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COMBINING ABILITY EFFECTS AND INHERITANCE OF MATURITY AND YIELD ASSOCIATED TRAITS IN F2 POPULATIONS OF WHEAT.

Byline: K. Din, N.U. Khan, S. Gul, S.U. Khan, I. Tahir, Z. Bibi, S. Ali, S.A. Khan, N. Ali, I.A. Khalil and O. Mumtaz

Key words. L x T combining ability; gene action; earliness; yield related traits; F2 populations; Triticum aestivum L.

INTRODUCTION

Triticum aestivum L. is a highly self-pollinated and annual cereal crop. It is a member of family Gramminae and genus Triticaceae, and a hexaploid having chromosomes number of 42. Wheat has perfect hermaphrodite flower and sexually autogamous crop. Its domestication was started from the fertile areas of Middle East, and later became one of the most important food crops of the human population of the entire globe(Sabit et al., 2017). Wheat occupies a larger part of the cultivated area and has maximum annual production than other crops, and therefore, it termed as king of the cereals. Being major source of food and energy, it occupies a unique position in human life with a large number of end use products like chapatti, bread, macaroni's, biscuits, pasta and is also a good source of animal feed(Afridi et al., 2018; Sattar et al., 2018).

Wheat is an elegant source of nutrients and energy containing major constituents of the food i.e., vitamins particularly riboflavin, thiamin, niacin and vitamin E. Wheat is enriched by protein and carbohydrates and vital minerals such as phosphorus, magnesium, copper, iron and zinc, and about 36% of the world human population uses wheat as staple food(Bhanu et al., 2018). Gluten is made up of proteins that give strength, structure, and texture to the different forms of the bread. At the time of crop maturity, wheat grains contain 8 to 20% protein, while gluten proteins constitute 80 to 85% of total wheat grain protein(Shewry et al., 1995; Gautam et al., 2013).

Wheat can tolerate the wide range of environmental conditions, however, dry, clear, and cool environmental conditions guarantee the better crop production. Although the crop is well adapted to climate between the latitudes of 30Adeg and 60AdegN, and 27Adeg and 40AdegS(Nuttonson, 1955). During 2017-18, the global wheat production was estimated as 743 million tons(FAO, 2018). Globally top wheat producing countries are China, India, Russia, USA, France, Canada, Germany and Pakistan. In Pakistan during 2018-19, wheat was cultivated on an area of 8.740 million hectares, which produced 25.195 million tons of grains with average production of 2883 kg ha-1(PBS, 2018-19).Among the important crops, the wheat and maize crops showed positive growth at the rate of 0.5% and 6.9%, respectively in Pakistan(Pakistan Economic Survey, 2018-19). However, other major crops witnessed with negative growth as production of cotton, rice and sugarcane declined by 17.5%, 3.3% and 19.4%, respectively.

The increased population demands enhanced production of the major staple food crops such as wheat and rice. Therefore, it is a dire obligation of plant breeders to develop the wheat genotypes with earliness, rust resistance, good yield and quality potential(Sharmin et al., 2015; Ahmed et al., 2017).

Transgressive segregation based on classification of the genotypes, have the ability of transmitting genes of interest in specific genotypic combinations. Biometrical techniques used for analysis of combining ability and other genetic parameters are vital and helpful to the plant breeder in picking improved wheat genotypes for existing environments and production system(Afridi et al., 2017, 2018). Grain yield is an unpredictable trait made up from interaction between yield components and environmental effects(Hei et al., 2015; Ahmad et al., 2017). Dependency of grain yield on yield contributing traits need improvement in yield traits, which would eventually bring variation and improvement in grain yield and could be used as selection criteria.

In breeding programs, breeders are mostly interested in desirable genes and gene complexes and selection of promising populations. Line by tester matting design and its analysis is one of the vital breeding methods, which was developed by Kempthorne(1957). It is used to estimate the general(GCA) and specific combining ability(SCA) effects among parental lines, testers, and their line bye tester interactions/populations, respectively. Mean performance of a parental genotype in a series of cross combinations is known as general combining ability which helps the breeder in selection of promising genotypes for crossing program based on GCA effects and mean performance(Griffing, 1956; Singh and Chaudhary, 1985). Performance of one specific parental genotype with other parent in a specific cross combination is termed as specific combining ability, which could help in developing of promising hybrids(Majeed et al., 2011; Istipliler et al., 2015; Mandal and Madhuri, 2016).

Therefore, the knowledge about GCA and SCA effects among parental genotypes their F1 and F2 populations, and gene action involved in various traits is direly needed for the selection of potential parents for crossing and development of cultivars and hybrids in wheat(Kruvadi, 1991; Rabbani et al., 2009; Tiwari et al., 2017; Sattar et al., 2018). Earlier studies on combining ability and genetic architecture in wheat by using the line x tester mating fashion enunciated that GCA and SCA effects were significant among parental genotypes and their F1 populations, respectively for grain yield and its associated traits in bread wheat(Inamullah et al., 2006; Murugan and Kannan, 2017; Rahul, 2017; Hama-Amin and Towfiq, 2019). Past findings indicated that variances due to lines, testers and line x tester F1 hybrids were significant for all the traits which were controlled by both additive and non-additive gene effects in wheat(Abro et al., 2016; Kumar et al., 2019).

Grain yield and its component traits i.e., flag leaf area, fertile tillers, spike traits, 1000-grain weight and grain yield were controlled by additive gene action in wheat(Liu and Wei, 2006; Akram et al., 2008, 2009; Verma et al., 2016; Kumar et al., 2018; Sharma et al., 2019). However, some investigations revealed that grain yield and other agronomic traits i.e., days to maturity, plant height and spike traits were managed by non-additive gene action in wheat(Saeed and Khalil, 2017; Ingle et al., 2018; Farooq et al., 2019; Parveen et al., 2019).

Considering the above facts, the present study was planned with the aim to determine the potential parental lines and testers and their F2populations by estimating their GCA and SCA effects, respectively and to estimate the nature and magnitude of gene action for maturity and yield-related traits in a line x tester mating design in bread wheat.

MATERIALS AND METHODS

Breeding Material and Procedure: The present investigations were carried out during cropping seasons 2015-16 and 2017-18 at the Cereal Crops Research Institute(CCRI), Pirsabak - Nowshera, Pakistan(situated between 34Adeg N latitude and 72Adeg E longitude, at an altitude of 288 m).The soil of the experimental site was sandy loam with pH(7.7), organic matter(0.33%), N(0.016%), P2O5(4.00 ppm) and K2O(85.00 ppm). Six wheat lines i.e., IBWSN-177, IBWSN-52, IBWSN131, and SRN-09111 [obtained from International Bread Wheat Screening Nursery(IBWSN), CIMMYT, Mexico], PR-107(CCRI, Pirsabak - Nowshera, Pakistan) and NR-21 [National Agriculture Research Center(NARC), Islamabad, Pakistan] and three testers i.e., Pakhtunkhwa-15, Pirsabak-15, and Shahkar-13(CCRI, Pirsabak - Nowshera, Pakistan) were crossed during 2015-16 in a line by tester matting design(Table 1).

After advancing the generation during 2016-17, 18 F2 populations along their parental lines and testers were grown and evaluated during 2017-18 in a randomized complete block design(RCBD) with three replications. Each genotype was planted in four rows in each replication with five meter length, having plants and rows spacing of 10 and 30 cm, respectively.

