Gota Morota, Ph.D.
Affiliations: | 2014 | Animal Sciences | University of Wisconsin, Madison, Madison, WI |
Area:
Genetics, Biostatistics Biology, Animal Culture and Nutrition AgricultureGoogle:
"Gota Morota"Parents
Sign in to add mentor| Daniel Gianola | grad student | 2014 | UW Madison | |
| (Whole-genome prediction of complex traits using kernel methods.) | ||||
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Publications
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| Kravitz A, Liao M, Morota G, et al. (2024) Retrospective Single Nucleotide Polymorphism Analysis of Host Resistance and Susceptibility to Ovine Johne's Disease Using Restored FFPE DNA. International Journal of Molecular Sciences. 25 |
| Sabag I, Bi Y, Sahoo MM, et al. (2024) Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame. The Plant Genome. e20481 |
| Aydin KB, Bi Y, Brito LF, et al. (2024) Review of sheep breeding and genetic research in Türkiye. Frontiers in Genetics. 15: 1308113 |
| Baba T, Morota G, Kawakami J, et al. (2023) Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle. Jds Communications. 4: 363-368 |
| Massahiro Yassue R, Galli G, James Chen CP, et al. (2023) Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation. Plant Direct. 7: e492 |
| Sabag I, Bi Y, Peleg Z, et al. (2023) Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame. Frontiers in Genetics. 14: 1108416 |
| Wang Z, Yu D, Morota G, et al. (2023) Genome-wide association analysis of sucrose and alanine contents in edamame beans. Frontiers in Plant Science. 13: 1086007 |
| Kadlec R, Indest S, Castro K, et al. (2022) Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera. Translational Animal Science. 6: txac163 |
| Qu J, Morota G, Cheng H. (2022) A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data. The Plant Genome. e20228 |
| Morota G, Jarquin D, Campbell MT, et al. (2022) Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods in Molecular Biology (Clifton, N.J.). 2539: 269-296 |