Generation mean analysis of some physiological traits in the hybrid maize cv. SC704 under different water regimes

Document Type : Research Paper


1 Department of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Department of Water Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.

4 Khorasan Razavi Agriculture and Natural Resources Research Center, Mashhad, Iran.


This research was carried out using seven generations of MO17 (the paternal line), B73 (the maternal line), F1 (SC704), F2, F3,BC1 and BC2 to study the inheritance of chlorophyll fluorescence parameters and relative water content using generation mean analysis. The experiment was conducted in the research station of the University of Tabriz, Iran, with two replications for each generation under normal irrigation and two water deficit conditions. The existence of epistatic gene interaction was observed in controlling of F0, Fm, FV (in the severe stress condition),FV/Fm (in the normal condition), FV/F0 (in the normal and severe stress conditions) and RWC (in the normal and moderate and severe stress conditions). Duplicate epistasis was observed for all studied traits under the normal and water deficit conditions except RWC in the severe stress condition. Therefore, exploiting non-additive gene action and delaying the selection to advanced generations for physiological traits in maize was suggested to adopt in the programs aimed at developing drought-tolerant lines and hybrids.


Article Title [Persian]

تجزیه میانگین نسل ها برای برخی صفات فیزیولوژیک در هیبرید سینگل کراس 704 ذرت تحت رژیم های آبی مختلف

Authors [Persian]

  • مژگان شیرین پور 1
  • علی اصغری 1
  • سعید اهری زاد 2
  • علی رسول زاده 3
  • سعید خاوری خراسانی 4
1 گروه زراعت و اصلاح نباتات، دانشگاه محقق اردبیلی، دانشکده کشاورزی و منابع طبیعی، اردبیل.
2 گروه به نژادی و بیوتکنولوژی گیاهی، دانشکده کشاورزی، دانشگاه تبریز، تبریز.
3 گروه مهندسی آب، دانشگاه محقق اردبیلی، دانشکده کشاورزی و منابع طبیعی، اردبیل.
4 مرکز تحقیقات کشاورزی و منابع طبیعی خراسان رضوی، مشهد، ایران
Abstract [Persian]

تحقیق حاضر با استفاده از هفت نسل  MO17} به عنوان لاین پدری؛ B73  به عنوان لاین مادری؛ هیبرید F1) SC704 )؛ F2؛ F3؛ BC1؛ BC2} به منظور بررسی وراثت پارامترهای فلورسانس کلروفیل و محتوای آب نسبی توسط تجزیه میانگین نسل­ها انجام شد. این آزمایش در ایستگاه تحقیقاتی دانشگاه تبریز با استفاده از طرح بلوک­هایکامل تصادفی با دو تکرار در شرایط آبیاری نرمال و دو شرایط کمبود آب انجام شد. وجود اثرات متقابل اپیستازی در وراثتF0 ، Fm ، FV (در شرایط تنش شدید)،  FV/Fm(در شرایط عادی)، FV/F0  (در شرایط نرمال و تنش شدید) و) RWC در شرایط نرمال و تنش­های ملایم و شدید) مشاهده شد. اپیستازی مضاعف در کلیه صفات مورد مطالعه به جز RWC در شرایط تنش شدید، مشاهده شد. این نتایج حاکی از لزوم بهره برداری از اثرات غیرافزایشی ژنتیکی و به تأخیر انداختن گزینش صفات فیزیولوژیکی مورد مطالعه به نسل­های پیشرفته در برنامه­های اصلاح ذرت برای تحمل خشکی می­باشند.

Keywords [Persian]

  • تجزیه میانگین نسل ها
  • تنش کم آبی
  • ذرت
  • صفات فیزیولوژیکی
  • وراثت پذیری


Various abiotic stresses such as drought, reduce the growth and yield of plants below the optimum level (Cramer et al. 2011). Maize (Zea mays L.) as one of the most important crops in the world, is drought sensitive, and its yield significantly decreases under water stress conditions (Shiferaw et al. 2011; Todaka et al. 2015). Drought causes degradation of photosynthetic pigments and thus decreases the light energy absorption on the photochemical apparatus and damages thylakoid membranes and centers of photosystem reaction (Batra et al. 2014). The chlorophylls and PSII absorb the light energy



which can be used to drive photosynthesis processes. When this energy is lost from PSII, it appears as heat or emitted chlorophyll fluorescence (Kalaji et al. 2012). Chlorophyll fluorescence has been extensively used to study the variation in the efficiency of light processing and stress tolerance in plants (Lichtenthaler et al. 2005; Stirbet et al. 2018). Relative leaf water content (RWC) has also been utilized for the evaluation of drought tolerance in crops (Dhanda and Sethi 2002). Decreasing this trait causes stomata closure and CO2 assimilation declines under drought stress (Gindaba et al. 2004).

