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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Imputing missing yield trial data.

H G Gauch1, R W Zobel

  • 1Department of Agronomy and USDA-ARS, Cornell University, 14853, Ithaca, NY, USA.

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Summary
This summary is machine-generated.

The new EM-AMMI model handles missing genotype-environment data in yield trials. This statistical approach improves yield estimation and genotype selection accuracy, even with incomplete datasets.

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Area of Science:

  • Agricultural Science
  • Statistical Modeling
  • Genetics

Background:

  • The Additive Main effects and Multiplicative Interaction (AMMI) model is crucial for analyzing genotype-environment interactions in crop yield trials.
  • Traditional AMMI models require complete data, excluding trials with missing genotype-environment combinations.
  • Missing data in yield trials limits the application of powerful statistical tools like AMMI.

Purpose of the Study:

  • To address the limitation of missing data in the AMMI statistical model.
  • To introduce and validate a modified AMMI model capable of handling incomplete datasets.
  • To provide theoretical insights into the imputation process and accuracy gains using the new model.

Main Methods:

  • Implementation of the Expectation-Maximization (EM) algorithm to fit the AMMI model with missing data, termed EM-AMMI.
  • Quantification of direct and indirect information within yield trial data using EM-AMMI.
  • Application of EM-AMMI to impute missing data in a New York soybean yield trial.

Main Results:

  • The EM-AMMI model successfully fits AMMI despite the presence of missing data.
  • Empirical measurements demonstrated good predictive success for the EM-AMMI model.
  • The statistical theory explains the observed accuracy gains through the Stein effect.

Conclusions:

  • EM-AMMI provides a robust solution for analyzing yield trials with missing data.
  • The model enhances the accuracy of yield estimation and genotype selection.
  • EM-AMMI has significant applications for both unintentional and intentional missing data scenarios.