The performance of phenomic selection depends on the genetic architecture of the target trait

Abstract

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.

Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses

“…This genomic structure impact on genomic prediction models was also reported previously (Riedelsheimer et al 2013;Schopp et al 2017;Werner et al 2020). The absence of impact of genomic structure on PP is in accordance with Zhu et al (2021Zhu et al ( , 2022 and Weiß et al (2022) studies. Therefore, HBLUP models should be preferred to predict complex traits for genetically diverse germplasm.…”

“…Brault et al (2022) also reported a low correlation between NIRS-based H matrix and SNP-based K matrix using a diversity panel of grapevine. A single study for triticale revealed a H matrix well correlated to the genomic (Zhu et al 2022). The low correlations between H and K matrices, might result from different histories of domestication and selection or from different range of considered genetic diversity.…”

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

“…It is also important to note that hybrids used in G2F contains the high genetic diversity, it is likely to expect the high prediction accuracies that does not necessarily reflect a public or special breeding populations ( Windhausen et al 2012 ), but G2F hybrid population is still useful population to compare temporal phenomic and GPs. Successful phenomic prediction studies reported to date have used NIRS data; some of phenomic prediction results performed favorably in comparisons with GP ( Rincent et al 2018 ; Lane et al 2020 ; Lane and Murray 2021 ; Zhu et al 2021 ; Robert et al 2022 ; Weiß et al 2022 ; Zhu et al 2022 ). Drone image-derived data have also been used as complementary data in GP that increased prediction accuracy of grain yield in wheat ( Rutkoski et al 2016 ; Sun et al 2019 ; Galán et al 2020 ; Krause et al 2020 ).…”

Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

“…The advantages include low cost and robustness across different environments ( Rincent et al, 2018 ). The phenomic selection performed more accurately for complex traits such as grain yield than traits controlled by a few genes ( Zhu et al, 2022 ). Since drought and heat tolerance are considered complex traits, peanut breeders are expected to benefit from phenomic selection and reduce the labor and time required for screening diverse populations.…”