Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. (25th February 2020)

• Record Type: Journal Article
• Authors: Lyra, Danilo H; Virlet, Nicolas; Sadeghi-Tehran, Pouria; Hassall, Kirsty L; Wingen, Luzie U; Orford, Simon; Griffiths, Simon; Hawkesford, Malcolm J; Slavov, Gancho T
• Editors: Rebetzke, Greg
• Is Part Of: Journal of experimental botany. Volume 71:Number 6(2020)
• Page Start: 1885
• Page End: 1898
• Publication Date: 2020-02-25
• Subjects: Data smoothing -- dimensionality reduction -- dynamic QTLs -- factor-analytic model -- function-valued traits -- genomic selection -- phenomics; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/erz545
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : Functional analysis of longitudinal phenotypic data for five and 10 time points saturates QTL detection power and genomic predictive ability for canopy height in wheat. 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.