Crop Husbandry: Before sowing, the field was well irrigated to create conditions conducive for seedbed preparation. The field was ploughed with deep plough then harrowed with planking each time to make the soil loose, fine, leveled and pulverized. The fertilizer was applied at the rate of 120:90:60 NPK kg ha-1. All P2O5, K2O and half N were applied at sowing time and the remaining half N was applied in two split doses with first and second irrigations. Sowing was carried out during 2nd week of November. In parental genotypes and their F2 populations the single seed per hill was planted. Overall, four irrigations have been given to the crop. The dominant weeds were Avena fatua, Chenopodium album, Chenopodium murale, Convolvulus arvensis, Cynodon dactylon, Phalaris minor and Rumex dentatus. The broad and narrow-leaved weeds were controlled with Buctril Super(750 ml ha-1) and Puma Super(1250 ml ha-1), respectively. However, the left over weed plants were removed manually.

Data Collection: Data were recorded on days to maturity(days), spike length(cm), spikelets per spike(#), 1000-grain weight(g), biological and grain yield per plant(g) using randomly selected 20 plants in parental genotypes and their F2 populations. The biological yield of pre-selected plants was measured at maturity and after proper sun drying; the plants were weighted using electric balance to obtaint he biological yield(g). Therandomly selected plants were harvested on single plant basis and used for the data recording separately after threshing with single plant thresher. A descriptive sample of 1000 grains was used in each entry/replication and weighed with an electric balance to record the 1000-grain weight(g). By weighing the grains of 20 plants in parent alcultivar sand their F 2 populations in each genotype/replication, and then averaged forgetting grain yield per plant(g).

Biometrical Analysis: Data on all the traits were initially subjected to the analysis of variance(ANOVA) to test the null hypothesis of no differences among various parental lines, tester sand their F2 populations(Steel etal., 1997). The genotype means for each parameter were further separated and compared by the least significant difference(LSD 0.05) test. Upon significant differences among the parental genotypes and their F 2 populations, the data were further subjected to line by tester combining ability analysis to as certain the variances and effects due to GCA and SCA among parental genotypes and line by tester F 2 populations,respectively,and gene action for various traits(Kempthorne,1957;Singh and Chaudhary, 1985).

Combining Ability Effects: The estimates of combining ability were computed by using line x tester analysis(Kempthorne,1957).The estimates of general combining ability(GCA) of lines and testers, and specific combining ability(SCA) of the F 2 populations were calculated as under:

Estimation of GCA Effects

(a)Lines:g i = xi../tr - x..../ltr

(b)Testers: gt= xj/lr - x.../xltr

Where:

l= Number of lines(female parents)

t= Number of testers(male parents)

r= Number of replications

x i = Total of F 2 resulting from crossing of ith lines with all the testers

x. j. = Total of all the F 2 crosses of jth testers with all lines

x...= Grand total of all the crosses

Estimation of SCA Effects

S ij = xij/r - xi../tr - x.j./lr + x.../ltr

Wherex ij =Total of F 2 resulting from crossing ith lines with jth testers

Standard Errors for Combining Ability Effects:

Standard errors for combining ability effects were calculated by the following equations:

S.E.(GCA)

S.E.(GCA for lines) = aMe/rt

S.E.(GCA for testers) = aMe/rl

S.E.(SCA effects for L x T Interactions) = aMe/r

S.E.(gl i-gl j) between lines GCA effects = a2Me/rt

S.E.(gt i-gt j) between tester sGCA effects a2Me/rl

S.E.(s ij-s kl) between SCA effects of two crosses = a2Me/r

Whereas;

S.E.= Standard error

M e = Error mean square

The distribution of populations in relation to GCA and SCA effects was worked out by taking significant positive combining ability effects as high, non-significant as average and significant negative as low for all the traits. For days to maturity, the significant positive combining ability effects were taken as low, non-significant as average and significant negative as high.

Genetic Components: Genetic components were calculated by following the Singh and Chaudhary(1985) as follows:

Cov.of half-sib(H.S) of line= Mt - Mtxl/rt

Cov.of half-sib(H.S) of tester= Mt-Mlxt/rt

While,

Cov Half-sib(H.S)(average)= 1/r(2lt-l-t)[(l-1)(Ml)(t-1)(Mt)/(l+t-2)] - mlxt

Cov Full-sib(F.S)=(Ml-Me)+(Mt-Me)+(Mlxt-Me)/3r + 6r Cov.H.S-r(l+t)Cov.HS/3r

Assuming no epistasis, variances due to GCA(I 2GCA)andSCA(I 2 SCA)were calculated as follows.

I 2GCA= [1+F/4]2 I2A

I 2 SCA= [1+F/4]2 I2D

Additive and dominance genetic variances(I 2 A andI 2D)were calculated by taking in breeding coefficient(F)equal to one i.e.,F=1(Singh and Narayanan, 2004).

Gene Action, and Degree of Dominance: Whenratioof I 2 GCA /I 2 SCA was less than unity, were taken as preponderance of non-additive type of gene action, greater than unity as additive and equal to unity were taken as equal effects of additive and non-additive type of gene action(Singh and Chaudhary, 1985). Similarly, when ratio of(I 2 D /I 2 A) 1/2 was less than unity, were taken as preponderance of additive gene effects, greater than unity as non-additive, and equal to unity was taken as equality of additive and non-additive effects.

Proportional Contribution of Populations to Total Variance: Contribution of lines, testers and L x T interactions/F 2 populations to the total variance were calculated in accordance with Singh and Chaudhary(1985).

Contribution of Lines= SS(lines)/SS(crosses)x100

Contribution of Testers= sS(testers)/SS(crosses)x100

Contribution of LxT Interactions= SS(lxt)/SS(crosses)x100

Whereas SS lines, SS testers, and SS line x tester interactions were sum of square for lines, testers, and line x tester interactions, respectively.

RESULTS

Analysis of Variance: Analysis of variance revealed significant(pa$?0.01) differences among the parental lines and testers,and their F 2 populations for all the studied parameters(Table2).Results showed inclusive genetic variation was found in the said breeding material which allows further assessment for general and specific combining ability effects and gene action. According to combining ability analysis of variance, lines revealed non-significant variations for all the studied traits.

Among testers, significant(pa$?0.01) differences were observed for days to maturity, spikelets per spike and 1000-grain weight. However, line x tester interactions revealed significant(pa$?0.01) differences for all the variables.

Genetic Variability in Lines, Testers and Line by Tester F 2 Populations

Days to Maturity: In parental lines, the days to maturity varied from147(SRN-09111) to152.3 days(IBWSN-177), testers ranging from 147.7(Pirsabak-15) to 149 days(Pakhtunkhwa-15)(Table3).InF 2 populations,days to maturity ranged from 146.3(NR-21 x Shahkar-13, IBWSN-131xShahkar-13)to152.3days(SRN-09111x Pirsabak-15). Overall, minimum and same days to maturity(146.3 days) were observed in two F 2 populationsi.e.,IBWSN-131xShahkar-13andNR-21xShahkar-13,followedbyIBWSN-177xShahkar-13and SRN-09111 with same days to maturity(147.0 days). However, maximum and same days to maturity(152.3 days) were observed in line IBWSN-177 and F 2 population SRN-09111 x Pirsabak-15, followed by F 2 populationPR-107xPakhtunkhwa-15(152.0days).

The remaining lines, testers and F 2 populations manifested medium days to maturity.Therefore,F 2 populations NR-21 x Shahkar-13, IBWSN-131 x Shahkar-13 and IBWSN-177 x Shahkar-13 could be used as source material for developing early maturing wheat genotypes in future wheat breeding programs.

