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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., Open Access Funding provided by Universitat Autonoma de Barcelona. The project was funded by Ministry of Science and Innovation-State Research Agency (AEI, Spain, https://doi.org/10.13039/501100011033) grant numbers PID2019-106374RB-I00 to JMC, PID2020-119255 GB-I00 to SERO and PID2019-108829RB-I00 to MPE. ITV is supported by a predoctoral fellowship funded by MCIN/AEI/https://doi.org/10.13039/501100011033 through the Grant BES-2017–081139 and by “ESF Investing in your future.” RC holds a Juan de la Cierva Incorporación Postdoctoral fellowship funded by the Spanish Ministry of Science and Innovation-State Research Agency. This work was also supported by grant CEX2019-000902-S funded by MCIN/AEI/https://doi.org/10.13039/501100011033 and by the CERCA Programme/Generalitat de Catalunya (Spain)., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000902-S)

Altres ajuts: acords transformatius de la UAB, 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.