, SNPs in chromosome 5A (89–93 cM) and 6A (80–85 cM) were co-located for yield and yield related traits. Overall, this study highlights the utility of SRIs as proxies for GY under YP and HS. High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.
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.
Publication - Genetic mapping and physiological breeding towards heat and drought tolerance in spring wheat(CIMMYT, 2016) Sukumaran, S.; Cossani, C.M.; Molero, G.; Valluru, R.; Tattaris, M.; Reynolds, M.P.
Publication - 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.
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