Seth C. Murray, Ph.D.
Affiliations: | Texas A & M University, College Station, TX, United States | ||
2008 | Cornell University, Ithaca, NY, United States |
Area:
Phenomics, quantitative genetics, plant breeding, maize, agronomyWebsite:
http://soilcrop.tamu.edu/people/murray-seth-c/Google:
"Seth C. Murray"Bio:
https://scholar.google.com/citations?user=o2U40BEAAAAJ&hl=en
Parents
Sign in to add mentorJ. Mitchell McGrath | research assistant | Michigan State | ||
(Undergraduate Research Mentor) | ||||
Stephen Kresovich | grad student | 2002-2008 | Cornell | |
(Genetic and phenotypic diversity in sorghum for improvement as a biofuel feedstock.) |
Children
Sign in to add traineeIvan D. Barrero | grad student | Texas A & M | |
Fabian Echeverria Beirute | grad student | Texas A & M | |
Gerald de la Fuente | grad student | Texas A & M | |
Holly M. Lane | grad student | Texas A & M | |
Adam L. Mahan | grad student | Texas A & M | |
Meghyn Stalcup Meeks | grad student | Texas A & M | |
Nancy Wahl | grad student | Texas A & M | |
Jacob Washburn | grad student | Texas A & M | |
Kerry Mayfield | grad student | 2011 | Texas A & M |
Steven L. Anderson | grad student | 2019 | Texas A & M |
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Publications
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Adak A, Murray SC, Washburn JD. (2024) Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations. The New Phytologist. 242: 121-136 |
Lima DC, Aviles AC, Alpers RT, et al. (2023) 2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative. Bmc Research Notes. 16: 219 |
Lima DC, Washburn JD, Varela JI, et al. (2023) Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. Bmc Research Notes. 16: 148 |
Adak A, Murray SC, Calderón CI, et al. (2023) Genetic mapping and prediction for novel lesion mimic in maize demonstrates quantitative effects from genetic background, environment and epistasis. Tag. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik. 136: 155 |
Adak A, Kang M, Anderson SL, et al. (2023) Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data. Journal of Experimental Botany |
Lima DC, Aviles AC, Alpers RT, et al. (2023) 2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project. Bmc Genomic Data. 24: 29 |
Adak A, Murray SC, Anderson SL. (2022) Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (Bethesda, Md.) |
Adak A, Murray SC, Anderson SL, et al. (2021) Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize. The Plant Genome. e20102 |
Adak A, Conrad C, Chen Y, et al. (2021) Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) Discovers Novel Temporal Phenotypes. G3 (Bethesda, Md.) |
Rogers AR, Dunne JC, Romay C, et al. (2021) The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3 (Bethesda, Md.). 11 |