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

Content Provider	Springer Nature : BioMed Central
Author	Shahi, Dipendra Guo, Jia Pradhan, Sumit Khan, Jahangir AVCI, Muhsin Khan, Naeem McBreen, Jordan Bai, Guihua Reynolds, Matthew Foulkes, John Babar, Md Ali
Abstract	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.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-022-08487-8.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-022-08487-8
Journal	BMC Genomics
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2022-04-12
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Canopy temperature NDVI Genomic prediction Multi-trait genomic prediction Spike partitioning index Fruiting efficiency
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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