Spike Length: In lines, the spike length varied from 12.1(IBWSN-52)to 12.9cm(IBWSN-131),tester sranged in between 13.7(Shahkar-13)and 14.9cm(Pakhtunkhwa-15)(Table3).InF 2 populations,the spike length ranged from 10.9(IBWSN-52 x Shahkar-13) to 14.3 cm(IBWSN-131 x Pakhtunkhwa-15). Overall, maximum spike length was observed in tester Pakhtunkhwa-15(14.9 cm), followed by F 2 population IBWSN-131 x Pakhtunkhwa-15 and tester Pirsabak-15 with same spike length(14.3cm). However, minimum spike length was observed in F 2 population IBWSN-52xShahkar-13(10.9 cm),followed by line SRN-09111(11.8cm). All other parental lines, testers and F 2 populations revealed medium valuesfor spike length. Plants with maximum spike length are preferred in wheat breeding programs because of its significant contribution in grain yield. Therefore, the F2 population IBWSN-131 x Pakhtunkhwa-15 and parental testers i.e., Pakhtunkhwa-15 and Pirsabak-15 could be used in future wheat breeding programs to improve the spike length.

Spikelets per Spike: In lines, the spikelets per spike varied from 18.3(IBWSN-52) to 20.5(PR-107), testers ranged from 20.5(Pakhtunkhwa-15) to 21.5(Shahkar-13)(Table 3). However, in F2 populations the spikelets per spike differed from 19.0(SRN-09111 x Pirsabak-15) to 23.0(IBWSN-131 x Pakhtunkhwa-15). Overall, the highest spikelets per spike were observed in F2 population IBWSN-131 x Pakhtunkhwa-15(23.0), followed by F2 populations IBWSN-177 x Shahkar-13(22.6) and SRN-09111 x Pakhtunkhwa-15(22.0). However, the lesser number of spikelets per spike was observed in line IBWSN-52(18.3), followed by F2 population SRN-09111 x Pirsabak-15(19.0). The remaining lines, testers and F2 populations showed medium number of spikelets per spike. Genotypes with more spikelets per spike are preferred because grain yield has significant(pa$?0.01) positive correlation with spikelets per spike.

Therefore, F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15, IBWSN-177 x Shahkar-13 and SRN-09111 x Pakhtunkhwa-15 could be used in future breeding program to enhance the spikelets per spike and eventually the grain yield.

1000-Grain Weight: In lines, 1000-grain weight ranged from 37.6(NR-21) to 45.7 g(IBWSN-52), testers varied from 32.4(Pirsabak-15) to 43.5 g(Pakhtunkhwa-15)(Table 3). In F2 populations the thousand grain weight varied from 35.5(IBWSN-177 x Shahkar-13) to 51.8 g(IBWSN-131 x Pakhtunkhwa-15). Overall, the maximum 1000-grain weight was observed in F2 population IBWSN-131 x Pakhtunkhwa-15(51.8 g), followed by line IBWSN-52(45.7 g) and F2 population PR-107 x Pakhtunkhwa-15(45.5 g). However, minimum 1000-grain weight was observed in tester Pirsabak-15(32.4 g), followed by F2 population IBWSN-177 x Shahkar-13(35.5 g). Other lines, testers and F2 populations showed the medium values for 1000-grain weight.

Genotypes with greater 1000-grain weight are preferred for selection, to increase the grain yield per unit area, therefore, the F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15, PR-107 x Pakhtunkhwa-15 and line IBWSN-52 could be used in future wheat breeding programs to improve the seed index.

Biological Yield per Plant: For lines the biological yield per plant varied from 60.4(SRN-09111) and 80.5 g(IBWSN-131) while in testers it ranged from 59.4(Pirsabak-15) to 73.7 g(Shahkar-13)(Table 3). In F2 populations the biological yield per plant ranged in between 61.7(IBWSN-177 x Shahkar-13) and 88.3 g(PR-107 x Shahkar-13). Overall, the highest biological yield per plant was observed in F2 population PR-107 x Shahkar-13(88.3 g), followed by SRN-09111 x Shahkar-13(86.6 g) and line IBWSN-131(80.5 g). However, the least biological yield per plant was recorded in tester Pirsabak-15(59.4 g) followed by line SRN-09111(60.4 g). The remaining genotypes showed medium values for biological yield per plant.

Genotypes with increased biological yield are preferred for selection with aim to use in future breeding program to develop wheat genotypes with increased fodder for livestock. Therefore, the F2 populations PR-107 x Shahkar-13, SRN-09111 x Shahkar-13 and line IBWSN-131 could be used in future breeding programs to enhance the fodder yield.

Grain Yield per Plant: Grain yield per plant is a complex trait which managed by yield contributing traits. In parental lines, the grain yield per plant ranged from 21.9(NR-21) to 29.5 g(IBWSN-52), testers varied in between 19.2(Pirsabak-15) and 27.6 g(Pakhtunkhwa-15)(Table 3). In F2 populations, the grain yield per plant differed from 22.7(SRN-09111 x Pakhtunkhwa-15) to 30.4 g(IBWSN-131 x Pakhtunkhwa-15). Overall, the highest grain yield per plant was observed in F2 population IBWSN-131 x Pakhtunkhwa-15(30.4 g), followed by F2 populations IBWSN-131 x Shahkar-13(30.0 g) and PR107 x Shahkar-13(29.95 g). However, the least grain yield per plant was observed in tester Pirsabak-15(19.2 g), followed by line NR-21(21.9 g) and F2 population SRN-09111 x Pakhtunkhwa-15(22.7 g). The remaining lines, testers and F2 populations exhibited medium values for grain yield per plant.

Grain yield per plant directly affects the overall grain yield; therefore, the F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15, IBWSN-131 x Shahkar-13 and PR107x Shahkar-13 were favored to select and to use in future wheat breeding programs to enhance the final grain yield per unit area.

Overall, the lines IBWSN-131 and IBWSN-52 performed better for spike length, 1000-grain weight, grain yield and biological yield per plant with medium days to maturity. Among the testers, Pakhtunkhwa-15 followed by Shahkar-13, revealed best mean performance for spike length, 1000-grain weight, and grain yield per plant. Among F2 populations, IBWSN-131 x Pakhtunkhwa-15, followed by NR-21 x Shahkar-13 and PR-107 x Shahkar-13, manifested immense and desirable mean values for spike length, spikelets per spike, 1000-grain weight, and grain yield per plant. Results further revealed that F2 populations were observed with improved performance than lines and testers for all the studies traits. Therefore, these promising and best performing populations could be used as gene source in future wheat breeding program to develop genotypes with early maturity and good yield potential.

Combining Ability Analysis: Greater genetic variation in the said breeding material allowed further analysis and partition of combining ability into its components i.e., general and specific combining ability effects in lines, testers and line by tester interactions, respectively(Table 2). According to GCA and SCA effects, the positive values are found desirable for majority of the traits in crop plants i.e., growth and yield related traits. However, negative GCA and SCA effects are enviable for those traits where minimum values are required and pleasing i.e., early flowering and maturity.

