Euphytica 135: 255–263, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands.
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Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments Vinesh Verma1 , M.J. Foulkes2 , A.J. Worland† , R. Sylvester-Bradley3 , P.D.S. Caligari4 & J.W. Snape1,∗ 1 John
Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, U.K.; 2 Division of Agricultural Sciences, School of Biosciences, University of Nottingham, Loughborough, Leics LE12 5RD, U.K.; 3 ADAS Boxworth, Boxworth, Cambridge, CB3 8NN, U.K.; 4 School of Plant Science, University of Reading, Reading RG6 6AS U.K., Present address: Instituto de Biologia Vegetal y Biotecnologia, Universidad de Talca, 2 Norte 685, Talca, Chile; † Deceased; (∗ Author for correspondence: e-mail:
[email protected]) Received 26 August 2002; accepted 7 April 2003
Key words: drought, QTL, senescence, wheat, yield
Summary The timing of flag leaf senescence (FLS) is an important determinant of yield under stress and optimal environments. A doubled haploid population derived from crossing the photoperiod-sensitive variety Beaver, with the photoperiod-insensitive variety Soissons, varied significantly for this trait, measured as the percent green flag leaf area remaining at 14 days and 35 days after anthesis. This trait also showed a significantly positive correlation with yield under variable environmental regimes. QTL analysis based on a genetic map derived from 48 doubled haploid lines using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers, revealed the genetic control of this trait. The coincidence of QTL for senescence on chromosomes 2B and 2D under drought-stressed and optimal environments, respectively, indicate a complex genetic mechanism of this trait involving the re-mobilisation of resources from the source to the sink during senescence.
Introduction Leaf senescence is the sequence of biochemical and physiological events comprising the final stage of leaf development from the mature, fully extended state, until death. It is induced either by internal hormonal factors related to ageing, or, prematurely, by external environmental factors such as high temperature and drought (Chandler, 2001). In wheat (Triticum aestivum L.), flag leaf senescence (FLS) relates to the period of reallocating resources from the source to the sink during grain filling. Since flag leaf photosynthesis in wheat contributes about 30–50% of the assimilates for grain filling (Sylvester-Bradley et al., 1990), the onset and rate of senescence are important factors for determining yield potential (Evans, 1993). Four classes of delayed senescence or ‘stay-green’ were
described by Thomas & Smart (1993). Two of these relate to functionally stay-green effects, corresponding to (i) delayed onset of senescence or (ii) slower rate in progress of senescence, whereas the remaining two classes relate to cosmetic effects that lack photosynthetic capability. Although there have been few studies on the inheritance of flag leaf senescence in wheat under optimal conditions, additive gene effects have been demonstrated in the genetic control of flag leaf area duration (Simon, 1999). Genetically determined late onset of leaf senescence in sorghum (Sorghum bicolour L.) (Borrell et al., 2000a, 2000b), maize (Zea mays L.) (Baenziger et al., 1999) and durum wheat (T. durum L.) (Benbella & Paulsen, 1998; Hafsi et al., 2000) has increased yield under water-stressed environments. A slower rate of senescence has also been associated
256 with genetic yield increases under drought in sorghum (Borrell et al., 2000a, 2000b). Because of these observations, understanding the genetics of leaf senescence is valuable for any plant breeder with the objective of increasing yield under drought. Furthermore, this understanding provides scientists with knowledge as to how genes and biochemical pathways controlling leaf senescence are regulated. Molecular markers improve the efficiency of breeding by allowing manipulation of the genome through marker-assisted selection. In order to identify molecular markers for flag leaf senescence, it is first necessary to construct a genetic map as a tool for discovering the genetic factors as quantitative trait loci (QTL). Though QTLs influencing senescence have been identified both in sorghum (Tuinstra et al., 1997; Crasta et al., 1999; Xu et al., 2000; Kebede et al., 2001) and maize (Beavis et al., 1994), to date, there have been no reports of mapping QTL which control FLS affecting yield in wheat. This study was carried out to do this using a doubled haploid (DH) population derived from two European winter wheats, Beaver (a UK-bred, photoperiod-sensitive, semi-dwarf, feed cultivar) and Soissons (a French-bred, photoperiodinsensitive, semi-dwarf, bread-making wheat). The two parents were known to contrast for flag leaf senescence, with Soissons showing delayed senescence compared to Beaver (Foulkes et al., 1998). Thus, the objectives were to: 1) identify the extent of genetic differences in flag leaf senescence in the population; 2) map QTL(s) for flag leaf senescence under irrigated and drought conditions; and 3) determine if these QTL(s) explain genetic variation in grain yield under irrigated and drought conditions. An additional benefit of mapping QTLs for flag leaf senescence, other than identifying markers for plant breeders, is to allow comparative analysis with already discovered loci for ‘stay-green’ in other grass species like sorghum and maize.
