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Updated: Jan 22, 2026

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Published on: October 5, 2012

Training set determination for genomic selection.

Jen-Hsiang Ou¹, Chen-Tuo Liao²

¹Department of Agronomy, National Taiwan University, Taipei, Taiwan.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|July 4, 2019

Summary

This summary is machine-generated.

A new method optimizes training sets for genomic selection by maximizing prediction accuracy. This approach improves genomic estimated breeding values (GEBVs) by considering genomic relationships for better variance and bias control.

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Area of Science:

Quantitative Genetics
Bioinformatics
Animal and Plant Breeding

Background:

Genomic selection (GS) models predict breeding values using genomic data.
Optimizing training set selection is crucial for accurate GS model performance.
Existing methods may not fully leverage genomic relationships between training and test sets.

Purpose of the Study:

To propose a novel optimality criterion for selecting training sets in genomic selection.
To develop an efficient algorithm for identifying optimal training subsets from large candidate populations.
To provide R functions for implementing the training set determination algorithms.

Main Methods:

Developed a new criterion based on Pearson's correlation between GEBVs and test set phenotypes.
Implemented an efficient algorithm to select optimal subsets from genotyped, unphenotyped candidate individuals.
Utilized whole-genome regression with ridge regression for marker effect estimation.
Applied the method to rice and wheat datasets with varying population structures.

Main Results:

The proposed method demonstrated advantages over existing approaches.
The new criterion effectively balances variance and bias in GEBV prediction.
The R package TSDFGS provides accessible tools for implementing the algorithms.
The method is robust across different degrees of population structure.

Conclusions:

The new optimality criterion and algorithm enhance training set selection for genomic selection.
Accurate GEBV prediction is improved by fully utilizing genomic relationships.
The provided R functions facilitate practical application in breeding programs.