Studies about the inheritance of chlorophyll fluorescence components are very limited in maize. Hola et al. (2010) examined the response of several photosynthetic parameters of maize plants to drought stress in five inbred lines and their F1 hybrids. They reported that the nonadditive genetic effects were often more important than the additive effects for the photosynthetic characteristics. However, the genetic changed when the plants were exposed to drought conditions. Also, it was not possible to predict the response of F1 hybrids to drought based on the photosynthetic performance of their parents. Simic et al. (2014) conducted a quantitative trait loci (QTL) analysis on nine chlorophyll fluorescence parameters in maize at four environments that differed in weather conditions. The chlorophyll fluorescence parameters were analyzed among 205 recombinant inbred lines derived from the intermated B73 × Mo17 maize population. The results revealed 10 significant QTLs for seven chlorophyll fluorescence parameters, of which five were co-localized after combining over the four environments. These results indicated the role of polygenic inheritance in governing the chlorophyll fluorescence parameters under study. They also detected one pleiotropic locus on chromosome 7, coinciding with the gene gst23 that possibly related to efficient photosynthesis in different field conditions.

Generation mean analysis is a biometrical method for estimating the genetic effects including additive, dominance and especially epistatic interactions by using means of different generations (Mather and Jinks 1982; Viana 2000). Information about the nature and magnitude of gene actions involved in the tolerance to water deficit stress can be useful for breeding drought-resistant varieties in maize. Thus, a generation mean analysis was designed to study the inheritance of physiological characteristics in maize under different water regimes.


Materials and Methods

Plant material and experiment

Seven generations including B73 (the maternal line), MO17 ( the paternal line), SC704 (F1), F2, BC1, BC2 and F3 were grown in PVC pipes with 20 cm diameter and the height of one meter, under normal irrigation and two water regimes (55% and 75% of available water depletion) in the research station of the University of Tabriz. With consideration of a proper distance between the seeds, two maize seeds were sown per pipe to secure at least one plant appearance in each pipe. Then, thinning was done at the tillering stage and only one plant was left in a pipe. Eventually, 20 plants for each generation were evaluated. Plants were grown in similar conditions in terms of soil type, light, temperature and pipes size. Also, the experimental site was protected from the rainfall by a plastic cover throughout the cropping period.


Chlorophyll fluorescence parameters

Chlorophyll fluorescence was measured on a fully developed leaf using a hand-held chlorophyll fluorometer (Opti-Sciences, OS-30p). The leaves were adapted in dark for 15 min at first and then fluorescence was recorded at 650 μmol m-2 s-1. Fluorescence values recorded included: F0: minimum fluorescence, Fm: maximum fluorescence, FV: variable fluorescence = (Fm - F0), FV/Fm: maximum quantum yield of photosystem II (PS II) and FV/F0: maximum primary yield of the photochemistry of PS II.



The measurement of RWCwas accomplished by excising 1-cm disks from the fresh leaves. Five disks from each pipe were weighed immediately and the fresh weight (FW) was measured. Then, the leaf disks were placed in the distilled water for 24 h and the turgid weight (TW) was obtained. Finally, the samples were dried in an oven at 72 °C for 24 h and their dry weight (DW) was recorded. RWC was determined as follows (Salisbury and Ross 1998):

RWC = ((FW - DW) / (TW - DW)) × 100


Statistical and genetic analysis

Generation mean analysis was carried out using the weighted least squares method considering the inverse of the variance of the means within each generation as the weight (Mather and Jinks 1982). In this method, the overall average for each trait is shown as follows: Y = m + α [d] + β [h] + α2 [i] + 2αβ [j] + β2 [l]

Where, Y= the generation mean, m= F∞ metric, d= additive effects, h= dominance effects, i= additive × additive interaction, j= additive × dominance interaction, 1= dominance × dominance interaction and α, 2αβ and β2 are coefficients of genetic parameters. To verify the goodness of fit of the model, a joint scaling test was carried out for each trait at each irrigation level (Cavalli 1952). All genetic parameters were tested for significance using a t-test.


