Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs. Issue 3 (1st November 2021)

• Record Type: Journal Article
• Authors: Merrick, Lance F.; Carter, Arron H.
• Is Part Of: œplant genome. Volume 14:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-01
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20158
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat ( Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40, 368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross‐validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross‐validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years. Core Ideas: Machine learning models can increase accuracy but can be inconsistent in individual years. Combining training populations over environments can increase the accuracy of complex traits. Breeding lines can increase accuracy over mapping populations within breeding programs. Parametric models can be used efficiently to predict complex traits in breeding programs. Choice of model depends on structure of training population.