Key research projects

1. Quantitative genetic analysis of high-throughput phenotyping data

We develop and apply statistical methods to connect data generated from high-throughput phenotyping with genetics. [YouTube 1] [YouTube 2] [YouTube 3]

Sabag I, Bi, Y, Sahoo MM, Herrmann I, Morota G, and Peleg Z. 2024. Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame. The Plant Genome. Early view. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Silva CM, Mezzomo, HC, Ribeiro JPO, Signorini VS, Lima GW, Torres Vieira EF, Portes MF, Morota G, Corredo LP, and Nardino M. Insights on multi-spectral vegetation indices derived from UAV-based high-throughput phenotyping for indirect selection in tropical wheat breeding. Euphytica. 220:35. [DOI] [Research Square]
Yassue RM, Galli G, Chen CJ, Fritsche-Neto R, and Morota G. 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. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Bi Y, Yassue RM, Paul P, Dhatt BK, Sandhu J, Do PT, Walia H, Obata T, and Morota G. 2023. Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice. G3: Genes, Genomes, Genetics. 13:5. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Yassue RM, Galli G, Fritsche-Neto R, and Morota G. 2023. Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data. Crop Science. 63:88-100. [DOI] [bioRxiv]
Qu J, Morota G, and 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. 15:e20228. [DOI] [PubMed] [EuropePMC]
Morota G, Jarquin D, Campbell MT, and Iwata H. 2022. Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data. In High Throughput Plant Phenotyping: Methods and Protocols. Molecular Biology Series, Springer, New York. 2539:269-296. [DOI] [PubMed] [EuropePMC] [arXiv]
Baba T, Pegolo S, Mota LFM, Penagaricano F, Bittante G, Cecchinato A, and Morota G. 2021. Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle. Genetics Selection Evolution. 53:29. [DOI] [PubMed] [EuropePMC]
Goncalves MTV, Morota G, Almeida Costa PM, Vidigal PMP, Barbosa MHP, and Peternelli LA. 2021. Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. PLOS One. 16(3):e0236853. [bioRxiv] [DOI] [PubMed] [EuropePMC]
Baba T, Momen M, Campbell MT, Walia H, and Morota G. 2020. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. PLOS One. 15(2):e0228118. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Momen M, Campbell MT, Walia H, and Morota G. 2019. Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines. G3: Genes, Genomes, Genetics. 9:3369-3380. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Morota G, Jarquin D, Campbell MT, and Iwata H. Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data. arXiv. [arXiv]
Campbell MT, Walia H, and Morota G. 2019. Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits. The Plant Genome. 12:180075. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Hussain W, Campbell MT, Walia H, and Morota G. 2018. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies. Plant Direct. 2:e00091. [DOI] [bioRxiv]
Campbell MT, Walia H, and Morota G. 2018. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping. Plant Direct. 2:1-11. [DOI] [PubMed] [EuropePMC] [bioRxiv]

2. High-Throughput Phenotyping or Phenomics

Wang J, Hu Y, Xiang L, Morota G, Brooks SA, Wickens CL Miller-Cushon EK, and Yu H. 2024. Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. Journal of Animal Science. 102:1-6. [DOI] [PubMed] [EuropePMC] [arXiv]

Bi Y, Campos LM, Wang J, Yu H, Hanigan MD, and Morota G. 2023. Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms. Smart Agricultural Technology. 6:100352. [DOI] [arXiv]

Kadlec R, Indest S, Castro K, Waqar S, Campos LM, Amorim ST, Bi Y, Hanigan MD, and Morota G. 2022. Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera. Translational Animal Science. 6:1-6. [DOI] [PubMed] [EuropePMC]

Alghamdi S, Zhao Z, Ha DS, Morota G, and Ha SS. 2022. Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data. Journal of Animal Science. 100:1-9. [DOI] [PubMed] [EuropePMC]

Chen CJ, Morota G, Lee K, Zhang Z, and Cheng H. 2022. VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera. Journal of Animal Science. 100:1-10. [DOI] [PubMed] [EuropePMC]

Yassue RM, Galli G, Borsato Junior R, Cheng H, Morota G, and Fritsche-Neto R. 2022. A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth-promoting bacteria. The Plant Phenome Journal. 5:e20043. [DOI] [bioRxiv]

Mota LFM, Pegolo S, Baba T, Morota G, Penagaricano F, Bittante G, and Cecchinato A. 2021. Comparison of single-breed and multi-breed training population for infrared predictions of novel phenotypes in Holstein cows. Animals. 11:1993. [DOI]

Mota LFM, Pegolo S, Baba T, Penagaricano F, Morota G, Bittante G, and Cecchinato A. 2021. Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. 104:8107-8121. [DOI] [PubMed] [EuropePMC]

