Advances in Applied Agricultural Sciences 2 (2014); 09: 49-63
Genetic analysis of adult plant resistance to stem rust (Puccinia graminis f. sp. tritici) and yield in wheat (Triticum aestivum L.)
Duncan Cheruiyot 1*, Pascal P. Okwiri Ojwang 1, Peter N. Njau 2, Peter F. Arama 3, Godwin K. Macharia 2
1 Egerton University, Egerton, Kenya. 2 Kenya Agricultural and Livestock Research Organization (KALRO), Njoro, Kenya. 3 Rongo University College, Rongo, Kenya.
Stem rust (Puccinia graminis f. sp. tritici) disease is a major challenge to wheat (Triticum sp.) production in Africa and other wheat growing countries of the world. Genetic resistance is a viable option to minimize yield losses due to the disease. The objectives of the study were to (i) estimate the kind of gene action involved in the inheritance of adult plant resistance to stem rust and yield related traits in wheat and (ii) to determine heritability of these traits. Six genotypes, four with known reaction to stem rust and two genotypes adapted to Kenyan growing environments were crossed in complete 6×6 diallel fashion. Results revealed that both general combining ability (GCA) and specific combining ability (SCA) effects were significant (P < 0.01) for all traits studied. Nonetheless, GCA effects were predominant for all the traits. This establishes predominance of additive genetic effects (fixable variation) over non-additive effects. Narrow sense heritability estimates were moderate (0.33 for grain yield) to high (0.78 for days to heading). Additionally, the Wr/Vr graph revealed partial dominance for stem rust infection, the number of days to heading and the number of productive tillers while over-dominance was observed for grain yield and plant height. Since all the traits were heritable, recurrent selection will be effective. In addition, inclusion of parents KSL 13 and KSL 42 as well as crosses KSL 34/KSL 52, NjBw II/KSL 42, Kwale/KSL 13, KSL 34/KSL 42 in a breeding program would produce desired segregants. These could therefore be exploited successfully in enhancement of stem rust disease resistance as well as yield in areas prone to stem rust infection.
Combining ability, General predictability ratio, Griffing’s analysis, Hayman’s analysis
Stem rust (Pucciniagraminis f. sp. tritici) is a major fungal diseases of bread wheat (Triticum aestivum L.) that can cause significant yield and economic yield losses in Kenya and other wheat growing countries of Africa (Wanyera et al. 2006; Njau et al.2009) and Asia (Singh et al. 2008; Singh et al.2011). Common bread wheat production in Kenya was estimated at 0.25 million tons in 2012 against the demand of 0.9 million tons in the same year. This deficit in wheat production is due to the stem rust disease among other biotic and abiotic challenges. Stem rust can cause up to 100% yield loss in case of an epidemic, or when a susceptible cultivar is grown (Park 2007).
Several options including chemical, cultural etc. are advocated for managing rust diseases, genetic resistance still remains the most viable and sustainable option. Attempts have been made to develop resistant cultivars. This had been achieved for over 30 years up to 90’s through characterization and utilization of more than 60 stem rust (Sr) resistant genes, of which all except Sr2 were major genes which are dominant in action (McIntosh et al.1995; Singh et al. 2011). But the rust pathogen is always mutating and is known to cause susceptibility in previously resistant wheat varieties (Luig 1983; Park 2007). Considering the constant mutation of the pathogen and occurrence of new variants especially in stem rust ‘hot spots’, there is a need for durable and broad-spectrum disease resistance conditioned by minor genes having additive effects. Sr2 is an adult plant resistant (APR) gene known to confer slow rust resistance or durable resistance (Sunderwirth and Roelfs 1980). In combination with other unknown minor or some of the already characterized major genes, Sr2 can confer adequate and durable levels of resistance (Knott 1988). Existence of unidentified stem rust APR minor genes has been reported by Knott (2001). In other studies, a number of quantitative trait loci (QTL) conferring APR to stem rust have been identified (Bhavani et al. 2011; Njau et al. 2013). This shows some evidence of quantitative and complex inheritance of APR to stem rust. The usefulness of carrying out genetic studies to explore the mechanism of inheritance of APR to stem rust is indicated by the fact that breeders must keep ahead of the pathogen by fast tracking breeding.
