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

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Published on: June 21, 2018

Genomic Predicted cross performance: a tool for optimizing parental combinations in breeding programs.

Christine Nyaga^1,2, Marlee R Labroo³, Agre Paterne⁴

¹Section of Plant Breeding and Genetics, School of Intergrative Plant Sciences, Cornell University, 236 Tower Road, Ithaca, NY 14853, United States.

Database : the Journal of Biological Databases and Curation

|November 17, 2025

Summary

This summary is machine-generated.

A new genomic predicted cross-performance (GPCP) tool improves crop breeding by predicting the performance of parental combinations, especially for traits influenced by dominance. This genomic prediction method enhances crossing strategies for faster genetic gains.

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Area of Science:

Plant breeding
Quantitative genetics
Genomics

Background:

Genomic prediction accelerates genetic gains by estimating breeding values.
Traditional genomic prediction focuses on additive effects.
Predicting cross-performance offers greater value for optimizing parental combinations.

Purpose of the Study:

Introduce the genomic predicted cross-performance (GPCP) tool.
Evaluate GPCP's effectiveness against traditional genomic estimated breeding values (GEBVs).
Highlight scenarios where cross-performance prediction is more advantageous.

Main Methods:

Developed the GPCP tool using a mixed linear model incorporating additive and directional dominance.
Integrated GPCP into the BreedBase environment and as an R package.
Assessed GPCP using simulated traits with varying dominance and four yam traits.

Main Results:

The GPCP tool outperformed traditional GEBVs for traits with significant dominance effects.
GPCP effectively identified optimal parental combinations for enhanced crossing strategies.
Demonstrated superiority in predicting cross-performance over individual GEBVs.

Conclusions:

The GPCP tool offers a robust solution for predicting cross-performance, benefiting breeding programs targeting dominance-influenced traits.
GPCP is particularly valuable for clonally propagated crops facing inbreeding depression and heterosis.
The tool enhances crossing strategies, leading to improved genetic outcomes.