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Use of Freeze-thawed Embryos for High-efficiency Production of Genetically Modified Mice

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Efficient genetic value prediction using incomplete omics data.

Matthias Westhues¹, Claas Heuer^2,3, Georg Thaller²

¹Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|January 19, 2019

Summary

This summary is machine-generated.

Covering a subset of individuals with omics data and imputing the rest using genomic or pedigree data can improve trait predictions in breeding. This approach enhances selection accuracy while managing costs effectively.

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Area of Science:

Quantitative genetics
Genomics
Bioinformatics

Background:

Accurate prediction of genetic values is crucial for effective selection in animal and plant breeding.
Novel 'omics'-based predictors offer improved accuracy over traditional genomic predictions for complex traits but are costly.
Existing datasets often have complete genetic marker data but incomplete 'omics' data.

Purpose of the Study:

To investigate the utility of the single-step prediction framework for imputing 'omics' data.
To assess the impact of imputing transcriptomic data on breeding value estimation.
To evaluate strategies for cost-effective trait prediction using 'omics' data.

Main Methods:

Utilized two maize datasets (inbred lines and hybrids).
Applied the single-step prediction framework to combine pedigree, genomic, and transcriptomic data.
Imputed transcriptomic data for individuals lacking direct measurements using pedigree or genomic information.

Main Results:

The single-step framework successfully imputed transcriptomic data.
Imputing transcriptomic data boosted prediction accuracy when its predictive ability surpassed that of pedigree or genomic data alone.
The strategy of covering a subset with 'omics' data and imputing others proved effective.

Conclusions:

Imputing 'omics' data within a single-step framework enhances trait prediction accuracy.
A cost-effective strategy involves measuring 'omics' data on a subset of individuals and imputing the rest.
This approach can significantly improve candidate selection, especially in hybrid breeding programs.