Researchers at the International center for maize and wheat improvement (CIMMYT) continue to develop and select for wheat germplasm that have resistance to a broad spectrum of abiotic and biotic challenges and good yield (Singh et al.2011). The yield potential as well as APR to stem rust in the CIMMYT materials and Kenyan old varieties can be utilized in wheat improvement programs aimed to increase wheat yield (Njau et al.2010; Nzuve et al.2012). However, for fast track success, information on genetics of inheritance of the targeted traits, with emphasis on the type of gene action involved (whether additive or non-additive) and heritability are critical.
The diallel cross technique according to Griffing’s (1956) and Hayman (1954) have been extensively used to assess the F1 crosses in self-pollinated crops. These methods have previously been used by many workers. Sangwan and Chaudhary (1999) reported equal contribution of additive and non-additive gene effects in inheritance of tillers per plant and grains per ear in wheat. Additive gene action and non-additive have been reported for days to maturity and plant height in wheat, respectively (Akram et al.2008). Muhammad et al.(2000) observed non-additive genetic effects for grain yield. In addition to the yield and yield related traits, the diallel cross technique has been used to study the genetics of resistance to rust diseases of wheat. Additive with partial dominance has been observed in inheritance of resistance to leaf rust (Puccinia triticina) incidence (Hussain 2005) and the major importance of additive effects with non-additive (dominance and epistasis) gene effects to lesser extent has been reported for yellow rust (Puccinia striiformis) (Wagoire et al. 1998). But these reports have proved inconsistent and the results obtained are restricted to the samples used and may not necessarily apply to other samples. Notably, limited information exists on the studies on inheritance of stem rust. Therefore, independent genetic studies are essential for any targeted population. The objective of this study therefore was to determine (i) the kind of gene action associated with adult plant resistance and yield related traits and (ii) heritability of the traits, in a diallel analysis of wheat genotypes adapted to key wheat growing environments in Kenya.
Materials and Methods
The experimental site was Kenya Agricultural Research Institute (KARI), Njoro situated at 0o20′S, 35o56′E at an elevation of 2185 m a.s.l., has an average annual rainfall of 939 mm (average of 15 years), and average annual minimum and maximum temperatures of 9°C and 24°C (average of 15 years) respectively. This site is one of the widely considered ‘hot-spots’ for stem rust disease and heavy natural epidemics of the pathogen are observed in most seasons.
Parental genotypes and field procedure
Four advanced wheat breeding lines namely; KSL 13, KSL 34, KSL 42 and KSL 51 and two locally adapted but stem rust susceptible cultivars NjBw II and Kwale were crossed using diallel mating design (Table 1). The selection of four advanced lines was based on high levels of resistance to stem rust and desirable agronomic traits based on the results of an earlier field screening trial. The two locally adapted cultivars were susceptible to stem rust (Njau et al. 2009). Crosses were made following a 6×6 complete diallel model. Subsequently, 30 F1s and six parents were planted in an alpha lattice design (12 rows and 3 columns) with three replications in the year 2013 cropping season. Each entry was planted in two rows of 1.5 m length; the plant to plant and row to row spacing was 0.1 m and 0.2 m, respectively. Incomplete blocks (columns) and replicates were separated by a space of 0.5 m. The highly susceptible wheat cultivar, Cacuke was planted around the experimental plot and in the middle of the 0.5 m alleyway on both sides of the entries to enhance inoculum build up, and serve as spreader. At planting, Nitrogen and Phosphorus were applied at the rate of 22.5 kg N ha-1 and 25.3 kg P ha-1, respectively. Buctril MC (225 g L-1 Bromoxynil octanoate and 225 g L-1 MCPA Ethylhexylester), a post emergence herbicide was sprayed at tillering stage at the rate of 7 ml L-1 of water to control broad-leafed weeds. To control of insect pest, Buldock Duo (225 g L-1 Beta-Cyfluthrin) was sprayed at the rate of 10 ml L-1 of water. Manual weeding by hand was done two times between stem elongation and booting stages to eradicate grasses.