Morota G, Cheng H, Cook D, and Tanaka E. 2021. ASAS-NANP SYMPOSIUM: Prospects for interactive and dynamic graphics in the era of data-rich animal science. Journal of Animal Science. 99:1-17. [DOI] [PubMed] [EuropePMC]

Yu H, Lee K, and Morota G. 2021. Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science. 5:1-9. [DOI] [PubMed] [EuropePMC]

Zhu F, Paul P, Hussain W, Wallman K, Dhatt BK, Sandhu J, Irvin L, Morota G, Yu H, and Walia H. 2021. SeedExtractor: an open-source GUI for seed image analysis. Frontiers in Plant Science. 11:581546. [DOI] [PubMed] [EuropePMC] [bioRxiv]

Morota G, Ventura RV, Silva FF, Koyama M, and Fernando SC. 2018. Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. 96:1540–1550. [DOI] [PubMed] [EuropePMC]

3. Network analysis

Network-based quantitative genetics offers new perspectives and approaches.

Suela MM, Azevedo CF, Nascimento ACC, Momen M, Caixeta ET, Morota G, and Nascimento M. 2023. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models. Tree Genetics and Genomes. 19:23. [DOI]
de Novais FJ, Yu H, Cesar ASM, Momen M, Poleti MD, Petry B, Mourao GB, de Almeida Regitano LC, Morota G, and Coutinho LL. 2022. Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. Frontiers in Genetics. 13:948240. [DOI] [PubMed] [EuropePMC]
Pegolo S, Yu H, Morota G, Bisutti V, Rosa GJM, Bittante G, and Cecchinato A. 2021. Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science. 104:5705-5718. [DOI] [PubMed] [EuropePMC]
Momen M, Bhatta M, Hussain W, Yu H, and Morota G. Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. 2021. Plant Direct. 00:e00304. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Wang Z, Chapman D, Morota G, and Cheng H. 2020. A Multiple-trait Bayesian variable selection regression method for integrating phenotypic causal networks in genome-wide association studies. G3: Genes, Genomes, Genetics. 10:4439-4448. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Yu H, Morota G, Celestino Jr. EF, Dahlen CR, Wagner SA, Riley DG, and Hulsman Hanna LL. Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics. In press. [DOI] [bioRxiv]
Pegolo S, Momen M, Morota G, Rosa GJM, Gianola G, Bittante G, and Cecchinato A. 2020. Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle. Scientific Reports. 10:7751. [DOI] [PubMed] [EuropePMC]
Momen M, Campbell MT, Walia H, and Morota G. Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies. Plant Methods. 15:107. [DOI] [bioRxiv]
Yu H, Campbell MT, Zhang Q, Walia H, and Morota G. 2019. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics. 9:1975-1986. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Momen M, Mehrgardi AA, Roudbar MA, Kranis A, Pinto RM, Valente BD, Morota G, Rosa GJM, and Gianola D. 2018. Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models. Frontiers in Genetics. 9:455. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Valente BD, Morota G, Penagaricano F, Gianola D, Weigel KA, and Rosa GJM. 2015. The causal meaning of genomic predictors and how it affects construction and comparison of genome-enabled selection models. Genetics. 200:483-494. [DOI] [PubMed] [EuropePMC] [bioRxiv] [arXiv]
Morota G and Gianola D. 2013. Evaluation of linkage disequilibrium in wheat with an L1 regularized sparse Markov network. Theoretical and Applied Genetics. 126:1991-2002. [DOI] [PubMed] [EuropePMC]
Morota G, Valente BD, Rosa GJM, Weigel KA, and Gianola D. 2012. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network. Journal of Animal Breeding and Genetics. 129:474-487. [DOI] [PubMed] [EuropePMC] [Genomic Selection Virtual Issue]

4. Variance heterogeneity genome-wide mapping analysis

We identified variance QTL that controll the variability of phenotypes rather than the mean of phenotypes.

Murphy MD, Fernandes SB, Morota G, and Lipka AE. 2022. Assessment of two statistical approaches for variance genome-wide association studies in plants. Heredity. 129:93–102. [DOI] [bioRxiv] [PubMed] [EuropePMC]
Hussain W, Campbell MT, Jarquin D, Walia H, and Morota G. 2020. Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions. The Plant Genome. 13:e20011. [DOI] [PubMed] [EuropePMC] [bioRxiv]

5. Genomic connectedness

We showed that genetic connectedness can be used in the context of whole-genome prediction.

Yu H and Morota G. 2021. GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics. 22:119. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Amorim ST, Yu H, Momen M, de Albuquerque, LG, Pereira, ASC, Baldi F, and Morota G. 2020. An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science. 98:1-12. [DOI] [PubMed] [EuropePMC]
Momen M and Morota G. 2018. Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions. Genetics Selection Evolution. 50:45. [DOI] [PubMed] [EuropePMC]
Yu H, Spangler ML, Lewis RM, and Morota G. 2018. Do stronger measures of genomic connectedness enhance prediction accuracies across management units? Journal of Animal Science. 96:4490-4500. [DOI] [PubMed] [EuropePMC]
Yu H, Spangler ML, Lewis RM, and Morota G. 2017. Genomic relatedness strengthens genetic connectedness across management units. G3: Genes, Genomes, Genetics. 10:3543-3556. [DOI] [PubMed] [EuropePMC] [bioRxiv]

6. Medical Subject Headings (MeSH) analysis

We pioneered MeSH analysis in agricultural species.