Results and Discussion

The results showed that the scaling tests for the three-parameter model were not significant for FV under the normal and mild stress conditions (55% AWD),FV/Fm under both stress conditions and FV/F0 under the mild stress condition, which indicates the lack of epistasis for these traits (Table 1). The scaling tests were significant for the other studied traits suggesting the inadequacy of the additive-dominance model or the presence of epistasis type of gene effects.

Estimates of genetic effects based on the three- or six-parameter models for the studied traits under different water regimesare given in Table 2. The generation mean analysis suggested the importance of both main effects (dominance and additive effects) and non-allelic interaction for most of the studied traits. However, for almost all characters the dominance effects were higher in magnitude as compared to the additive gene effects under different water regimes. The opposite signs of [h] and [l] revealed duplicate epistasis for all traits in the normal and water deficit conditions except RWC in the severe stress condition. Negative and positive signs of the [h] parameter showed that partial dominance was towards the decreasing F0, Fm, FV andRWC and increasing of FV/Fm and FV/F0 in the cross under consideration, respectively.


F0: The best-fit model for this trait was the six- parameter model [m-d-h-i-j-l]. Additive × additive



Table 1. Scaling tests (±SE) for physiological traits in maize generations at different water regimes.


Water Regimes






(μmol. m2s-1)

Full irrigation

-41.85 ± 5.84 **

-18.1 ± 7.66 **

-10.55 ± 14.80 ns

8.55 ± 14.23 ns

55% AWD

-32.45 ± 10.20 **

-11.55 ± 9.81 ns

-30.20 ± 21.23 ns

40.50 ± 16.76 **

75% AWD

-12.50 ± 6.06 *

-17.25 ± 7.09 **

12.95 ± 14.76 ns

28.65 ± 14.32 **


(μmol. m2s-1)

Full irrigation

-35.35 ± 30.81 ns

-25.35 ± 32.91 ns

40.60 ± 55.44 ns

109.80 ± 49.79 **

55% AWD

-61.30 ± 25.35 **

-64.85 ± 25.51 **

13.05 ± 44.61ns

77.05 ± 42.42 ns

75% AWD

-50.70 ± 29.04 ns

-72.60 ± 34.95 **

-144.30 ± 56.86 **

-8.90 ± 64.72 ns


(μmol. m2s-1)

Full irrigation

5.40 ± 31.60 ns

-7.30 ± 29.20 ns

51.80 ± 60.19 ns

95 ± 54.48 ns

55% AWD

-28.85 ± 28.63 ns

-53.30 ± 27.57 ns

43.25 ± 45.70 ns

36.55 ± 42.47 ns

75% AWD

-40.10 ± 31.75 ns

-66.35 ± 35.99 ns

-119.65 ± 54.81 **

-29.25 ± 69.69 ns

Fv ̸ Fm

Full irrigation

0.05 ± 0.02 **

0.02 ± 0.02 ns

0.03 ± 0.04 ns

0.02 ± 0.04 ns

55% AWD

0.02 ± 0.02 ns

-0.01 ± 0.02 ns

0.05 ± 0.03 ns

-0.03 ± 0.02 ns

75% AWD

-0.007 ± 0.02ns

-0.02 ± 0.02 ns

-0.02 ± 0.03 ns

-0.04 ± 0.04 ns

Fv ̸ F0

Full irrigation

0.71 ± 0.21 **

0.21 ± 0.22 ns

0.46 ± 0.42 ns

0.40 ± 0.35 ns

55% AWD

0.35 ± 0.27 ns

-0.12 ± 0.23 ns

0.70 ± 0.46 ns

-0.42 ± 0.35 ns

75% AWD

-0.05 ± 0.18 ns

-0.07 ± 0.19 ns

-0.86 ± 0.36 **

-0.47 ± 0.39 ns


Full irrigation

-24.22 ± 3.65 **

-9.30 ± 4.26 **

-38.42 ± 7.59 **

-6.98 ± 7.74 ns

RWC (%)