Days to heading for each entry was recorded as the number of days from planting to 50% of plants booting i.e. Zadok’s stage 57 (three fourths of ear emerged) (Zadok et al.1974). Assessment of plants for APR to stem rust was done on dough stage (Zadoks 83 of grain development) when the spreader reached 50% severity. The adult plant response to infection was classified according to Roelfs et al. (1992) into four categories; R=resistant, MR=moderately resistant, MS=moderately susceptible, S=susceptible. Overlapping responses between two categories was denoted using a dash between the two categories. The stem rust severities ranging from 5 to 100% were determined by use of the modified Cobb scale (Peterson et al.,1948). Coefficient of infection (CI) for all genotypes were calculated by taking into account the disease severity and their infection response where; 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0 represented immune, resistant (R), moderately resistant (MR), moderately resistant to moderately susceptible (M), moderately susceptible (MS) and susceptible (S), respectively (Roelfs et al.1992). The average plant height (cm) from the base of the plant to the base of the spike and average number of productive tillers were measured in 10 randomly selected plants. The average grain weights of all the plants per entry were extrapolated to obtain the grain yield per plot and standardized at 12% grain moisture content.
Statistical and Genetic Analyses
Data collected on 30 hybrids and 6 parents were subjected to analysis of variance (ANOVA) using the Statistical Analysis Software (SAS) version 9.1 and procedure PROC GLM (SAS Institute Inc., Cary 2002) by implementing the statistical model;
Yijk=µ +Gi +Rj+ B(jk)+εijk
Where; Yijk= observed phenotype of the ith genotype, in the kth incomplete blok of jth replicate; µ= mean of the experiment; Gi= effect of the ith genotype; Rj =effect of the jth replicate; B(jk)= effect of the kthincomplete block within the jth replicate and εijk = experimental error.
Combining Ability Analyses
The “Diallel-SAS05” routine of the SAS programme was used to perform Griffing’s method 1 model 1 analysis (Zhang et al., 2005): Yij =μ +gi + gj +sij + rij +εij
Where; µ = general mean, giand gj= general combining ability effects of the ith and jth parent, respectively, sij = specific combining ability effects of the cross (ixj), rij=reciprocal effect, εij=experimental error. General predictability ratio, GPR was calculated as 2MSGCA/2MSGCA+ MSSCA as suggested by (Baker 1987)and narrow sense heritability h2 as VA/VA+ VD + VE, where VA= additive genetic component of variance, VD = non-additive genetic component of variance and VE = error variance, was calculated as suggested by Kearsey and Pooni (1996).
Hayman’s (1954) approach was used to partition the components of variation for the traits that showed significant variation into: a (additive); b (the dominance effect); [dominance effects b is further sub-divided into b1 (directional dominance), b2 (asymmetrical distribution of dominance) and b3 (peculiarity of dominance to some crosses)]; c (maternal); and d (non-maternal). This was performed by the GenStat 14thedition statistical software (VSN international Ltd, 2010) using the following additive dominance model;
Y=µ + block +a + b + c + d+(a×block)+(b×block)+(c×block)+(d×block)
Where; Y – observed effect; µ – grand mean; block – block effects; a – additive effects; b – dominance effects; c – additive maternal effects; d – maternal interaction effects. The a×block+b×block+c×block+d×block is the interaction of the blocks with the model components. The F-test was performed to test for the significance as the ratio of mean square of an item in the model and the block*effect interaction mean square. Test for homogeneity of the interactions were done according to Bartlett test (Steel and Torrie, 1980). However, for testing of b1, b2 and b3, the homogeneity of their interaction with blocks was first tested and since they were homogeneous, all the three effects were tested against blockb. For a, b, c, and d, their interactions with the blocks were also tested for homogeneity. These were pooled together as they were homogeneous and the pooled error was used to test each of the items.
