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

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

SUBMITTER: Shahi D 

PROVIDER: S-EPMC9004054 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature