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Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes.

Bader Arouisse1, Tom P J M Theeuwen2, Fred A van Eeuwijk1

  • 1Biometris, Wageningen University and Research, Wageningen, Netherlands.

Frontiers in Genetics
|June 10, 2021
PubMed
Summary
This summary is machine-generated.

Leveraging secondary traits can enhance genomic prediction in plant breeding. Novel methods like penalized selection indices and dimension reduction show promise, especially when secondary trait data is available for the test set.

Keywords:
GBLUPgenomic predictionpenalized regressionrandom forestsecondary traitsselection indices

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Area of Science:

  • Plant breeding and genetics
  • Genomic prediction
  • High-throughput phenotyping

Background:

  • Genomic prediction significantly impacts plant breeding.
  • High-throughput technologies generate numerous secondary traits alongside target traits.
  • Utilizing secondary traits to improve genomic prediction for target traits is an open question, especially with many secondary traits.

Purpose of the Study:

  • To investigate methods for improving genomic prediction using a large number of secondary traits.
  • To overcome limitations of existing methods, such as infeasibility when secondary traits are not measured on the test set or inability to distinguish genetic/non-genetic correlations.
  • To propose and evaluate novel approaches for incorporating secondary traits in genomic prediction.

Main Methods:

  • Dimension reduction of secondary traits using penalized regression (LS-BLUP) or random forests (RF-BLUP), followed by bivariate genomic best linear unbiased prediction (GBLUP).
  • Penalized selection index (SI-BLUP) using bivariate GBLUP with plot-level data for simulated datasets.
  • Genomic prediction of secondary traits and their use in multi-kernel methods (GM-BLUP), enabling prediction when secondary traits are only in the training set.

Main Results:

  • SI-BLUP was most accurate for most simulated data, followed closely by RF-BLUP or LS-BLUP.
  • In real datasets (Arabidopsis metabolites, maize transcriptomics), no method substantially improved univariate prediction when secondary traits were only in the training set.
  • LS-BLUP and RF-BLUP showed the highest accuracy when secondary traits were available for the test set.

Conclusions:

  • Novel methods offer potential for improving genomic prediction using multiple secondary traits.
  • The availability of secondary trait data in the test set is crucial for substantial improvements.
  • Further research is needed to optimize these methods for practical plant breeding applications.