Materials and methods Plant material A population of 48 DH lines was derived via the wheat × maize technique from the F1 between Beaver and Soissons. Soissons has good canopy persistence (Foulkes et al., 1998) while Beaver has poorer canopy persistence.
Field experiments Design and treatments In each of two seasons, 1999/2000 and 2000/2001, field experiments were carried out at an experimental site at Gleadthorpe, UK (55◦ 13’ N, 1◦ 6’ W) containing a random sample of 34 of the DH lines. The soil was loamy medium sand to 0.35 m over medium sand (Cuckney Series) with good drainage. Each experiment used a randomised block, split-plot design, in which two irrigation treatments (fully irrigated and unirrigated) were randomised as main-plots and the 34 DH lines and the two parents were randomised as sub-plots (1.4 × 5 m). Eight metre discards separated main-plots and there were three replicates. In the irrigated main-plots, water was applied using a linear overhead irrigator to maintain soil moisture deficit (SMD), calculated using the ADAS Irriguide model (Bailey & Spackman, 1996), to < 50% AW (70 mm) up to GS61+ 4 wks and < 75% AW (105 mm) thereafter. Seed rate was adjusted by genotype according to 1000-grain weight to achieve a target seed number of 325 per m2 ; rows were 0.12 m apart. In each experiment, 220 kg/ha nitrogenous fertilizer as ammonium nitrate was applied in a four-split programme; 40 kg/ha was applied in early March, 70 kg/ha in late March, 60 kg/ha in early May with the remainder in mid May. Prophylactic applications of fungicides were given at GS31, GS39 and GS59 (Tottman, 1987), to keep diseases to very low levels. No plant growth regulators were applied. Pesticides and herbicides were used as necessary to minimise the effects of pests and weeds. Crop measurements Flowering date: The date of GS61 was recorded on all sub-plots of each experiment. This was done by observing sub-plots every 2 to 3 days from the beginning of May to the end of June. The sub-plot was considered to have reached GS61 when, from a visual assessment of all shoots in the sub-plot, > 50% shoots were at GS61. Flag leaf senescence: In each experiment, for each line, the percentage of flag leaf area remaining green ( %GFLA) was measured by an overall visual assessment of all fertile shoots (those with an ear) in situ in the sub-plot at 14 (+14d) and 35 days (+35d) after flowering (GS61). These assessments were carried out by the same operator in each experiment in the two years to avoid any bias between operators influencing results.
257 Combine yield: In each experiment in each year, combine grain yields were measured on a 1.4 × 5m area of each sub-plot for each line. A sample of 250 g grain was also taken and the dry weight of the sample recorded after drying to constant weight at 80◦ C for 48 h, so that yields could be expressed as 85% DM. Map development Amplified fragment length polymorphism (AFLP) (Vos et al., 1995) and simple sequence repeat (SSR) technologies were used to develop the genetic map on 48 of the DH lines. For AFLP, DNA was isolated from leaf tissue of glasshouse-grown seedlings of all the DH and parental lines as described by SaghaiMaroof et al. (1984). Two hundred and fifty nanograms of genomic DNA were restricted and ligated in one step at 37 ◦ C and left overnight. Digestion was done with Sda1 (Helena Biosciences, UK) and Mse1 (New England Bio-labs, UK) and Sse8387I and Mse1 adapters used for ligation. Pre-amplification was performed with primers specific for Sse83871(5’-AGA CTG CGT ACA TGC AGG-3’) and Mse1(5’-GAC GAT GAG TCC TGA GTAA-3’) adapters and selective amplification was done using the same primers with two or three additional nucleotides. The Sse primers were labelled with p33 CTP to facilitate the detection of amplified fragments on Sequagel XR (National diagnostic, UK). Autoradiography was done using Kodak scientific-Biomax MR imaging film. For SSR analysis, PCR was performed using publicly available GWM (Roeder et al., 1998), PSP (Bryan et al., 1997) and wheat microsatellite consortium (unpublished) primer pairs. Fragments amplified by PCR for all the DH lines and parents