Transposable element polymorphisms improve prediction of complex agronomic traits in rice

• DOI: 10.1007/s00122-022-04180-2
• Publication Date: 2022-08-05T18:02:47Z
• Authors: Ioanna-Theoni Vou...; Raúl Castanera; Sebastián E. Ramo...; Josep M. Casacuberta; Miguel Pérez-Enciso
• Keywords: 0301 basic medicine; 2. Zero hunger; Rice population; 0303 health sciences; Key message; Training sets; Kernel Hilbert space; Bayes Theorem; Oryza; Single nucleotide polymorphisms; Bayesian; Polymorphism, Single Nucleotide; 03 medical and health sciences; Phenotype; DNA Transposable Elements; Agronomic traits; Original Article; Genetic variation; Transposable elements; Predicted performance

Abstract

Key message

Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set.

Abstract Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30–50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.