Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

A - Papers appearing in refereed journals

Lyra, D. H., Virlet, N., Sadeghi-Tehran, P., Hassall, K. L., Wingen, L. U., Orford, S., Griffiths, S., Hawkesford, M. J. and Slavov, G. 2020. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform . Journal of Experimental Botany. p. erz545.

AuthorsLyra, D. H., Virlet, N., Sadeghi-Tehran, P., Hassall, K. L., Wingen, L. U., Orford, S., Griffiths, S., Hawkesford, M. J. and Slavov, G.
Abstract

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5–8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1–4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.

KeywordsData smoothing; Dimensionality reduction; Dynamic QTLs; Factor-analytic model; Function-valued traits; Genomic selection; Phenomics
Year of Publication2020
JournalJournal of Experimental Botany
Journal citationp. erz545
Digital Object Identifier (DOI)doi:10.1093/jxb/erz545
Web address (URL)https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erz545/5757976
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeDesigning Future Wheat (DFW) [ISPG]
Publisher's version
Supplemental file
Output statusPublished
Publication dates
Online25 Feb 2020
Publication process dates
Accepted19 Feb 2020
Submitted07 May 2019
PublisherOxford University Press (OUP)
ISSN0022-0957

Permalink - https://repository.rothamsted.ac.uk/item/97521/functional-qtl-mapping-and-genomic-prediction-of-canopy-height-in-wheat-measured-using-a-robotic-field-phenotyping-platform

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