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.

Keywords: Crop model; QTL; Triticum spp.; flowering; phenology; photoperiod; vernalization..

Fig. 1.

Daily minimum (MinT) and maximum (MaxT) temperature and day length for natural and extended treatments during growth season. The day length was extended to midnight using lights (3300 luminous flux).

Fig. 2.

The locations of calibration (blue) and validation (red) datasets with circle size indicating the number of observations in 190 trials across the Australian wheatbelt (grey shaded region).

Fig. 3.

Estimated heading time difference (°Cd) between V2P2 (‘control’) and other treatments (see text for description of REML method). The MLGs are for Vrn-A1, Vrn-B1, Vrn-D1, and Ppd-D1 loci (see Table 2). The differences between V1P2 and V2P2 (V1P2 – V2P2) indicate VRN effects, and those between V2P1 and V2P2 (V2P1 – V2P2) indicate PPD effects. Results are shown as ±standard deviation.

Fig. 4.

Comparison between observed and simulated heading times for calibration datasets using APSIM-Wheat-G. These trials were conducted over 2 years in South Perth, Western Australia, with four treatments: natural and pre-VRN (V1 and V2, respectively), and natural and extended PPD (P1 and P2, respectively). The total RMSE was 4.1 (y=1.06x+4.9, P <0.001, N=1359; R ²=0.86, dashed line; 1:1, solid line).

Fig. 5.

Comparison between observed and simulated heading times for validation datasets using APSIM-Wheat- G (A), and simulated heading times between APSIM-Wheat-M and APSIM-Wheat-G for calibration and validation datasets together (B) with a linear fit indicated by dashed line, and 1:1 by a solid line.

Fig. 6.

Comparison between heading times (day of year, DOY) and sowing times (DOY) for a subset of selected sites and years where large numbers of lines were assessed at multiple sowing times. The lower and upper solid lines show the heading times of the virtual shortest-season lines and longest-season lines, respectively. The shortest-season lines had spring alleles of Vrn-A1, Vrn-B1, and Vrn-D1, an insensitive allele of Ppd-D1, and TT _FI,FL of 455 °Cd. The longest-season lines had winter alleles of Vrn-A1, Vrn-B, and Vrn-D1, a sensitive allele of Ppd-D1, and TT _FI,FL of 1025 °Cd. The coloured strips show the distribution of the number of wheat lines in a trial with a specific observed heading time at a specific sowing date in the validation datasets.

Fig. 7.

Median heading times simulated by APSIM-Wheat-G at 1479 locations across the Australian wheatbelt for 1 June sowing (1960–2009). The map shows heading times for four virtual wheat genotypes with a winter allele at Vrn-A1 (v), spring alleles at Vrn-B1 and Vrn-D1 (a), a PPD-sensitive allele at Ppd-D1 (b), and an intermediate thermal time from floral initiation to flowering (400–1000 °Cd). The similar cultivars include Baxter and Ellison.

Fig. 8.

Impact of sowing time and allele combinations for VRN1 and Ppd-D1 genes and TT _FI,FL on heading times compared with the occurrence of extreme-temperature events (for example, Moree in New South Wales for 1960–2009). The boxplot shows the variation in heading time (x-axis) for different sowing times (y-axis: every half month from 15 April to 15 July) and for MLGs. The MLGs are the alleles of Vrn-A1, Vrn-B1, Vrn-D1, and Ppd-D1. a and v indicate homozygous genotypes for the spring and winter alleles of the VRN1 genes, respectively, while a and b indicate homozygous genotypes for the insensitive and sensitive alleles of Ppd-D1, respectively. The panels show a range of different TT _FI,FL from 400 to 1000 °Cd. Probabilities of last frost days (left solid line) and first heat days (right solid line) are calculated as the percentile of last frost days (<0 °C) and first heat days (>35 °C) from 1960 to 2009. The lower horizontal dashed line indicates the date of 10% risk of last frost day (x-axis) and the upper horizontal dashed line indicates the date of 30% risk of first heat day (x-axis). The low-risk period for frost and heat is highlighted in grey and helps to determine the best sowing window, on the y-axis. See Zheng et al. (2012) for methods to calculate last frost days, first heat days, and the low-risk window.