The graphical approach (Hayman 1954) was used to test: i) the adequacy of the dominance-additive model, ii) the degree of dominance, i.e. whether partial, complete or over-dominance and, iii) distribution of dominance and recessive genes. For testing the adequacy of the model, a scaling test known as regression coefficient analysis was used according to Hayman (1954). Regression coefficient was generated from a plot of the covariance (Wr) of family means with non-recurrent parents against variance (Vr) of the family means within an array. Departure of the regression coefficient (b) from zero was tested using (b-0)/s.ebwhile departure of b from unity was tested using (1-b)/s.eb, where s.e is the standard error. Significant deviation of regression coefficients from Zero but not from unity, i.e. uniformity of Wr, Vr indicated the validity of assumptions for diallel analysis made by Hayman (1954).
Studies on evaluation of genotypic variation among the wheat genotypes under natural infection of stem rust disease revealed significant genotypic differences at P < 0.05 for all the studied traits indicating genetic variability (Table 2).
Combining Ability Effects
Results of Griffing’s method 1 model 1 analyses showed significant general combining ability (GCA) (P < 0.001) and specific combining ability (SCA) (P < 0.05) effects for all the studied traits (Table 3). However, GCA effects were greater than SCA effects across all traits. General predictability ratio (GPR) ranged from 0.866 (for grain yield) to 0.977 (for the number of days to heading) which suggested that the traits were predominantly controlled by GCA effects.
Based on predominance of GCA effects of parents, performance of a single-cross derived progeny will be highly predictable. There was no significance in maternal, non-maternal and reciprocal effects. Narrow sense heritability estimates were moderate (0.33 for grain yield) to high (0.78 for days to heading). General combining ability effects of the parents for the studied traits are presented in Table 4.
Early maturing wheat varieties are desirable because the risks of grain loss due to factors such as disease and water stress can be reduced. Therefore, negative GCA effects for days to heading are desirable. KSL 13 and KSL 34 had negative and significant (P < 0.001) GCA effects for this trait. This indicates the usefulness of the two parents in breeding for early maturing wheat. For number of tillers the parents NjBw II and Kwale had positive and significant (P < 0.001) GCA effects which indicate that these parents can increase the number of tillers. The desired GCA effects for plant height should be negative; GCA effects for parents KSL 13 and KSL 51 were negative and significant (P < 0.001 and P < 0.5, respectively). The parents KSL 13 and KSL 42 had positive and significant (P < 0.001) GCA effects for grain yield. The desired GCA effects for disease resistance should be negative; parents KSL 42 and KSL 13 were important in contributing negative significant (P < 0.05) GCA effects for disease resistance.
Table 1. Description of bread wheat (Triticum aestivum L.) genotypes used in a 6×6 diallel cross
KSL, Kenyan Selection; CIMMYT, Center for Maize and Wheat Improvement; NjBw II, Njoro bread wheat II
Table 2. Mean squares derived from analysis of variance for stem rust disease resistance and yield components of wheat genotypes
Days to heading
No. of tillers
Grain yield/plot (kg)
Block (in rep)
* represent significance at P < 0.05; df, degrees of freedom; CI, Coefficient of infection
Table 3. Analysis of variance of general combining ability (GCA), specific combining ability (SCA) maternal (MAT), non-maternal (NMAT) effects, reciprocal and general predictability ratio for evaluated traits.
Source of variation
No. of tillers
Plant height (cm)
Grain yield/plot (Kg)
* represent significance at P < 0.05; ** represent significance at P < 0.01; *** represent significance at P < 0.001; d. f, degrees of freedom; h2, narrow-sense heritability; CI, Coefficient of stem rust infection.
The SCA effects of wheat crosses are presented in Table 5. Crosses Kwale/ KSL 13 and Kwale/KSL 51 displayed negative and significant (P < 0.001) SCA for number of days to heading. Notably, these crosses are derived from parents with negative GCA effects for the trait.
