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Research article2024Peer reviewedOpen access

Comparison of Single-Trait and Multi-Trait GBLUP Models for Genomic Prediction in Red Clover

Osterman, Johanna; Gutierrez, Lucia; Ohlund, Linda; Ortiz, Rodomiro; Hammenhag, Cecilia; Parsons, David; Geleta, Mulatu

Abstract

Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts.

Keywords

GBLUP; genomic prediction; genomic selection; longitudinal genomic prediction model; multi-trait genomic prediction model; pool-seq; red clover

Published in

Agronomy
2024, volume: 14, number: 10, article number: 2445

SLU Authors

Global goals (SDG)

SDG13 Climate action

UKÄ Subject classification

Plant Biotechnology
Genetics and Breeding in Agricultural Sciences
Agricultural Science

Publication identifier

  • DOI: https://doi.org/10.3390/agronomy14102445

Permanent link to this page (URI)

https://res.slu.se/id/publ/132936