Amorim ST, Tsuyuzaki K, Nikaido I, and Morota G. 2022. Improved MeSH analysis software tools for farm animals. Animal Genetics. 53:171-172. [DOI] [PubMed] [EuropePMC]
Beissinger TM and Morota G. 2017. Medical subject heading (MeSH) annotations illuminate maize genetics and evolution. Plant Methods. 13:8. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Morota G, Beissinger TM, and Penagaricano F. 2016. MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken. G3: Genes, Genomes, Genetics. 6:2447-2453. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Morota G, Penagaricano F, Petersen JL, Ciobanu DC, 2015. Tsuyuzaki K, and Nikaido I. An application of MeSH enrichment analysis in livestock. Animal Genetics. 46:381-387. [DOI] [PubMed] [EuropePMC]
Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, and Nikaido I. 2015. MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis. BMC Bioinformatics. 16:45. [DOI] [PubMed] [EuropePMC]

7. Biological information driven whole-genome prediction

Annotation-based whole-genome regression methods can partition the source of genomic prediction accuracy.

Abdollahi-Arpanahi R, Morota G, and Penagaricano F. 2017. Predicting bull fertility using genomic data and biological information. Journal of Dairy Science. 100:9656-9666. [DOI] [PubMed] [EuropePMC]
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJM, and Gianola D. 2016. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genetics Selection Evolution. 48:10. [DOI] [PubMed] [EuropePMC]
Morota G, Abdollahi-Arpanahi R, Kranis A, and Gianola D. 2014. Genome-enabled prediction of broiler traits in chickens using genomic annotation. BMC Genomics. 15:109. [DOI] [PubMed] [EuropePMC]

8. Machine learning and Data Science

Application of machine learning and data science to genetics.

Alghamdi S, Zhao Z, Ha DS, Morota G, and Ha SS. 2022. Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data. Journal of Animal Science. 100:1-9. [DOI] [PubMed] [EuropePMC]
Morota G, Cheng H, Cook D, and Tanaka E. 2021. ASAS-NANP SYMPOSIUM: Prospects for interactive and dynamic graphics in the era of data-rich animal science. Journal of Animal Science. 99:1-17. [DOI] [PubMed] [EuropePMC]
Yu H, Lee K, and Morota G. 2021. Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science. 5:1-9. [DOI] [PubMed] [EuropePMC]
Morota G, Ventura RV, Silva FF, Koyama M, and Fernando SC. 2018. Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. 96:1540–1550. [DOI] [PubMed] [EuropePMC]
Morota G and Gianola D. 2014. Kernel-based whole-genome prediction of complex traits: a review. Frontiers in Genetics. 5:363. [DOI] [PubMed] [EuropePMC]
Gianola D, Morota G, and Crossa J. 2014. Genome-enabled Prediction of Complex Traits with Kernel Methods: What Have We Learned? Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, BC, Canada. August 17-22. [WCGALP] [PDF]
Morota G, Boddhireddy P, Vukasinovic N, Gianola D, and DeNise S. 2014. Kernel-based variance components estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Frontiers in Genetics. 5:56. [DOI] [PubMed] [EuropePMC]
Morota G, Koyama M, Rosa GJM, Weigel KA, and Gianola D. 2013. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genetics Selection Evolution. 45:17. [DOI] [PubMed] [EuropePMC]

9. Software development

We are interested in developing modern interactive visualization tools in agriculture science.

Wang J, Hu Y, Xiang L, Morota G, Brooks SA, Wickens CL Miller-Cushon EK, and Yu H. 2024. Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. Journal of Animal Science. In press. [arXiv]
Yassue RM, Galli G, Chen CJ, Fritsche-Neto R, and Morota G. 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. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Chen CJ, Morota G, Lee K, Zhang Z, and Cheng H. 2022. VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera. Journal of Animal Science. 100:1-10. [DOI] [PubMed] [EuropePMC]
Yu H and Morota G. 2021. GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics. 22:119. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Hussain W, Campbell MT, Walia H, and Morota G. 2018. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies. Plant Direct. 2:1–4. [DOI] [PubMed] [EuropePMC] [bioRxiv]
Morota G. ShinyGPAS: 2017. Interactive genomic prediction accuracy simulator based on deterministic formulas. Genetics Selection Evolution. 49:91. [DOI] [PubMed] [EuropePMC] [bioRxiv]