The SCA effects of wheat crosses are presented in Table 5. Crosses Kwale/ KSL 13 and Kwale/KSL 51 displayed negative and significant (P < 0.001) SCA for number of days to heading. Notably, these crosses are derived from parents with negative GCA effects for the trait. The cross KSL 34/KSL 42 displayed positive and significant (P < 0.05) SCA effects for number of tillers per plant. For plant height cross KSL 34/KSL 51 had significant (P < 0.01) SCA effect; and both parents in this cross showed negative GCA effects. There was positive and significant SCA (P < 0.05) effect in cross NjBw II/ KSL 42 for grain yield. The same cross also displayed negative and significant SCA effect for disease CI. This implies that apart from yield, this cross also had high level of resistance to stem rust disease.
Fig. 1. Linear regression of Wr/Vr for days to heading
Fig. 2. Linear regression of Wr/Vr for number of tillers
Mean squares for the disease and yield components of evaluated wheat genotypes are presented in Table 6. The additive component of variation a was highly significant for days to heading, number of tillers, grain yield per plot and stem rust CI (P < 0.001) and plant height (P < 0.05). These results agree with combining ability analysis. For plant height, dominance was more significant (P < 0.01), but based on the magnitude of the mean squares, additive component was more important.
The dominance component of variation was significant for all the traits. There was no significance in maternal c and non-maternal d, suggesting there was no need to retest a and b. Further partitioning of dominance b, direction of dominance b1 was significant for number of tillers at P < 0.001 and grain yield at P < 0.01. Asymmetry in gene distribution b2 was significant (P < 0.05) for days to heading. Lastly, residual dominance effects (b3) were also significant (P < 0.01) for plant height suggesting that some dominance for this trait were peculiar to some F1 crosses. Regressions of covariance (Wr) on variance (Vr) for the traits are presented in Figs. 1 to 5. For the number of days to heading (Fig. 1), the Wr/Vr regression was highly significant (P < 0.001) from zero, with a regression coefficient not significantly different from unity (Fig. 1). This satisfies the assumption of the absence of non-allelic (epistatic) interaction.
The point where the regression line cuts the Wr-axis provides a measure of average degree of dominance. For days to heading, regression line intercepted the Wr axis above the Wr origin (Fig. 1), this revealed partial dominance of genes. Parent KSL 13 contributed most of the dominant genes towards days to heading, since it was close to the origin of the slope. Kwale contributed most recessive genes since it was furthest on the regression slope. Parent KSL 34 contributed both dominant and recessive genes since it was equidistant to the line.
Fig. 3.Linear regression of Wr/Vr for plant height
Fig. 4. Linear regression of Wr/Vr grain yield per plot
For inheritance of number of tillers per plant (Fig. 2), regression coefficient was significant from zero, but not from unity. Regression line intersected Wr-axis above the origin also depicting partial dominance. All the parents except Kwale were clustered near the origin of the slope suggesting important contribution of dominant alleles. Kwale which was at the furthest end of regression line contributed the recessive genes.
The Wr/Vr regression was significant from zero but not from unity for plant height (Fig. 3). The line touched Wr-axis below the point of origin indicating over-dominance of genes. Distribution of array points along regression line showed that NjBw II contributed most of the dominant alleles since it was the closest to the origin, while KSL 13 which was furthest from the origin contributed most of the recessive alleles. Parents KSL 42, Kwale and KSL51 were situated at the middle of the regression line; this indicated contribution of both dominant and recessive alleles by the parents.
For grain yield, Wr/Vr regression was significant (P < 0.05) from zero but not from unity (Fig. 4). Over-dominant type of gene action was revealed as intercept point on Wr-axis was negative. NjBw II contributed most of the recessive genes as it was the furthest from the origin while KSL 42 seemed to be the closest to the origin implying that it contributed most of the dominant genes.
Lastly, Wr/Vr regression line for CI was highly significant (P < 0.001) from zero but not from unity. The line touched Wr-axis above the origin indicating the partial dominance. Parent NjBw II was the farthest from the origin suggesting that this parent contributed most of the recessive alleles for disease susceptibility, while KSL 42 contributed most of the dominant alleles for disease resistance since it was the closest to the origin (Fig. 5).
