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Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data

Tsai, Hsin-Yuan and Cericola, Fabio and Edriss, Vahid and Andersen, Jeppe Reitan and Orabiid, Jihad and Jensen, Jens Due and Jahoor, Ahmed and Janss, Luc and Jensen, Just (2020). Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data. PLoS ONE. 15 , e0232665 , 1-14
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Abstract

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Authors/Creators:Tsai, Hsin-Yuan and Cericola, Fabio and Edriss, Vahid and Andersen, Jeppe Reitan and Orabiid, Jihad and Jensen, Jens Due and Jahoor, Ahmed and Janss, Luc and Jensen, Just
Title:Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data
Year of publishing :2020
Volume:15
Article number:e0232665
Number of Pages:14
Publisher:PLoS
ISSN:1932-6203
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
URN:NBN:urn:nbn:se:slu:epsilon-p-106649
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-106649
Additional ID:
Type of IDID
DOI10.1371/journal.pone.0232665
Web of Science (WoS)000537481000029
ID Code:17218
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:10 Jul 2020 08:08
Metadata Last Modified:10 Jul 2020 08:08

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