General and Specific Combining Ability Effects: For days to maturity, in lines the GCA effects ranged from - 1.19 to 1.81. Three lines i.e., IBWSN-131, IBWSN-177 and NR-121 showed negative and desirable GCA effects while the other three lines i.e., IBWSN-52, SRN-09111 and PR-107 revealed positive GCA effects(Table 4). Significant(pa$?0.01) negative GCA effects were observed in line IBWSN-131(-1.19) followed by NR-21(-0.85), while maximum positive GCA effects were observed in line SRN-09111(1.81). In case of testers, the GCA effects ranged from-1.13 to 1.04 for days to maturity. The tester Shahkar-13 showed significant(pa$?0.01) negative GCA effects while the remaining two testers i.e., Pirsabak-15 and Pakhtunkhwa-15 manifested positive GCA effects. Significant(pa$?0.01) negative GCA effects were shown by tester Shahkar-13(-1.13) while the maximum positive GCA effects were exhibited by Pakhtunkhwa-15(1.04).

Overall in parental lines and testers, the highest negative and desirable GCA effects were recorded in line IBWSN-131 and tester Shahkar-13and were identified as best general combiners for days to maturity. For days to maturity, in F2 populations the SCA effects ranged from-2.15 to 1.96(Table 5). Ten out of eighteen F2 populations showed negative and desirable SCA effects ranging from-2.15 to-0.4 while the other eight F2 populations revealed positive SCA effects(0.02 to 1.96) for days to maturity. The significant(pa$?0.01)negative SCA effects were observed in F2 population SRN-09111 x Pakhtunkhwa-15(-2.15), followed by two other F2 populations i.e., PR-107 x Pirsabak-15(-1.76) and NR-21 x Pirsabak-15(-1.22), and identified as best specific combiners. Results further revealed that low x low general combiners were involved in these F2 populations with promising SCA for the said trait.

However, the maximum positive SCA effects were observed in F2 population PR-107 x Pakhtunkhwa-15(1.96), followed by two other F2 populations i.e., SRN-09111 x Pirsabak-15(1.80) and IBWSN-177 x Pakhtunkhwa-15(0.52).

For spike length, in lines the GCA effects ranged in between-0.83 and 0.59(Table 4). Three lines(IBWSN-177, IBWSN-131 and PR-107) showed positive and desirable GCA effects while the other three lines i.e., IBWSN-52, SRN-09111, and NR-21 showed negative GCA effects. Significant(pa$?0.01) positive GCA effects were observed in line IBWSN-131(0.59) while maximum negative GCA effects were recorded in line IBWSN-52(-0.83). In testers, the GCA effect varied from-0.38 to 0.28 for spike length. Two testers i.e., Pakhtunkhwa-15 and Pirsabak-15 showed the positive GCA effects while the tester Shahkar-13revealed negative GCA effects. Significant(pa$?0.01) positive GCA effects governed by tester Pirsabak-15(0.28) while the maximum negative GCA effects shown by Shahkar-13(-0.38). Overall, in parental genotypes the highest and desirable GCA effects were recorded in line IBWSN-131(0.59) followed by tester Pirsabak-15(0.28) and identified as best general combiners for spike length.

For spike length, in F2 populations the SCA effects ranged from-0.73 to 0.83(Table 5). The 50% of the F2 populations revealed positive and desirable SCA effects ranging from 0.16 to 0.83 while other 50% showed negative SCA effects(-0.73 to-0.05). Significant(pa$?0.01) positive SCA effects were observed in F2 population IBWSN-131 x Pakhtunkhwa-15(0.83) followed by SRN-09111 x Shahkar-13(0.59), and were considered as best specific cross combinations. Results further revealed that highx low and low x low general combiners were engaged in the F2 populations with promising SCA for spike length. However, the maximum negative SCA effects were found in F2 population PR-107 x Pakhtunkhwa-15(-0.73), followed by F2 population IBWSN-52 x Shahkar-13(-0.68).

For spikelets per spike, the parental lines GCA effects ranged from-0.85 to 1.07(Table 4).Four lines i.e., IBWSN-177, IBWSN-52, IBWSN-131 and NR-21 showed positive and desirable GCA effects while the other two lines(SRN-09111, PR-107) showed negative GCA effects. Significant(pa$?0.01) positive GCA effects were observed in line IBWSN-177(1.07) while maximum negative GCA effects were noted in line PR-107(-0.85). In testers, for spikelets per spike the GCA effects were ranging in between-0.68 and 0.79. The tester Pakhtunkhwa-15 showed positive GCA effects while other two testers(Pirsabak-15 and Shahkar-13) showed negative GCA effects. significant(pa$?0.01) positive GCA effects were observed in Pakhtunkhwa-15(0.79) while maximum negative GCA effects were observed in Pirsabak-15(-0.68).

Overall, in parental genotypes the maximum positive and desirable GCA effects were observed in line IBWSN-177(1.07) followed by tester Pakhtunkhwa-15(0.79), and considered as best general combiners for spikelets per spike.

For spikelets per spike, in F2 populations the SCA effects ranged from-1.03 to 1.21(Table 5). Nine out of eighteen F2 populations showed revealed and desirable SCA effects ranging from 0.06 to 1.21 while the other nine F2 populations exhibited negative SCA effects(-1.03 to-0.25) for spikelets per spike. Significant(pa$?0.05) positive SCA effects were observed in F2 population IBWSN-131 x Pakhtunkhwa-15(1.21), followed by SRN-09111 x Pakhtunkhwa-15(0.97). However, maximum negative SCA effects were observed in F2 population PR-107 x Pakhtunkhwa-15(-1.03), followed by IBWSN-131 x Shahkar-13(-0.96). Spikelets per spike are positively correlated with grain yield, and therefore, genotypes having positive combining ability for spikelets per spike are preferred for selection. Therefore, the F2 populations IBWSN-131 x Pakhtunkhwa-15, SRN-09111 x Pakhtunkhwa-15 and IBWSN-177 x Shahkar-13 were considered as the best specific combiners for spikelets per spike.

Results further revealed that low x high and low x low general combiners were involved in the management of best F2 populations with promising SCA. For 1000-grain weight, the lines GCA effects varied from-3.50 to 1.89(Table 4). Four lines i.e., IBWSN-52, IBWSN-131, SRN-09111 and PR-107 enunciated positive and desirable GCA effects while the other two lines(IBWSN-177 and NR-21) showed negative GCA effects for 1000-grain weight. Significant(pa$?0.01) positive GCA effects were observed in line PR-107(1.89), followed by IBWSN-131(1.76) and SRN-09111(1.23), while maximum negative GCA effects were recorded in line NR-21(-3.50). In testers, the GCA effects ranged from-2.11 to 2.46 for 1000-grain weight. Results further revealed that tester Pakhtunkhwa-15 showed positive GCA effects while the remaining two testers(Pirsabak-15 and Shahkar-13) showed negative GCA effects.

Significant(pa$?0.01) positive GCA effects were observed in tester Pakhtunkhwa-15(2.46) while maximum negative GCA effects were exhibited by tester Shahkar-13(-2.11). Overall, in parental genotypes, maximum positive GCA effects were found in tester Pakhtunkhwa-15, followed by lines i.e., PR-107and IBWSN-131, and were identified as best general combiners. For 1000-garin weight, in F2 populations the SCA effects ranged from-4.19 to 5.27(Table 5). Ten populations showed positive SCA effects ranging from 0.02 to 5.27 while the remaining eight populations showed negative SCA effects(-4.19 to-1.09). Significant(pa$?0.01) positive SCA effects were recorded in F2 population IBWSN-131 x Pakhtunkhwa-15(5.27), followed by two other F2 populations i.e., IBWSN-177 x Pakhtunkhwa-15(3.19) and IBWSN-52 x Shahkar-13(2.91). However, maximum negative SCA effects were found in F2 population IBWSN-131 x Shahkar-13(-4.19), followed by IBWSN-52 x Pakhtunkhwa-15(-3.64).

