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]

2. High-Throughput Phenotyping or Phenomics

  • Mota LFM, Pegolo S, Baba T, Penagaricano F, Morota G, Bittante G, and Cecchinato A. 2021. Evaluating the performance of machine learning and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. Journal of Dairy Science. Early view. [DOI]
  • 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.

    4. Variance heterogeneity genome-wide mapping analysis

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

    5. Genomic connectedness

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

    6. Medical Subject Headings (MeSH) analysis

    We pioneered MeSH analysis in agricultural species.

    7. Biological information driven whole-genome prediction

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

    8. Machine learning and Data Science

    Application of machine learning and data science to genetics.

    9. Software development

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