Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.)

Authors: ATANDA, SIKIRU - North Dakota State University; STEFFES, JENNA - North Dakota State University; LAN, YANG - North Dakota State University; AL BARI, MD ABDULLAH - North Dakota State University; KIM, JEONGHWA - North Dakota State University; MORALES, MARIO - North Dakota State University; JOHNSON, JOSEPHINE - North Dakota State University; SALUDARES, RICA - North Dakota State University; WORRAL, HANNAH - North Dakota State University; PICHE, LISA - North Dakota State University; ROSS, ANDREW - North Dakota State University; Grusak, Michael; Coyne, Clarice - Clare; McGee, Rebecca; RAO, JIAJIA - North Dakota State University; BANDILLO, NONOY - North Dakota State University

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/10/2022
Publication Date: 10/3/2022
Citation: Atanda, S.A., Steffes, J., Lan, Y., Al Bari, M., Kim, J., Morales, M., Johnson, J., Saludares, R.A., Worral, H., Piche, L., Ross, A., Grusak, M.A., Coyne, C.J., McGee, R.J., Rao, J., Bandillo, N. 2022. Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.). The Plant Genome. 2022. Article e20260. https://doi.org/10.1002/tpg2.20260.
DOI: https://doi.org/10.1002/tpg2.20260

Interpretive Summary: Plant breeders are continually working to develop new varieties of nutritious and high yielding peas for farmers to grow and grocers to sell. Because nutritional traits are controlled by numerous genes, plant breeders have developed mathematical modeling approaches to predict gene combinations that will provide nutritional trait improvement. In this study, we tested different ways to associate the pea trait data with gene locations and whether these could improve the predictive ability of different breeding models. Our results identified new approaches that should enable breeders to more effectively and efficiently make improvements in these complex plant traits and release more nutritious pea varieties to farmers and ultimately consumers.

Technical Abstract: The superiority of multi-trait genomic selection (MT-GS) over univariate genomic selection (UNI-GS) can be improved by redesigning the phenotyping strategy. In this study, we used about 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and about 200 USDA accessions evaluated for ten nutritional traits to assess the efficiency of sparse testing in MT-GS. Our results showed that sparse phenotyping using MT-GS consistently outperformed UNI-GS when compared to partially balanced phenotyping using MT-GS. This strategy can be further extended to multi-environment multi-trait GS to improve prediction performance and reduce the cost of phenotyping and time-consuming data collection process. Given that MT-GS relies on borrowing information from genetically correlated traits and relatives, consideration should be given to trait combinations in the training and prediction sets to improve model parameters estimate and ultimately prediction performance. Our results point to heritability and genetic correlation between traits as possible parameters to achieve this objective.