Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat
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.
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BioMed Central
2022
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, BREEDING, CANOPY, FRUITING, GENETIC GAIN, GENOTYPES, GENOTYPING, HARVEST INDEX, PHENOTYPES, SINGLE NUCLEOTIDE POLYMORPHISM, SPIKES, VEGETATION, WHEAT, |
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dig-cimmyt-10883-220642024-01-26T21:20:32Z Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat Shahi, D. Jia Guo Pradhan, S. Afridi, K. Avci, M. Khan, N. McBreen, J. Bai, G.H. Reynolds, M.P. Foulkes, M.J. Babar, A. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT 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. 2022-05-06T00:20:13Z 2022-05-06T00:20:13Z 2022 Article Published Version https://hdl.handle.net/10883/22064 10.1186/s12864-022-08487-8 English https://www.ncbi.nlm.nih.gov//bioproject/PRJNA578088 https://figshare.com/articles/dataset/Additional_file_1_of_Multi-trait_genomic_prediction_using_in-season_physiological_parameters_increases_prediction_accuracy_of_complex_traits_in_US_wheat/19588827 Nutrition, health & food security Accelerated Breeding Genetic Innovation CGIAR Trust Fund United States Department of Agriculture (USDA) https://hdl.handle.net/10568/130122 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access London (United Kingdom) BioMed Central 1 23 1471-2164 BMC Genomics 298 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT Shahi, D. Jia Guo Pradhan, S. Afridi, K. Avci, M. Khan, N. McBreen, J. Bai, G.H. Reynolds, M.P. Foulkes, M.J. Babar, A. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
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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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BREEDING CANOPY FRUITING GENETIC GAIN GENOTYPES GENOTYPING HARVEST INDEX PHENOTYPES SINGLE NUCLEOTIDE POLYMORPHISM SPIKES VEGETATION WHEAT |
author |
Shahi, D. Jia Guo Pradhan, S. Afridi, K. Avci, M. Khan, N. McBreen, J. Bai, G.H. Reynolds, M.P. Foulkes, M.J. Babar, A. |
author_facet |
Shahi, D. Jia Guo Pradhan, S. Afridi, K. Avci, M. Khan, N. McBreen, J. Bai, G.H. Reynolds, M.P. Foulkes, M.J. Babar, A. |
author_sort |
Shahi, D. |
title |
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
title_short |
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
title_full |
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
title_fullStr |
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
title_full_unstemmed |
Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
title_sort |
multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in us wheat |
publisher |
BioMed Central |
publishDate |
2022 |
url |
https://hdl.handle.net/10883/22064 |
work_keys_str_mv |
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