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Related Experiment Video

Updated: Jun 1, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Genomic selection for QTL-MAS data using a trait-specific relationship matrix.

Zhe Zhang¹, Xiangdong Ding, Jianfeng Liu

¹Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China. dj.dekoning@roslin.ed.ac.uk.

BMC Proceedings

|June 1, 2011

Summary

This summary is machine-generated.

The trait-specific additive genetic relationship matrix (TABLUP) method shows comparable accuracy to BayesB for predicting genomic estimated breeding values (GEBV). TABLUP offers a promising alternative for genomic selection using low-density markers.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Animal Breeding and Genetics
Quantitative Genetics
Genomic Selection

Background:

Genomic estimated breeding values (GEBV) are crucial for animal breeding.
Predicting GEBV accurately is essential for efficient genetic gain.
Existing methods like RRBLUP and BayesB have varying requirements and accuracies.

Purpose of the Study:

To evaluate the performance of the trait-specific additive genetic relationship matrix (TABLUP) method for GEBV prediction.
To compare TABLUP with established methods like RRBLUP and BayesB.
To assess the utility of TABLUP with low-density markers.

Main Methods:

GEBV were predicted using TABLUP, RRBLUP, and BayesB on the XIV QTL-MAS workshop dataset.
TABLUP utilizes a trait-specific marker-derived relationship matrix (TA).
Marker effects were estimated using RRBLUP and BayesB to construct the TA matrix for TABLUP, with subsets of markers explored for low-density selection.

Main Results:

Correlations between GEBV predictions from different methods exceeded 0.95.
TABLUP using 200+ selected markers achieved correlations >0.98 with BayesB (all markers).
TABLUP accuracy reached >0.67 with 100+ markers, nearly matching BayesB (all markers).

Conclusions:

TABLUP demonstrated performance nearly equal to BayesB.
TABLUP provides a viable alternative for GEBV prediction using low-density markers.
TABLUP is a promising method for genomic selection requiring further investigation.