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Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

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Title Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat
 
Creator Shahi, Dipendra
Jia Guo
Pradhan, Sumit
Afridi, Jahangir Khan
Avci, Muhsin
Khan, Naeem
McBreen, Jordan
Guihua Bai
Reynolds, Matthew P.
Foulkes, John Michael
Babar, Md Ali
 
Subject breeding
canopy
fruiting
genetic gain
genotypes
genotyping
harvest index
phenotypes
single nucleotide polymorphism
spikes
vegetation
wheat
genetics
biotechnology
 
Description Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.
 
Date 2022-04-12
2023-04-23T10:53:53Z
2023-04-23T10:53:53Z
 
Type Journal Article
 
Identifier Shahi, D., Guo, J., Pradhan, S., Khan, J., Avci, M., Khan, N., McBreen, J., Bai, G., Reynolds, M., Foulkes, J. and Babar, M.A. 2022. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat. BMC Genomics, 23(1), 298. https://hdl.handle.net/10883/22064
1471-2164
https://hdl.handle.net/10568/130122
https://hdl.handle.net/10883/22064
https://doi.org/10.1186/s12864-022-08487-8
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher Springer Science and Business Media LLC
 
Source BMC Genomics