The positive combining ability is more important for 1000-grain weight because it has significant positive correlation with grain yield and it can be used as selection criteria in wheat breeding program. Present results revealed that parental tester and lines i.e., Pakhtunkhwa-15, PR-107 and IBWSN-131 could be used as best general combiners for 1000-grain weight while F2 populations IBSWN-131 x Pakhtunkhwa-15, IBWSN-177 x Pakhtunkhwa-15 and IBWSN-52 x Shahkar-13 could be used as best specific combiners in future wheat breeding program. Results further revealed that high x high and low x high general combiners were involved in the F2 populations with promising SCA.

For biological yield per plant, the lines GCA effects varied from-5.37 to 8.34(Table 4). Two lines i.e., SRN-09111 and PR-107 revealed positive GCA effects while the remaining four lines(IBWSN-177, IBWSN-52, IBWSN-131 and NR-21) showed negative GCA effects. Significant(pa$?0.01) positive GCA effects were observed in line SRN-09111(8.34), followed by PR-107(3.83), while maximum negative GCA effects were noted in line IBWSN-52(-5.37). In testers, the GCA effects varied from-3.29 to 5.45 for biological yield per plant. The tester Shahkar-13exhibited positive GCA effects while the remaining two testers(Pakhtunkhwa-15 and Pirsabak-15) showed negative GCA effects. Significant(pa$?0.01) positive GCA effects were observed in tester Shahkar-13(5.45) while maximum negative GCA effects were manifested by tester Pirsabak-15(-3.29).

Overall, in parental genotypes, the highest positive and desirable GCA effects were recorded in line SRN-09111(8.34), followed by tester Shahkar-13(5.45), and were considered as best general combiners for the said trait. For biological yield per plant, in F2 populations the SCA effects ranged from-11.45 to 11.49(Table 5). Half of the F2 populations showed positive and desirable SCA effects(0.94 to 11.49) while the remaining half showed negative SCA effects(-11.45 to-0.04). The F2 population IBSWN-177 x Pakhtunkhwa-15(11.49) showed significant(pa$?0.01) positive SCA effects, followed by PR-107 x Shahkar-13(7.55), and were identified as best specific combiners for biological yield. Results further revealed that low x high and low x low general combiners were occupied in the F2 populations with promising SCA. However, maximum negative SCA effects were observed in F2 population IBWSN-177 x Shahkar-13(-11.45), followed by PR-107 x Pakhtunkhwa-15(-4.91).

For grain yield per plant, the lines GCA effects varied from-1.15 to 2.54(Table 4). Two lines i.e., IBWSN-52 and IBWSN-131 exhibited positive and desirable GCA effects while remaining four lines(IBWSN-177, SRN-09111, PR-107 and NR-21) showed negative GCA effects. Significant(pa$?0.01) positive GCA effects were observed in line IBWSN-131(2.54) while maximum negative GCA effects were observed in line SRN-09111(-1.15). In testers, the GCA effects varied from-0.62 to 1.19 for grain yield per plant. The tester Shahkar-13 showed the positive GCA effects while the remaining two testers i.e., Pakhtunkhwa-15 and Pirsabak-15 presumed with negative GCA effects. Significant(pa$?0.01) positive GCA effects were found in tester Shahkar-13(1.19) while the maximum negative GCA effects were observed in tester Pakhtunkhwa-15(-0.62).

Overall, in parental genotypes, maximum positive and desirable GCA effects were recorded in line IBWSN-131 and tester Shahkar-13, and were identified as best general combiners for grain yield. For grain yield per plant, among F2 populations the SCA effects ranged from-3.02 to 2.81(Table 5). Eight F2 populations were found with positive and desirable SCA effects ranging from 0.25 to 2.81 while the remaining ten F2 populations exhibited negative SCA effects(-3.02 to-0.10). Significant(pa$?0.01) positive SCA effects were found in F2 population PR-107 x Shahkar-13(2.81), followed by two other F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15(2.01) and IBWSN-177 x Pirsabak-15(1.93), which could be further studied as best specific combiners for improving the grain yield in wheat. Results further revealed that low x high and low x low general combiners were implied in the F2 populations with promising SCA.

Maximum negative SCA effects were observed in F2 population IBWSN-177 x Shahkar-13(-3.02), followed by SRN-09111 x Pakhtunkhwa-15(-2.03). Grain yield is a complex trait and its variance managed by different yield attributing traits.

Present study revealed that in parental genotypes, the line IBWSN-131 proved to be the best general combiner for days to maturity, spike length 1000-grain weight, and grain yield per plant. Among testers, the cultivar Shahkar-13 observed to be the best general combiner for days to maturity, biological yield and grain yield per plant, while Pakhtunkhwa-15 manifested best GCA effects for spikelets per spike and 1000-grain weight. Overall, the lines(IBWSN-131 and IBWSN-177), testers(Shahkar-13 and Pakhtunkhwa-15), and their F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15, SRN-0911 x Shahkar-13 and IBWSN-177 x Pirsabak-15, were found as best general and specific combiners, respectively and performed better for maturity and yield traits. High x high, low xhigh and high x low general combiners were involved in the presentation of F2 populations with promising SCA and best mean performance.

Gene Action, and Degree of Dominance: Overall, the variances due to general combining ability(I2GCA) were lower than variances of specific combining ability(I2SCA) for all the studies traits, suggesting the preponderance of non-additive gene effects which controlled these characters(Table 6). The values of dominance genetic variance were greater than additive for all traits. These results were also supported by the ratios of variances of GCA to SCA(I2GCA/I2SCA) which were found smaller than unity while the rations of degree of dominance(I2D/I2A)1/2 were greater than unity for all the traits. Therefore, it appeared that the inheritance of all the traits was controlled by non-additive gene action. The differed ratio of GCA and SCA variances(I GCA/I SCA) always based on frequencies of alleles found in parental genotypes. The diverse parental genotypes had favorable ratio of GCA and SCA variances because of their high GCA effects.

Proportional Contribution of Populations to Total Variance: In proportional contribution to total variance, the L x T interactions/F2 populations have shown maximum share and contribution to total variance for majority of the traits i.e., spike length(42.72%), spikelets per spike(34.67%), 1000-grain weight(39.11%), biological yield per plant(40.63%) and grain yield per plant(47.63%), followed by lines(Table 7). For days to maturity(35.66%), the share of lines was leading as compared to testers and L x T interactions. Results further revealed that line x tester interactions and parental lines played an important role in managing the variation in the studied traits.

Table 1. Parental genotypes(lines and testers) and their line by tester F2 populations used in the studies.

S. No.###Parental genotypes###S. No.###F2 Populations

Lines###4###IBWSN-52 x Pakhtunkhwa-15

###1###IBWSN-177###5###IBWSN-52 x Pirsabak-15

###2###IBWSN-52###6###IBWSN-52 x Shahkar-13

###3###IBWSN-131###7###IBWSN-131 x Pakhtunkhwa-15

###4###SRN-09111###8###IBWSN-131 x Pirsabak-15

###5###PR-107###9###IBWSN-131 x Shahkar-13

###6###NR-21###10###SRN-09111 x Pakhtunkhwa-15

Testers###11###SRN-09111 x Pirsabak-15

###1###Pakhtunkhwa-15###12###SRN-09111 x Shahkar-13

###2###Pirsabak-15###13###PR-107 x Pakhtunkhwa-15

###3###Shahkar-13###14###PR-107 x Pirsabak-15

F2 Populations###15###PR-107 x Shahkar-13

###1###IBWSN-177 x Pakhtunkhwa-15###16###NR-21 x Pakhtunkhwa-15

###2###IBWSN-177 x Pirsabak-15###17###NR-21 x Pirsabak-15

###3###IBWSN-177 x Shahkar-13###18###NR-21 x Shahkar-13

Table 2. Mean squares for various traits among line by tester populations in wheat.

