Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments

Abstract

Heading time is a major determinant of the adaptation of wheat to different environments, and is critical in minimizing risks of frost, heat, and drought on reproductive development. Given that major developmental genes are known in wheat, a process-based model, APSIM, was modified to incorporate gene effects into estimation of heading time, while minimizing degradation in the predictive capability of the model. Model parameters describing environment responses were replaced with functions of the number of winter and photoperiod (PPD)-sensitive alleles at the three VRN1 loci and the Ppd-D1 locus, respectively. Two years of vernalization and PPD trials of 210 lines (spring wheats) at a single location were used to estimate the effects of the VRN1 and Ppd-D1 alleles, with validation against 190 trials (~4400 observations) across the Australian wheatbelt. Compared with spring genotypes, winter genotypes for Vrn-A1 (i.e. with two winter alleles) had a delay of 76.8 degree days (°Cd) in time to heading, which was double the effect of the Vrn-B1 or Vrn-D1 winter genotypes. Of the three VRN1 loci, winter alleles at Vrn-B1 had the strongest interaction with PPD, delaying heading time by 99.0 °Cd under long days. The gene-based model had root mean square error of 3.2 and 4.3 d for calibration and validation datasets, respectively. Virtual genotypes were created to examine heading time in comparison with frost and heat events and showed that new longer-season varieties could be heading later (with potential increased yield) when sown early in season. This gene-based model allows breeders to consider how to target gene combinations to current and future production environments using parameters determined from a small set of phenotyping treatments.

A simple approach to predict growth stages in winter wheat (Triticum aestivum L.) combining prediction of a crop model and marker based prediction of the deviation to a reference cultivar: A case study in France

“…Other studies have been carried out on marker-based modeling for major crops (Reymond et al, 2003;White and Hoogenboom, 2003;Nakagawa et al, 2005;Quilot et al, 2005;Yin et al, 2005;Malosetti et al, 2006;Messina et al, 2006;White et al, 2008;Uptmoor et al, 2011;Zheng et al, 2013;Bogard et al, 2014). All of these consisted in finding a statistical model relating directly parameters values of the crop model to genes or genetic markers.…”

“…Attempts have been made to establish marker-based models for leaf elongation rate in Zea maize (Reymond et al, 2003), Prunus quality (Quilot et al, 2005), phenology in Glycine max (Messina et al, 2006), leaf senescence in Solanum tuberosum (Malosetti et al, 2006), flowering date in Oriza sativa (Nakagawa et al, 2005), in Phaseolus vulgaris (White and Hoogenboom, 2003), in wheat (White et al, 2008) and in Brassica oleracea (Uptmoor et al, 2010). In particular, (White et al, 2008;Zheng et al, 2013;Bogard et al, 2014) proposed marker-based models to predict cultivars genetic model parameters using multiple linear regression models with genetic markers as predictors and obtained RMSEP of ca. 4-5 days for heading date.…”

Drivers of trends in Australian canola productivity and future prospects

“…Nelson et al (2016) describe the potential application of genomics to improve the phenological adaptation of canola which is a key driver for higher productivity in different and changing environments. Understanding the genetics controlling responses to vernalisation and photoperiod in wheat, and using them as markers in breeding programs and in predictive models (Zheng et al 2013), can unlock tremendous potential to tailor new varieties to specific environments with significant increases in yield potential. This vision is now targeted for canola (Nelson et al 2016).…”

Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design

“…Generally GxE interaction will be more complex (Tardieu 2003;Cooper et al 2014), hampering the calculation of parameter values as functions of QTLs. The effort to explicitly link model parameters to the genome remains an active area of research (Chenu et al 2009;Hammer et al 2006;Tardieu 2003;Xu et al 2011;Zheng et al 2013).…”