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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Multiple-trait genomic selection methods increase genetic value prediction accuracy.

Yi Jia1, Jean-Luc Jannink

  • 1Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA.

Genetics
|October 23, 2012
PubMed
Summary
This summary is machine-generated.

Multivariate genomic selection leverages genetic correlations between traits, significantly improving prediction accuracy for low-heritability traits and situations with missing phenotypes. This approach offers advantages over single-trait methods in breeding programs.

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Area of Science:

  • Quantitative genetics
  • Animal and plant breeding

Background:

  • Genetic correlations between traits are common in breeding programs.
  • Univariate genomic selection overlooks this valuable information.
  • Multivariate genomic selection is underexplored in practical applications.

Purpose of the Study:

  • To present and compare multivariate linear models for genomic selection.
  • To evaluate their performance against univariate models.
  • To investigate model extensions for hyperparameter estimation and missing data imputation.

Main Methods:

  • Comparison of three multivariate models (GBLUP, BayesA, BayesCπ) with univariate models.
  • Simulation and analysis of real quantitative trait data.
  • Extension of BayesA for hierarchical hyperparameter estimation.
  • Extension of BayesCπ for missing phenotype imputation.

Main Results:

  • Optimal marker-effect variance priors are dependent on genetic architecture, making estimation beneficial.
  • Multivariate genomic selection significantly boosts prediction accuracy for low-heritability traits when a correlated high-heritability trait is available.
  • Multivariate methods outperform univariate methods when phenotypes are missing for some individuals or traits.

Conclusions:

  • Multivariate genomic selection effectively utilizes genetic correlations to enhance prediction accuracy.
  • Model extensions improve performance by estimating hyperparameters and handling missing data.
  • This approach holds significant potential for improving breeding program efficiency.