Fig. 5. Linear regression of Wr/Vr for disease Coefficient of infection
The means of array variance and covariance for studied yield components are shown in Table 7. According to Hayman (1954), very low Wr-Vr values and high Wr+Vr values also indicate the major control of recessive genes. Therefore from our study, recessive genes controlled inheritance of the days to heading and number of tillers in Kwale, plant height in KSL 13, and grain yield and disease in NjBw II. It can be deduced that for all traits studied, there was superiority of parental means over array means. However, a few instances of higher array means than parental means were also observed; this confirmed that dominance also played a role in inheritance of the traits.
Disease coefficient of infection (CI) is one of the parameters that have been used to evaluate the level of host reaction to the stem rust disease of wheat. This parameter is commonly used for the measurement and quantification of genetic variation for resistance to the disease. Results of the study showed significance in GCA and SCA effects for stem rust CI and yield related traits evaluated under the disease infection. The GCAeffects had relatively higher magnitudes as compared to the SCA effects. According to Griffing (1956), high GCA effects are related to additive or additive x additive interaction effects, the components that respond to selection. Baker ratio (Baker, 1978) further confirms the importance of additive gene effects. Therefore, with regard to the GCA effects and the Baker ratios, additive gene effects controlled inheritance of all the traits under stem rust disease pressure. Similar findings of predominance of GCA effects in wheat, although in absence of disease pressure have been reported previously for; days to heading (Zare-kohanand Heidari 2012) number of tillers per plant (Chowdhry et al.1992) and grain yield (Hassan et al. 2007). However, some contrasting reports on days to heading (Iqbal 2007), number of productive tillers and grain yield (Shabbir et al. 2011) did not conform to our findings. The dominance of GCA effects for plant height conformed to previous reports of Zare-kohan and Heidari (2012) but not to that of Shahzad and Chowdhry (1998) which reported domination of SCA effects. The results of our analyses highlighted the primary importance of additive properties of gene effects in inheritance of all the traits studied. This was further confirmed by moderate to high narrow sense heritability estimates (h2) which highlights the proportion of variation which is due to the additive genetic effects.
Both Griffing’s and Hayman’s analyses indicated that even though inheritance stem rust disease resistance and yield related traits under stem rust infection was mainly governed by additive type of gene action. Dominant genetic effects also played a role but to a smaller extent. From the Wr/Vr analyses, the genetic model proved to be adequate for the data set since the regression coefficients deviated significantly from zero but not from unity for all the traits. This indicated the absence of non-allelic (epistatic) interaction. According to Ponziak et al. (2012) absence of epistatic interaction makes it easy in case of practical application of marker-assisted selection (MAS) for quantitative traits.
Early maturing wheat crop has advantages in the sense that the risks of crop losses due to vagaries such as diseases, water stress etc. is minimized. It is therefore an important trait that can be invested on by the breeders. In addition to the additive gene action in inheritance of days to heading, the Wr/Vr graph also revealed partial dominance. Similar result in wheat genotypes has previously been obtained (Siddique et al. 2004). In order to developed wheat varieties with short time to heading, the parents used in crosses should have negative and significant GCA effects. From the current study, KSL 13 and KSL 34 proved to be the best parents for reduction of the number of days to heading due to their negative and significant (P < 0.001) GCA effects.
According to Otteson et al.(2008), grain yield depends substantially on the number of productive tillers. Therefore, selection based on productive tillering is essential in enhancing wheat productivity. Inheritance of number of tillers per wheat plant was governed by partial dominance of gene action in addition to the major action of additive genetic effects; this conformed to the findings of Ullah et al.(2010). Positive GCA effects are desired for improvement of the number of productive tillers. NjBw II and Kwale had positive and significant (P < 0.001) GCA effects. These present as the best parents for improvement of the trait.
