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Genetic evaluation including intermediate omics features.

Ole F Christensen1, Vinzent Börner1, Luis Varona2

  • 1Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.

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|December 1, 2021
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Summary
This summary is machine-generated.

This study introduces a new statistical model for integrating omics data into genetic evaluations. The model enables accurate estimation of breeding values, crucial for advancing animal and plant breeding strategies.

Keywords:
GenPredGenomic PredictionShared Data Resourcebreeding valuegenetic evaluationmetabolomicsmixed model equationssingle-step methodtranscriptomics

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Area of Science:

  • Genetics and genomics
  • Animal and plant breeding
  • Bioinformatics

Background:

  • Increasing interest in intermediate omics traits (metabolomics, transcriptomics) in breeding.
  • Need for statistical models integrating genotypes, phenotypes, pedigree, and omics data for genetic evaluation.
  • Requirement for computational methods to estimate breeding values using omics data.

Purpose of the Study:

  • To present a joint statistical model for phenotypes and omics data.
  • To derive a formula for estimating breeding values.
  • To develop computational methods for genetic evaluation incorporating omics traits.

Main Methods:

  • Development of a joint model for phenotypes and omics data.
  • Derivation of a formula for breeding values.
  • Presentation of three equivalent methods for best linear unbiased prediction (BLUP) for complete omics data.
  • Application of restricted maximum likelihood (REML) for parameter estimation.
  • Extension of methods for incomplete omics data by incorporating individuals without omics data.

Main Results:

  • A joint model integrating omics data for genetic evaluation is presented.
  • Formulas for breeding values are derived.
  • Three BLUP methods for complete omics data and extensions for incomplete omics data are provided.
  • The methods require solving mixed model equation systems.
  • Parameter estimation using REML is demonstrated.

Conclusions:

  • The presented statistical model and computational methods enable the integration of omics data into genetic evaluation systems.
  • The methods facilitate accurate estimation of breeding values, advancing breeding programs.
  • The approach is applicable to both complete and incomplete omics datasets.