55% AWD

-20.11 ± 2.72 **

-15.88 ± 2.64 **

-43.66 ± 4.73 **

-2.07 ± 5.62 ns


75% AWD

-10.32 ± 2.50 **

-3.75 ± 3.30 ns

-26.83 ± 5.39 **

-15.84 ± 6.04 **

ns, * and **: non-significant and significant at 0.05 and 0.01 probability levels, respectively; AWD: available water depletion; F0, Fm, Fv, Fv ̸ Fm and Fv ̸ F0: minimum fluorescence, maximum fluorescence value, variable fluorescence, the maximum quantum yield of photosystem II (PSII) and maximum primary yield of the photochemistry of PSII, respectively; RWC: relative water content.





and dominance × dominance interactions had a significant role in the genetic control of F0in all three water regimes. Asadi et al. (2015) reported a high additive gene effect for F0 in wheat under normal conditions. Contrary, the non-additive gene effect was reported in sugarcane for F0by Zhang et al. (2010).


Fm: In the normal and mild stress conditions the six-parameter model [m-d-h-i-j-l] and in the severe stress conditions the five-parameter model [m-d-h-j-l] were the best-fit models for Fm. Dominance and dominance × dominance effects were significant and governed the genetics of this character. Therefore, exploiting the non-additive gene action can be effective for improving Fm. The same findings were documented by Hola et al. (2010) in five inbred lines of maize. The authors revealed that epistatic and dominance gene effects were important in controlling the chlorophyll fluorescence parameters.  


FV: The findings revealed that the three-parameter model [m-d-h] was fitted well for Fv under normal and mild stress conditions. Both additive and dominance gene effects were significant for this trait under the two aforementioned conditions. Whereas, in the severe stress condition the five-parameter model [m-d-h-j-l] was determined as the best model for this character. The dominance and dominance × dominance effects were also significant and had an important role in controlling Fv.


FV/Fm: The five-parameter model [m-d-h-j-l] was best fitted for this trait in the normal conditions. The dominance    gene   effect   and   dominance × dominance    interaction    were    significant    and




Water Regimes










(μmol. m2s-1)

Full irrigation

208.6 ± 5.85**

-2.54 ± 2.18ns

-101.7 ± 17.27ns

-23.83 ± 5.94**

-25.67 ± 8.54ns

78.79 ± 13.38**



55% AWD

224.2 ± 7.08**

4.44 ± 2.51ns

-99.4 ± 23.62**

-25.82 ± 7.22**

-21.05 ± 12.91ns

72.35 ± 18.99**



75% AWD

244.8 ± 5.82**

8.39 ± 2.20**

-85.8 ± 16.89**

-27.12 ± 5.91**

4.06 ± 8.30ns

54.04 ± 12.99**




(μmol. m2s-1)

Full irrigation

738.1 ± 22.31**

13.87 ± 8.60ns

-212.1 ± 74.47**

-78.19 ± 22.86**

-10.57 ± 42.48ns

131.6 ± 58.06**



55% AWD

788.9 ± 18.94**

 22.53 ± 6.85**

-272.3 ± 61.39**

-80.51 ± 19.33**

3.22 ± 34.12ns

188.3 ± 46.91**



75% AWD

727.0 ± 9.54**

-2.65 ± 9.81ns

-120.1 ± 43.11**


23.22 ± 42.11ns

129.0 ± 41.29**




 (μmol. m2s-1)

Full irrigation

484.2 ± 7.38**

17.57 ± 8.21**

-9.10 ± 13.38ns






55% AWD

513.5 ± 5.85**

20.22 ± 6.54**

-9.62 ± 11.55**






75% AWD

513.2 ± 10.20**

-9.52 ± 10.48ns

-101.1 ± 44.79**


27.34 ± 45.08ns

112.7 ± 42.36**



Fv ̸ Fm

Full irrigation

0.72 ± 0.006**

0.01 ± 0.007ns

0.06 ± 0.03*


0.03 ± 0.02ns

-0.06 ± 0.03**



55% AWD

0.72 ± 0.003**

0.004 ± 0.004ns

0.003 ± 0.008ns






75% AWD

0.70 ± 0.005**

-0.009 ± 0.005ns

0.008 ± 0.008ns






Fv ̸ F0

Full irrigation

2.58 ± 0.06**

0.12 ±0.06*

0.76 ± 0.28**


0.50 ± 0.29ns

-0.78 ± 0.27**



55% AWD

2.60 ± 0.05**

0.05 ± 0.05ns

0.05 ± 0.10ns






75% AWD

2.03 ± 0.10**

-0.14 ± 0.06**

0.44 ± 0.13**

0.33 ± 0.11**

0.03 ± 0.02ns




RWC (%)

