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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03533naa a2200325 4500 | |
| 001 | oai:slubar.slu.se:106649 | |
| 003 | SwePub | |
| 008 | 240410s2020 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://res.slu.se/id/publ/1066492 URI |
| 024 | 7 | a https://doi.org/10.1371/journal.pone.02326652 DOI |
| 040 | a (SwePub)slu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Jahoor, Ahmedu Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för växtförädling,Department of Plant Breeding,Nordic Seed A/S4 aut0 (Swepub:slu)47171 |
| 245 | 1 0 | a Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data |
| 264 | c 2020-05-13 | |
| 264 | 1 | b Public Library of Science (PLoS),c 2020 |
| 264 | 1 | b Public Library of Science,c 2024 |
| 520 | a 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. | |
| 650 | 7 | a LANTBRUKSVETENSKAPERx Bioteknologi med applikationer på växter och djurx Genetik och förädling inom lantbruksvetenskap0 (SwePub)404022 hsv//swe |
| 650 | 7 | a AGRICULTURAL SCIENCESx Agricultural Biotechnologyx Genetics and Breeding in Agricultural Sciences0 (SwePub)404022 hsv//eng |
| 710 | 2 | a Sveriges lantbruksuniversitetb Institutionen för växtförädling4 org |
| 710 | 2 | a Sveriges lantbruksuniversitet |
| 773 | 0 | t PLoS ONEd : Public Library of Science (PLoS)g 15q 15x 1932-6203 |
| 856 | 4 | u https://pub.epsilon.slu.se/id/eprint/17218/contentsx primaryx Raw objectx freey FULLTEXT |
| 856 | 4 | u https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0232665&type=printable |
| 856 | 4 8 | u https://res.slu.se/id/publ/106649 |
| 856 | 4 8 | u https://doi.org/10.1371/journal.pone.0232665 |
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