Source of variation###d.f.###Days to###Spike###Spikelets###1000-grain###Biological###Grain yield

###maturity###length###spike-1###weight###yield plant-1###plant-1

Replications###2###0.94###0.13###2.70###1.66###79.96###4.08

Genotypes###26###8.45**###2.05**###3.64**###47.03**###177.95**###22.48**

Parents###8###8.90*###3.46**###2.82**###52.01**###143.17**###33.85**

Parents vs. Crosses###1###7.14 *###0.77*###9.48 **###56.13 **###112.18*###55.78**

Crosses###17###8.31 **###1.47 **###3.68 **###44.15 **###198.18**###15.17**

Lines###5###10.07NS###2.03NS###4.24NS###53.09NS###237.25NS###19.29NS

Testers###2###21.24*###2.07NS###9.84*###95.76 *###407.01NS###19.28NS

Lines vs. Testers###10###4.84 **###1.07 **###2.17 **###29.35 **###136.88**###12.28**

Error###52###1.49###0.15###0.65###1.84###18.40###2.21

C.V.%###-###0.82###3.03###3.90###3.25###6.01###5.74

Table 3. Mean performance of lines, testers and line by tester F2 populations for various traits in wheat.

Lines, Testers###Days to###Spike###Spikelets###1000-grain###Biological###Grain

and F2 Populations###maturity###length###spike-1###weight(g)###yield plant-###yield

###(Days)###(cm)###1(g)###plant-1(g)

Lines

IBWSN-177###152.3###12.4###19.9###44.4###72.5###23.4

IBWSN-52###151.7###12.1###18.3###45.7###73.9###29.5

IBWSN-131###148.7###12.9###19.3###43.3###80.5###26.3

SRN-09111###147.0###11.8###19.8###38.5###60.4###23.9

PR-107###149.3###12.3###20.5###39.5###64.9###28.2

NR-21###149.0###12.3###20.2###37.6###66.4###21.9

Testers

Pakhtunkhwa-15###149.0###14.9###20.5###43.5###68.7###27.6

Pirsabak-15###147.7###14.3###21.1###32.4###59.4###19.2

Shahkar-13###148.7###13.7###21.5###40.0###73.7###22.7

F2 Populations

IBWSN-177 x Pakhtunkhwa-15###150.0###13.2###22.2###45.2###77.1###25.9

IBWSN-177 x Pirsabak-15###148.3###13.4###21.0###38.0###64.4###26.7

IBWSN-177 x Shahkar-13###147.0###12.2###22.6###35.5###61.7###23.6

IBWSN-52 x Pakhtunkhwa-15###149.7###12.4###21.1###42.4###63.3###26.5

IBWSN-52 x Pirsabak-15###148.3###12.5###20.7###44.0###66.2###27.7

IBWSN-52 x Shahkar-13###148.0###10.9###21.3###44.4###68.7###28.2

IBWSN-131 x Pakhtunkhwa-15###148.3###14.3###23.0###51.8###63.9###30.4

IBWSN-131 x Pirsabak-15###147.7###13.1###20.1###42.6###69.3###26.7

IBWSN-131 x Shahkar-13###146.3###12.7###19.9###37.8###77.5###30.0

SRN-09111 x Pakhtunkhwa-15###149.3###12.4###22.0###43.7###80.3###22.7

SRN-09111 x Pirsabak-15###152.3###12.7###19.0###44.4###72.5###25.8

SRN-09111 x Shahkar-13###149.7###12.9###19.7###42.4###86.6###27.6

PR-107 x Pakhtunkhwa-15###152.0###12.3###19.8###45.5###68.2###24.4

PR-107 x Pirsabak-15###147.3###13.6###19.8###43.9###69.3###23.5

PR-107 x Shahkar-13###147.7###12.9###20.5###43.1###88.3###29.95

NR-21 x Pakhtunkhwa-15###148.7###12.4###21.8###39.8###62.9###25.4

NR-21 x Pirsabak-15###148.3###12.9###20.5###38.8###67.2###25.1

NR-21 x Shahkar-13###146.3###12.7###20.6###37.8###78.5###26.9

LSD0.05###2.00###0.64###1.32###2.22###7.03###2.44

Table 4. General combining ability effects among lines and testers for various traits in wheat.

Parental genotypes###Days to###Spike###Spikelets###1000-grain###Biological yield###Grain yield

###maturity###length###spike-1###weight###plant-1###plant-1

Lines

IBWSN-177###-0.19###0.16###1.07**###-2.71**###-3.70*###-1.11*

IBWSN-52###0.04###-0.83**###0.19###1.33**###-5.37**###0.96

IBWSN-131###-1.19**###0.59**###0.14###1.76**###-1.21###2.54**

SRN-09111###1.81**###-0.08###-0.65*###1.23**###8.34**###-1.15*

PR-107###0.37###0.21###-0.85**###1.89**###3.83*###-0.55

NR-21###-0.85*###-0.05###0.10###-3.50**###-1.88###-0.70

S.E.###0.41###0.13###0.27###0.45###1.43###0.50

CD0.05###0.83###0.26###0.54###0.92###2.90###1.01

CD0.01###1.11###0.35###0.73###1.23###3.90###1.35

Testers

Pakhtunkhwa-15###1.04**###0.09###0.79**###2.46**###-2.16**###-0.62

Pirsabak-15###0.09###0.28**###-0.68**###-0.34###-3.29**###-0.57

Shahkar-13###-1.13**###-0.38**###-0.10###-2.11**###5.45**###1.19**

S.E.###0.29###0.09###0.19###0.32###1.01###0.35

CD0.05###0.58###0.19###0.39###0.65###2.05###0.71

CD0.01###0.78###0.25###0.52###0.87###2.76###0.96

Table 5. Specific combining ability effects among line by tester F2 populations for various traits in wheat.

F2 Populations###Days to###Spike###Spikelets###1000-grain###Biological###Grain yield