Full irrigation

71.3 ± 3.34**

1.10 ± 1.15ns

-27.02 ± 10.20**

0.73 ± 3.39ns

-14.72 ± 5.35**

33.92 ± 7.52**



55% AWD

64.9 ± 2.44**

-3.57 ± 0.75**

-37.09 ± 7.19**

-1.93 ± 2.46ns

-4.40 ± 3.52ns

40.88 ± 5.33**



75% AWD

41.2 ± 2.61**

-4.27 ± 0.85**

2.88 ± 7.75ns

8.73 ± 2.64**

-6.15 ± 3.86ns

6.49 ± 5.70ns




Table 2. Estimates of genetic parameters (±SE),  for the joint scaling test for physiological traits in maize at


different water regimes using generation mean analysis.

See Table 1 for abbreviations.



involved in governing the genetics of FV/Fm. Under both water stress conditions, the three-parameter model [m-d-h] was fitted for this character. Similar findings were reported by Vijayalakshmi et al. (2010) in winter wheat which showed a low additive gene effect for FV/Fm. The quantum efficiency of PS II (Fv/Fm) has a major role in photosynthesis and it can be used as a screening tool to identify tolerant varieties to drought (Sharma et al. 2014). A low ratio of Fv/Fm shows low photosynthetic efficiency, thus, genotypes that have a higher ratio of Fv/Fm may be more tolerant to drought stress (Ristic et al. 2007; Kumar et al. 2012).


FV/F0: In the normal and severe stress conditions the five-parameter models [m-d-h-j-l] and [m-d-h-i-j] were regarded as the best models explaining the inheritance of this character, respectively. Thus, both main gene effects and epistatic interactions governed the control of this trait under these conditions. However, under the mild stress conditions, only the three-parameter model was best fitted for this trait.


RWC: Under normal and water deficit conditions the six-parameter model [m-d-h-i-j-l] was the best fitted this character. Both main effects were significant but the dominance effect was higher than the additive effect in the normal and mild stress conditions. Generation mean analysis in maize (Moharramnejad et al. 2018), as well as durum wheat (Salmi et al. 2019), under control and drought stress conditions showed the importance of dominance genes effects in operating the inheritance of RWC. In contrast to our results, the additive gene effect for RWC in wheat was reported by Said (2014) under the normal condition and Asadi et al. (2015) under both normal and water deficit conditions. It seems that the genetic effects vary according to crop species, varieties and the environmental conditions in which the research was conducted.



In this experiment, the importance of dominance and epistasis (duplicate) effect was recognized for most of the studied physiological traits under water stress conditions, which implies exploiting non-additive gene action and delaying the selection to advanced generations for these traits in maize aiming at developing drought-tolerant lines and hybrids in maize. However, the additive effect was more important in the inheritance of F0 and FV/Fm in the normal condition and early selection can be efficient for the improvement of these characters.


Conflict of Interest

The authors declare that they have no conflict of interest with any organization concerning the subject of the manuscript.

