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Related Experiment Video

Updated: May 31, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Accuracy of multi-trait genomic selection using different methods.

Mario P L Calus¹, Roel F Veerkamp

¹Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Lelystad, The Netherlands. mario.calus@wur.nl

Genetics, Selection, Evolution : GSE

|July 7, 2011

Summary

This summary is machine-generated.

Genomic selection models improve accuracy for difficult-to-measure traits. Multi-trait models combining indicator traits with genomic data enhance selection accuracy, especially when genetic correlation is high.

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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 Genetics
Plant Genetics
Quantitative Genetics

Background:

Genomic selection is a key tool in animal and plant genetics.
It is particularly useful for traits that are difficult or expensive to measure.
Genomic selection can be applied across different environments.

Purpose of the Study:

Develop and compare multi-trait genomic selection models.
Combine scarce trait data with genetically correlated indicator traits.
Evaluate model performance against single-trait models using simulated data.

Main Methods:

Utilized three Single Nucleotide Polymorphism (SNP) based models: G, BCπ0, and BSSVS.
Model BSSVS sampled SNP effects to identify quantitative trait loci.
Included a pedigree-based model (A) for comparison.

Main Results:

The BSSVS model showed the highest accuracy for animals without phenotypes.
Multi-trait SNP models increased accuracy for juvenile animals by up to 0.10.
Accuracy gains for animals with indicator trait phenotypes varied with genetic correlation (up to 0.14).

Conclusions:

Genotyping is more effective than phenotyping indicator traits when genetic correlation is below 0.5.
Indicator traits improve accuracy more when genetic correlation exceeds 0.5.
Optimal strategy depends on heritability and sample size, requiring further derivation.