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Página principal > Artículos > Artículos publicados > Transposable element polymorphisms improve prediction of complex agronomic traits in rice |
Fecha: | 2022 |
Resumen: | 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. |
Ayudas: | Agencia Estatal de Investigación PID2019-106374RB-I00 Agencia Estatal de Investigación PID2020-119255GB-I00 Agencia Estatal de Investigación PID2019-108829RB-I00 Ministerio de Ciencia e Innovación BES-2017-081139 Agencia Estatal de Investigación CEX2019-000902-S |
Nota: | Altres ajuts: acords transformatius de la UAB |
Derechos: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Lengua: | Anglès |
Documento: | Article ; recerca ; Versió publicada |
Materia: | Agronomic traits ; Bayesian ; Genetic variation ; Kernel Hilbert space ; Key message ; Predicted performance ; Rice population ; Single nucleotide polymorphisms ; Training sets ; Transposable elements |
Publicado en: | Theoretical and Applied Genetics, Vol. 135, Issue 9 (September 2022) , p. 3211-3222, ISSN 1432-2242 |
12 p, 2.6 MB |