Plant height is also critical contributor of yield. In many wheat improvement programs, short wheat plants are desired as they are resistant to lodging and responsive to fertilizer. This precedence was set in green revolution following which incorporation of dwarfing genes in breeding population became a routine. Inheritance of this trait was mainly governed by additive genetic effects with over-dominance. Negative GCA effects are desired for plant height, which was displayed by parents KSL 13. Therefore, this parent is important in reducing the plant height. For grain yield (Fig. 4), in addition to the major role of additive gene action, over-dominant type of gene action was also revealed. This conformed to previous findings of Muhammad et al.(2000). Positive GCA effects are important for improvement of grain yield; therefore in the current study, parents KSL 42 and KSL 13 are recommend because of their positive and significant (P < 0.001) GCA effects. The parents that could provide superior segregants for early maturity are Kwale/ KSL 13 and Kwale/KSL 51, while KSL 34/KSL 42 can be utilized for number of tillers per plant.
Table 4. General combining ability effects of parents for the studied traits of wheat
Days to heading
No. of tillers
Plant height (cm)
Grain yield/plot (Kg)
* represent significance at P < 0.05; ** represent significance at P < 0.01; *** represent significance at P < 0.001; CI, Stem rust coefficient of infection.
Table 5. Specific combining abilities of F1 wheat crosses for traits evaluated under stem rust infection
Days to heading
No. of tillers
Grain yield/plot (Kg)
NjBw II/ Kwale
NjBw II/ KSL 13
NjBw II/ KSL 34
NjBw II/ KSL 42
NjBw II/ KSL 51
Kwale/ KSL 13
Kwale/ KSL 34
Kwale/ KSL 42
Kwale/ KSL 51
KSL 13/ KSL 34
KSL 13/ KSL 42
KSL 13/ KSL 51
KSL 34/ KSL 42
KSL 34/ KSL 51
KSL 42/ KSL 51
CI, coefficient of Infection; *, **, and *** represent significance at P < 0.05, P < 0.01 and P < 0.001, respectively.
Table 6. Mean squares for the stem rust disease infection and yield components of wheat genotypes in Hayman analysis
Days to heading
No. of Tillers
Block x a
Block x b
Block x b1
Block x b2
Block x b3
Block x c
Block x d
Block x Total
df degrees of freedom; b1, direction of dominance; b2, asymmetry of alleles; b3, residual dominance effects; CI, Coefficient of infection; *, ** and *** represent significance at P < 0.05, P < 0.01 and P < 0.001; ns, non-significance
Table 7. Means of array variance and covariance for the traits associated with resistance to stem rust
Days to heading
No. of tillers
Plant height (cm)
Grain yield (kg)
Stem rust coefficient
of infection (CI)
The cross NjBw II/ KSL 42 would produce desired segregants for grain yield and resistance to stem rust disease.
In conclusion, the present study provided useful information by way of indicating the nature of inheritance of stem rust disease resistance and yield related components under stem rust disease infection. The results showed that all the studied traits were mainly governed by additive type of gene action and displayed moderate to high values of heritability. This implies that the traits are fixable. The moderate heritability values for grain yield (0.33) and plant height (0.36) showed that non-additive gene action was also important in inheritance of these traits. Therefore, recurrent selection is advocated as it would result in accumulation of genes from different sources. It was further possible to classify the parents on the basis of the type of alleles present in them and this provides useful clues for the selection of parents which are likely to give better segregates. KSL 13 was the best general combiner for days to heading, plant height and grain yield and in addition a good combiner for resistance to stem rust. KSL 42 was also the best combiner for resistance to stem rust as well as a good combiner for grain yield. Therefore, inclusion of parents KSL 13 and KSL 42 as well as crosses NjBw II/ KSL 42, Kwale/ KSL 13, and KSL 34/KSL 42 in a breeding program would provide favorable alleles for enhancement of stem rust disease resistance as well as yield in areas prone to stem rust infection.
This research was supported by the Durable Rust Resistance in Wheat Project (DRRW) through Kenya Agricultural Research Institute (KARI). Genotypes identified as Kenya selection (KSL) were sourced from the International center for maize and wheat improvement (CIMMYT) nurseries being part of the International Stem Rust Resistance Screening Nursery coordinated by CIMMYT, DRRW, Borlaug Global Rust initiative (BGRI), Cornel University and KARI. We greatly acknowledge these institutions and organizations.
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