were separated on 5% polyacrylamide gels at 90w for 1.5 h. The amplified fragments were visualized by silver staining. From the characterized genotype data, map construction was carried out using JoinMap version 2.0 (Stam, 1993). The Kosambi mapping function (Kosambi, 1944) was used to calculate the centiMorgan (cM) distances. Statistical and QTL analysis Mixed linear models were used for analyzing the phenotypic variation. Analyses of variance were performed using REML in Genstat 5, (Genstat Committee, 1993) taking Lines and Irrigation as fixed factors and Years and Blocks as random factors. The differences between the DH lines were assessed over
years, treatments and blocks for all the four traits studied (yield, %GFLA at two stages and heading date). Pearson’s correlation coefficients were also calculated between the means for the combined plot yields, %GFLA at the two stages, and flowering date. Initial QTL analysis was performed using the programme QTL-Café which is available over the internet (http: //web.bham.ack.uk/g.g.seaton/). Single marker ANOVA, marker regression analysis (Kearsey & Hyne, 1994) and interval mapping (Lander & Botstein, 1989) were applied to identify putative QTLs for the four traits. Once QTLs were identified, composite interval mapping was carried out using MQM analysis (Jansen & Stam, 1994) MapQTL version 3 (Van Ooijen & Maliepaard, 1997).
Results Phenotypic analysis The 1999/2000 season was characterised by rainfall above the long-term mean (LTM) in April and May and close to the LTM in June and July, and thus drought did not develop in the unirrigated conditions for any sustained period. As a consequence, the irrigation effect was not statistically significant on either flowering date, %GFLA or combine grain yield (Table 1). In 2000/2001, however, rainfall was close to the LTM in April and below average in May and June, and drought developed under the unirrigated conditions, the onset of which coincided with ear emergence in late May. Consequently, effects of irrigation were statistically significant for all traits except flowering date in that year (Table 1). Averaging across the 34 DH lines, drought decreased %GFLA from 86 to 49 at GS61+14d (p < 0.05) and from 51 to 7% at GS61+35d (p < 0.05) in this year. Grain yield at harvest was also decreased from 8.98 to 6.17 t/ha under drought (p < 001; Table 1). There was a nonsignificant trend for flowering date to be advanced, on average, by 1 day under drought. There were statistically significant differences amongst the DH lines in %GFLA at GS61+14d in 2000 (p < 0.001) and in 2001 under irrigated (p < 0.05) and unirrigated (p < 0.05) conditions (Table 1). Similarly, DH lines differed in %GFLA at GS61+35d and in grain yield in 2000 (p < 0.001), and in 2001 in both irrigation treatments (p < 0.001). Percentage GFLA at GS61+14d was greater for Soissons than Beaver under irrigated and drought conditions
258 Table 1a. Means of DH lines and two parents; and standard errors of the difference of the means (degrees of freedom in parenthesis) showing effect of irrigation on flowering date, %GFLA and combine grain yield in 2000
2000 Irrigated P1 (Beaver) P2 (Soissons) Mean of DH lines 2000 Unirrigated P1 (Beaver) P2 (Soissons) Mean of DH lines
Flowering date
%GFLA GS61+14d
%GFLA GS61+35d
Grain yield t/ha 85% DM
12 June 1 June 6 June
93.5 94.2 90.8
67.5 72.5 59.5
7.52 6.78 6.21
12 June 1 June 6 June
91.3 94.7 91.9
60.0 69.2 57.4
7.24 6.65 6.13
Sed Irrigation (2) Sed DH line (140) Sed Parents Sed Irr∗ DH line (140) Sed Irr∗ Parents
0.974 2.891 2.045 4.151 2.978
4.18 8.19 5.79 12.18 9.01
0.373 0.515 0.364 0.809 0.604
Table 1b. Means for DH lines and two parents; and standard errors of the difference of the means (degrees of freedom in parenthesis) showing effect of irrigation on flowering date, %GFLA and combine grain yield in 2001
2001 Irrigated P1 (Beaver) P2 (Soissons) Mean of DH lines 2001 Unirrigated P1 (Beaver) P2 (Soissons) Mean of DH lines
Flowering date
%GFLA GS61+14d
%GFLA GS61+35d
Grain yield t/ha 85% DM
26 June 16 June 20 June
85.5 93.3 85.7
56.7 70.3 51.2
10.27 9.41 8.98
24 June 15 June 19 June
48.3 60.0 48.5
