Open AccessMulti‐trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea
Author(s) -
Atanda Sikiru Adeniyi,
Steffes Jenna,
lan Yang,
Al Bari Md Abdullah,
Kim JeongHwa,
Morales Mario,
Johnson Josephine P.,
Saludares Rica,
Worral Hannah,
Piche Lisa,
Ross Andrew,
Grusak Mike,
Coyne Clarice,
McGee Rebecca,
Rao Jiajia,
Bandillo oy
Publication year - 2022
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.1002/tpg2.20260
Subject(s) - biology , heritability , selection (genetic algorithm) , univariate , trait , genomic selection , genetic gain , best linear unbiased prediction , breeding program , microbiology and biotechnology , predictive modelling , plant breeding , genetic correlation , computational biology , machine learning , genetic variation , genotype , genetics , computer science , multivariate statistics , gene , single nucleotide polymorphism , agronomy , cultivar , programming language
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.