Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.

Abstract

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.

Plant phenotyping relevance

疎な表現型測定から欠測植物形質を予測し、表現型取得コストを削減する計算的方法が研究の中心であるため、植物育種への応用であっても表現型推定手法として含める。

Code and data availability

The paper's phenotypic data (10 seed mineral/nutritional traits for NDSU breeding lines and USDA accessions) is not publicly deposited; the authors state it must be requested from the corresponding author. The only public deposit is the SNP genotyping dataset (SRA PRJNA730349), which is a molecular genomics deposit, a

No evidence-backed public reproduction asset is currently recorded.