Xintian Zhu, Hans Peter Maurer, Mario Jenz, Volker Hahn, Arno Ruckelshausen, Willmar L. Leiser & Tobias Würschum

Theoretical and Applied Genetics; February 2022; vol. 135: 653–665

Key message

The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL.

Abstract

Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

See: https://link.springer.com/article/10.1007/s00122-021-03997-7

Fig. 1: Characterization of the NIRS data. a Raw NIRS profiles of triticale grain samples from HOH in 2014. The black line shows the average and the yellow lines are individual genotypes that illustrate the variation. b Correlations among all 1165 wavelengths. c Proportion of genotypic, genotype-by-environment interaction and residual variance of each wavelength along the NIR spectrum. d Discriminant analysis of principal components (DAPC) scatter plot of 1216 triticale genotypes based on NIRS data or molecular markers. Three groups are shown: the diversity panel (n = 846), and the doubled haploid populations DH1 (n = 180) and DH2 (n = 190). The top left inset shows the variance explained by retained principal components and the inset bottom left graph shows the variance explained by the discriminant functions. The crosses refer to the center of each group. e Correlation (r) between the traits grain yield (GY), thousand-kernel weight (TKW), plant height (PH), powdery mildew (PM) and yellow rust (YR) BLUEs and NIRS BLUEs across environments shown along the entire spectrum