Asadi AA, Valizadeh M, Mohammadi SA and Khodarahmi M, 2015. Genetic analysis of some physiological traits in wheat by generations mean analysis under normal and water deficit conditions. Biological Forum 7(2): 722-733.
Batra NG, Sharma V and Kumari N, 2014. Drought-induced changes in chlorophyll fluorescence, photosynthetic pigments, and thylakoid membrane proteins of Vigna radiata. Journal of Plant Interactions 9(1): 712-721.
Cavalli L, 1952. An analysis of linkage in quantitative inheritance. In: Reive ECR and Waddington CH (eds). Quantitative Inheritance. Pp. 135-144. HMSO, London.
Cramer GR, Urano K, Delrot S, Pezzotti M and Shinozaki K, 2011. Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biology 11: 1-14.
Dhanda S and Sethi G, 2002. Tolerance to drought stress among selected Indian wheat cultivars. The Journal of Agricultural Science 139(3): 319-326.
Gindaba J, Rozanov A and Negash L, 2004. Response of seedlings of two Eucalyptus and three deciduous tree species from Ethiopia to severe water stress. Forest Ecology 201(1): 119-129.
Hao D, Chao M, Yin Z and Yu D, 2012. Genome-wide association analysis detecting significant single nucleotide polymorphisms for chlorophyll and chlorophyll fluorescence parameters in soybean (Glycine max) landraces. Euphytica 186(3): 919-931.
Hola D, Benesova M, Honnerova J, Hnilicka F, Rothova O, Kocova M and Hnilickova H, 2010. The evaluation of photosynthetic parameters in maize inbred lines subjected to water deficiency: can these parameters be used for the prediction of performance of hybrid progeny? Photosynthetica 48(4): 545-558.
Kalaji HM, Goltsev V, Bosa K, Allakhverdiev SI and Strasser RJ, 2012. Experimental in vivo measurements of light emission in plants: a perspective dedicated to David Walker. Photosynthesis Research 114(2): 69-96.
Kumar S, Sehgal SK, Kumar U, Prasad PV, Joshi AK and Gill BS, 2012. Genomic characterization of drought tolerance-related traits in spring wheat. Euphytica 186(1): 265-276.
Lichtenthaler H, Buschmann C and Knapp M, 2005. How to correctly determine the different chlorophyll fluorescence parameters and the chlorophyll fluorescence decrease ratio RFd of leaves with the PAM fluorometer. Photosynthetica 43(3): 379-393.
Mather K and Jinks JL, 1982. Biometrical Genetics: the Study of Continuous Variation. Third edition. Chapman and Hall, USA, 396 pp.
Moharramnejad S, Valizadeh M and Emaratpardaz J, 2018. Generation mean analysis in maize (Zea mays L.) under drought stress. Fresenius Environmental Bulletin 27(4): 2518-2522.
Ristic Z, Bukovnik U and Prasad PV, 2007. Correlation between heat stability of thylakoid membranes and loss of chlorophyll in winter wheat under heat stress. Crop Science 47(5): 2067-2073.
Said AA, 2014. Generation mean analysis in wheat (Triticum aestivum L.) under drought stress conditions. Annals of Agricultural Sciences 59(2): 177-184.
Salisbury F and Ross C, 1998. Plant Physiology. Fourth edition. Wads Worth Publishing Company, Belmont, California, USA.
Salmi M, Benmahammed A, Benderradji L, Fellahi ZEA, Bouzerzour H, Oulmi A and Benbelkacem A, 2019. Generation means analysis of physiological and agronomical targeted traits in durum wheat (Triticum durum Desf.) cross. Revista Facultad Nacional de Agronomía Medellín 72(3): 8971-8981.
Sharma DK, Fernandez JO, Rosenqvist E, Ottosen CO and Andersen SB, 2014. Genotypic response of detached leaves versus intact plants for chlorophyll fluorescence parameters under high temperature stress in wheat. Journal of Plant Physiology 171(8): 576-586.
Shiferaw B, Prasanna BM, Hellin J and Banziger M, 2011. Crops that feed the world. 6. Past successes and future challenges to the role played by maize in global food security. Food Security 3(3): 307-327.
Simic D, Lepedus H, Jurkovic V, Antunovic J and Cesar V, 2014. Quantitative genetic analysis of chlorophyll a fluorescence parameters in maize in the field environments. Journal of Integrative Plant Biology 56(7): 695-708.
Stirbet A, Lazar D, Kromdijk J and Govindjee G, 2018. Chlorophyll a fluorescence induction: can just a one-second measurement be used to quantify abiotic stress responses? Photosynthetica 56: 86-104.
Todaka D, Shinozaki K and Yamaguchi-Shinozaki K, 2015. Recent advances in the dissection of drought-stress regulatory networks and strategies for development of drought-tolerant transgenic rice plants. Frontiers in Plant Science 6: 84. doi: 10.3389/fpls.2015.00084. 
Viana JMS, 2000. Generation mean analysis in relation to polygenic systems with epistasis and fixed genes. Pesquisa Agropecuaria Brasileira 35(6): 1159-1167.
Vijayalakshmi K, Fritz AK, Paulsen GM, Bai G, Pandravada S and Gill BS, 2010. Modeling and mapping QTL for senescence-related traits in winter wheat under high temperature. Molecular Breeding 26(2): 163-175.
Zhang ZB, Xu P, Jia JZ and Zhou RH, 2010. Quantitative trait loci for leaf chlorophyll fluorescence traits in wheat. Australian Journal of Crop Science 4(8): 571-579.