###maturity###length###spike-1###weight###yield plant-1###plant-1

IBWSN-177 x Pakhtunkhwa-15###0.52###0.19###-0.52###3.19**###11.49**###1.09

IBWSN-177 x Pirsabak-15###-0.20###0.16###-0.25###-1.25###-0.04###1.93*

IBWSN-177 x Shahkar-13###-0.31###-0.35###0.77###-1.94*###-11.45**###-3.02**

IBWSN-52 x Pakhtunkhwa-15###-0.04###0.42###-0.70###-3.64**###-0.57###-0.38

IBWSN-52 x Pirsabak-15###-0.43###0.25###0.37###0.73###3.43###0.85

IBWSN-52 x Shahkar-13###0.46###-0.68**###0.33###2.91**###-2.85###-0.47

IBWSN-131 x Pakhtunkhwa-15###-0.15###0.83**###1.21*###5.27**###-4.20###2.01*

IBWSN-131 x Pirsabak-15###0.13###-0.52###-0.25###-1.09###2.34###-1.78*

IBWSN-131 x Shahkar-13###0.02###-0.31###-0.96*###-4.19**###1.86###-0.24

SRN-09111 x Pakhtunkhwa-15###-2.15**###-0.35###0.97*###-2.23**###2.65###-2.03

SRN-09111 x Pirsabak-15###1.80*###-0.24###-0.53###1.26###-4.02###1.02

SRN-09111 x Shahkar-13###0.35###0.59*###-0.44###0.97###1.37###1.01

PR-107 x Pakhtunkhwa-15###1.96**###-0.73**###-1.03*###-1.11###-4.91###-0.94

PR-107 x Pirsabak-15###-1.76*###0.39###0.47###0.02###-2.64###-1.87*

PR-107 x Shahkar-13###-0.20###0.34###0.56###1.08###7.55**###2.81**

NR-21 x Pakhtunkhwa-15###-0.15###-0.35###0.06###-1.49###-4.46###0.25

NR-21 x Pirsabak-15###-1.22*###0.46###-0.05###0.19###0.33###0.94

NR-21 x Shahkar-13###-0.32###-0.31###0.40###-0.25###1.16###3.53

S.E.###0.57###0.70###0.22###0.46###0.78###2.48

C.D0.05###1.15###1.43###0.46###0.94###1.59###5.03

C.D0.01###1.55###1.92###0.61###1.27###2.13###6.75

Table 6. Genetic components among line by tester populations for various traits in wheat.

Genetic###Days to###Spike length###Spikelets###1000-grain###Biological###Grain yield

Components###maturity###spike-1###weight###yield plant-1###plant-1

2GCA###0.10###0.01###0.05###0.44###1.84###0.09

2SCA###1.12###0.30###0.50###9.17###39.50###3.35

2A###0.21###0.02###0.09###0.89###3.67###0.17

2D###1.12###0.30###0.50###9.17###39.50###3.35

2GCA/2SCA###0.09###0.04###0.08###0.04###0.04###0.02

(2D/2A)1/2###2.32###3.55###2.36###3.21###3.28###4.40

Table 7. Proportional contribution of lines, tester and line by tester populations.

###Line x

Parameters###Lines###Testers###Tester

###(%)###(%)###interactions

###(%)

Days to maturity###35.66###30.07###34.27

Spike length###40.66###16.62###42.72

Spikelets spike-1###33.90###31.43###34.67

Biological yield plant-1###35.21###24.16###40.63

1000-grain weight###35.37###25.52###39.11

Grain yield plant-1###37.41###14.96###47.63

DISCUSSION

General and specific combining ability variances were estimated with a view to interpret the genetic design of the maturity and yield related traits of L x T F2populations in wheat. The enormity and tendency of combining ability effects provides the strategy for the utilization of potential parental lines and testers in hybridization programs. Combining ability illustrates the breeding value of parental lines and testers to produce F1 and F2 populations in a breeding program and to exercise the selection in terms of maturity and yield related traits in wheat(Romanus et al., 2008).

Present results revealed that significant(pa$?0.01) differences were observed among the parental lines, testers and their F2 populations for all the traits. Breeding material with comprehensive genetic variation allowed further assessment for general and specific combining ability effects(Kempthorne, 1957). In combining ability analysis of variance, testers revealed significant(pa$?0.01) differences for majority of the traits. However, mean squares due to line x tester interactions were significant(pa$?0.01) for all the traits. Earlier studies reported significant differences among the wheat populations for the concerned traits, predict sufficient genetic variation which provides open choice for the selection of best individual genotypes in wheat(Ahmad et al., 2017; Ahmed et al., 2017; Rahul, 2017; Farooq et al., 2019).

Past studies of on line x tester combining ability analysis revealed significant differences among the parental genotypes and their F1 and F2 populations for maturity and yield related traits in wheat(Esmail, 2007; Ingle et al., 2018; Kumar et al., 2018). Ahmad et al.(2017) estimated the GCA and SCA effects and heritability through L x T combining ability, and observed significant differences among the parental lines, testers and line by tester populations for maturity, morphological and yield traits in wheat. Past studies about line x tester combining ability revealed similar pattern of significance and inheritance for various traits in wheat populations(Akram et al., 2008, 2009; Fellahi et al., 2013; Tabassum et al., 2017). Bibi et al.(2013) findings revealed that parental lines, testers and their F1 populations showed significant differences for spike length, spikelets per spike 1000-grain weight and grain yield per plant in LxT combining ability analysis in wheat.

Combining ability of parental genotypes(lines and testers) and their line x tester F2 populations were studied through line by tester analysis to estimate the ability of parental genotypes to combine their favorable genes in F2 populations after hybridization. Two kinds of combining abilities(GCA and SCA) were studied in the present research. Generally, the GCA effects are manifested due to additive gene effects while SCA effects are due to dominant or epistatic gene effects(Griffing, 1956; Kempthorne, 1957). Parental genotypes(lines and testers) having desirable GCA effects were considered as best parental genotypes and good general combiners for the said trait in wheat breeding(Afridi et al., 2017, 2018; Parveen et al., 2019). However, F2 populations having desirable SCA effects were considered as best specific combiners for the concerned trait.

The F2 populations i.e., NR-21 x Shahkar-13, IBWSN-131 x Shahkar-13 and IBWSN-177 x Shahkar-13 were identified as early maturing genotypes on the basis of mean performance and desirable negative SCA effects. There promising populations could be used in future wheat breeding programs for the development of the wheat genotypes with early maturity and good yield potential. In wheat breeding, early maturity is preferred because the early maturing genotypes could escape from biotic and abiotic stresses, save the inputs, vacate the soil early for the following crop and earlier access of the crop produce to the market to fetch best prices(Faisal et al., 2005; Afridi, 2016). Previous studies reported that lines, testers and their hybrid populations were found to have significant negative GCA and SCA effects and took less day to maturity in wheat(Esmail, 2007; Akram et al., 2008).

Past findings also revealed that wheat genotypes with minimum days to maturity were more preferable from farmer and end user point of view(Akram et al., 2009; Afridi et al., 2018).

In wheat crop, the increased spike length and number of spikelets are preferred because it contributes significantly to the final grain yield. Therefore, the F2 population IBWSN-131 x Pakhtunkhwa-15, and parental line and tester i.e., IBWSN-177 and Pakhtunkhwa-15, respectively involved in the said cross combination performed better for spike traits with desirable GCA and SCA effects. These promising populations could be used in future breeding programs to increase the spike length and spikelets per spike in wheat. Spike length and spikelets per spike are important components of grain yield and have significant positive correlation with grain yield; hence, these spike traits affects the grain yield directly in wheat. Present results revealed that wheat genotypes with maximum spike length were found more vigorous and produce greater grain yield, which got support from the past findings in wheat(Farooq et al., 2019; Parveen et al., 2019).

Wheat genotypes revealed significant differences for spike length and spikelets per spike, and increased spike length also leads to enhanced grain yield(Yucel et al., 2009; Cifci and Yagdi, 2010; Kumar et al., 2019). Earlier study of line x tester combining ability reported that F1 population 9738 x Chakwal-50 manifested the significant and desirable SCA effects for spike related characters, while 9740 x Chakwal-50 was found as best specific combiner for the grain yield(Sattar et al., 2018).

Earlier studies revealed that F1 hybrids with increased spike length and spikelets were found more productive and also have greater grain yield than their parental genotypes in wheat(Afridi, 2016; Afridi et al., 2017). Previous studied also exhibited that lines Faisalabad-85 and Faisalabad-83 and tester PBW-65 were found as best general combiners for spike length and spikelets per spike while F2 populations Faisalabad-83 x PBW-65 and Faisalabad-85 x PBW-502 were considered as best specific combiners for spike traits in wheat(Akbar et al., 2009).

