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Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data.

Gota Morota1, Diego Jarquin2, Malachy T Campbell3

  • 1Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. morota@vt.edu.

Methods in Molecular Biology (Clifton, N.J.)
|July 27, 2022
PubMed
Summary

Plant phenomics and high-throughput phenotyping (HTP) generate vast data for improving crop traits. Statistical methods integrating HTP data enhance genomic selection (GS) prediction accuracy for complex traits.

Keywords:
Genetic gainHigh-throughput phenotypingImage dataLongitudinal traitQuantitative genetics

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Area of Science:

  • Plant science
  • Genetics
  • Quantitative genetics

Background:

  • Next-generation sequencing and plant phenomics offer new avenues for studying complex traits.
  • High-dimensional data from these technologies pose challenges for quantitative genetics.

Purpose of the Study:

  • To describe statistical methods for analyzing high-throughput phenotyping (HTP) data.
  • To enhance the prediction accuracy of genomic selection (GS) using HTP data.

Main Methods:

  • Field-based HTP using unoccupied aerial vehicles and light detection and ranging.
  • Extending GS models (single-trait, multi-trait, GxE) to incorporate HTP data as covariates.
  • Utilizing random regression models for longitudinal data analysis.

Main Results:

  • Image data from HTP can increase genetic gain.
  • HTP data capture dynamic growth, development, and stress responses with high resolution.
  • Integrated models improve prediction accuracy for complex traits.

Conclusions:

  • Statistical frameworks are needed to effectively analyze high-dimensional HTP data.
  • Integrating HTP data into GS models is crucial for advancing plant breeding.
  • Further research is needed to address standing issues in HTP data analysis.