10.7 10.0 7.3
7.14 6.52 6.17
Sed Irrigation (2) Sed DH line (140) Sed Parents Sed Irr∗ DH line (140) Sed Irr∗ Parents
(p < 0.05) in 2001, but there was only a weak non-significant trend for greater %GFLA for Soissons compared to Beaver in 2000. At GS61+35d, Soissons showed greater %GFLA than Beaver in 2001 under irrigated conditions but not under drought, where values for the two parents were similar. In 2000, Soissons again showed a trend for greater %GFLA than Beaver at this stage. In all environments (2000 irrigated and unirrigated; 2001 irrigated and unirrigated), however,
0.23 5.453 3.856 7.613 5.313
4.82 4.80 3.39 8.25 6.72
0.102 0.363 0.256 0.516 0.367
grain yield was greater for Beaver than Soissons, on average by 0.70 t ha−1 . Averaging across the two irrigation treatments in both years, there were differences amongst the DH lines and between the two parents in %GFLA at +14d and +35d, with Soissons exhibiting higher retention of green flag leaf area at both stages (p < 0.05 Table 1; Figures 1a and b). Beaver was, however, significantly higher yielding than Soissons (Table 1; p < 0.05)
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Figure 1. Phenotypic distributions of the DH lines for different traits (mean of irrigated and unirrigated treatments in 2000 & 2001).
(Figure 1c). As expected, there were also significant differences between the DH lines for flowering time, with the photoperiod-insensitive parent Soissons on average flowering eleven days earlier than Beaver (Figure 1d). Since there were no significant differences between irrigation treatments in 2000 for the four characters measured, data were pooled over the two treatments and the mean was used in the subsequent correlation and QTL analyses. For 2001, however, as there were significant differences in the treatments for traits, the two treatments were analysed separately. Correlation analysis In all three environments, there was a significant correlation between plot yield and %GFLA at GS61+35d amongst the DH lines (Table 2). For GS61+14d, though, the correlation was only significant in 2001 under unirrigated conditions. Although the correlations were significant, the amount of variation in yield data accounted for was generally small, in the order
of 20–30%. The correlations between flowering date and %GFLA were always non-significant, except for 2001 under irrigated conditions, where later flowering was associated with a more rapid senescence at GS61+35d. Similarly, correlations between flowering date and grain yield were generally not significant, except in one case (unirrigated treatment in 2001) where later flowering was associated with higher yields. Genetic linkage map The genetic map of the Beaver × Soissons DH population was developed on 48 DH lines using 170 polymorphic AFLP markers (out of 340 screened), anchored using 71 polymorphic SSR markers. These markers cover a map distance of 2290 cM, and represent the 21 wheat chromosomes. Of particular interest (see below) were chromosomes 2B and 2D, which had genetic lengths of 123.3 and 104.7 cM, respectively.
260 Table 2. Pearsons’ correlation coefficient between the means for senescence level, heading date and yield under three different environments
Environment
%GFLA +14d Yield Flowering
%GFLA +35d Yield Flowering
Yield Flowering
2000 2001, irrigated 2001, unirrigated
0.10NS(216) 0.08NS(107) 0.31∗∗∗(104)
0.23∗∗∗(217) 0.23∗∗∗(106) 0.40∗∗∗(107)
0.21NS(216) –0.01NS(106) 0.26∗∗∗(104)
–0.03NS(216) 0.15NS(106) –0.11NS(104)
0.07NS(217) –0.32∗∗∗(106) 0.09NS(107)
NS non significant, ∗∗∗ significant at p < 0.01 and df in parentheses.
Figure 2a. Mean additive effects of QTL for the three traits on chromosome 2D of wheat. The arrows indicate the significance level at p < 0.05, for the pointed traits.
QTL Analysis Four different analyses were carried out using QTLCafé for the four traits in each of the three environments: mean of year 2000, 2001 irrigated, and 2001 unirrigated. Marker regression, interval mapping and single marker ANOVA were used to discover putative QTL. The most significant results were for chromosomes 2D (Figure 2a) and 2B (Figure 2b) at a probability of p