Seed index is an important yield component which has direct positive impact on grain yield and genotypes with maximum 1000-grain weight significantly enhanced the grain yield(Hei et al., 2015; Mandal and Madhuri, 2016; Murugan and Kannan, 2017). In present study, the genotypes with greater 1000-grain weight were preferred to select for increased grain yield, therefore, based on mean performance and desirable SCA and GCA effects, the F2 populations i.e., IBWSN-131 x Pakhtunkhwa-15, IBWSN-177 x Pakhtunkhwa-15, IBWSN-52 x Shahkar-13, line IBWSN-52 and tester Pakhtunkhwa-15 could be used in future wheat breeding programs. Past studies reported that wheat genotypes revealed significant differences for seed index and the grain yield primarily depends on the 1000-grain weight(Awan et al., 2005; Akbar et al., 2009).

Khan et al.(2007) findings enunciated that some parental genotypes and specific cross combinations were identified as best general and specific combiners with significant GCA and SCA effects for 1000-grain weight in wheat. In other studies, both lines and testers showed positive GCA effects and most of their F1/F2populations exhibited positive SCA effects with best mean performance for 1000-grain weight and spike traits in wheat(Ajmal et al., 2004; Farooq et al., 2006; Saeed and Khalil, 2017).

Wheat genotypes with greater vegetative growth, more foliage and eventually increased biological yield will provide more fodder and to help in food security of the livestock. In present studies, the F2 populations i.e., PR-107 x Shahkar-13, IBSWN-177 x Pakhtunkhwa-15, SRN-09111 x Shahkar-13, line IBWSN-131 and tester Shahkar-13 were found as promising populations based on best mean performance for biological yield with desirable GCA and SCA effects, and which can be used for development of the genotypes with enhanced biological and fodder yield in future wheat breeding programs. Previous studies revealed that F1 and F2 populations with desirable SCA effects revealed greater performance than parental genotypes for biological yield in wheat(Khan et al., 2007; Khattab et al., 2010). Wheat genotypes revealed significant differences for biological yield and the genotypes with increased biological yield were found more desirable from fodder point of view(Kumar and Anil, 2011).

Previous studied revealed that lines and testers were observed with significant positive GCA effects while their some of the F2 populations were also identified as best specific combiners for biological yield in wheat(Jain and Sastry, 2012). Similar variation for tillers per plant was observed among the wheat testers, lines, F1 and F2 populations and their studies further revealed that genotypes with maximum tillers have greater biological and grain yield(Ahmad et al., 2010; Abd-El-Mohsen et al., 2012). Past studies about line by tester analyses revealed significant differences among lines, testers and line x tester interactions for plant height, tiller per plant, and the populations revealed greater genetic variability for morphological and yield traits(Nour et al., 2011; Ahtisham et al., 2014; Tripathi et al., 2015).

Past studies revealed that lines and testers and their L x T populations revealed desirable GCA and SCA effects and suggested as best general and specific combiners for biological yield in wheat(Araus and Cairns, 2016; Murugan and Kannan, 2017).

Grain yield per plant directly affects the overall grain yield; therefore, the F2 populations IBWSN-131 x Pakhtunkhwa-15, IBWSN-131 x Shahkar-13, IBWSN-177 x Pirsabak-15 and PR107 x Shahkar-13 are favored to select based on best mean performance and desirable GCA and SCA effects and to utilize in future wheat breeding programs. The lines IBWSN-52 and IBWSN-131 and tester Shahkar-13 also performed well individually and in production of above promising F2 populations. Previous studies reported that grain yield was primarily affected and managed by several traits i.e., tillers per plant, spike length, spikelets per spike and 1000-grain weight, and grain yield could be indirectly enhanced by improving these yield attributing traits in wheat(Ingle et al., 2018).

Significant differences were observed among parental genotypes and their F1 hybrids, and the F1 populations showed increased grain yield than parental genotypes in wheat(Cifci and Yagdi, 2010; Jatav et al., 2014). Line by tester analyses revealed that F1 and F2 populations, and their parental lines and testers performed better for yield and yield contributing traits with desirable GCA and SCA effects(Fellahi et al., 2013; Istipliler et al., 2015). Past findings revealed that parental genotypes i.e., Mexicali-75 and Kunduru showed desirable GCA effects while the population Belfugitu x Alifian exhibited significant positive SCA effects for grain yield and spike traits in wheat(Gorjanovic and Balalic, 2004). Past study revealed that line BRF1 and tester ZM04 were found as best general combiners while F1 populations B4N11 x PS08 and BRF1 x ZM04 were identified as best specific combiners to be used in future breeding program to improve the grain yield in wheat(Saeed and Khalil, 2017).

According to gene action, the variances due to specific combining ability were found greater than general combining ability variances for all traits, suggesting the predominance of non-additive gene effects for maturity and yield traits. The values of dominance genetic variances were also greater than additive for all traits. Ratios of variances of general to specific combining ability and degree of dominance were less and more than unity, respectively which also authenticated non-additive gene effects. Such type of gene action needs that selection of superior populations in terms of maturity, morphological and yield related traits should be delayed to the later segregating generation to improve these traits in wheat. The ratio of GCA and SCA variances varies depending on the allele frequencies between parental lines and testers(Reif et al., 2007; Longin et al., 2013).

The parental genotypes selected from different gene pools had encouraging ratio because of their high GCA effects(Labate et al., 1997). Past findings revealed that variances due general and specific combining abilities and their ratio reported control of non-additive gene action for majority of the traits in wheat(Saeed and Khalil, 2017; Farooq et al., 2019). The ratios of GCA to SCA variances indicated that the inheritance was controlled by non-additive genes for earliness, maturity, morphological and yield attributing traits in wheat(Akbar et al., 2009; Ingle et al., 2018; Hama-Amin and Towfiq, 2019). Line by tester combining ability indicated that additive gene effects were less important than non-additive gene effects, and non-additive gene effects have greater role in the inheritance of all the traits in wheat(Nour et al., 2011; Parveen et al., 2019; Sharma et al., 2019).

In proportional contribution of populations to total variance, the L x T interactions/F2 populations have shown maximum share(as compared to lines and testers) for majority of the traits i.e., spike length, spikelets per spike, 1000-grain weight, biological yield per plant and grain yield per plant. However, for days to maturity the contribution of lines was commendable and leading as compare to testers and L x T interactions. Results revealed that line x tester interactions and lines brought much variation in the expression of all the studied traits. In earlier studies on line x tester combining ability, the contribution of line by tester interaction to total variance was found much greater than lines and testers individually in wheat(Istipliler et al., 2015).

Past studies also reported that line x tester contribution to total variance was leading as compare to lines and testers and managed the variances for majority of the maturity, morphological and yield associated traits in wheat(Akbar et al., 2009; Fellahi et al., 2013; Sattar et al., 2018).

Conclusion: Parental lines i.e., IBWSN-52 and IBWSN-131, testers(Shahkar-13 and Pakhtunkhwa-15), and their F2 populations viz., IBWSN-131 x Pakhtunkhwa-15, SRN-0911 x Shahkar-13, and IBWSN-177 x Pirsabak-15 were found as best general and specific combiners, respectively and performed better for maturity and yield traits. High x high, low x high and highxlow general combiners were involved in the production of F2 populations with promising SCA and best mean performance. The ratios of variances of GCA to SCA were smaller than unity while values of degree of dominance were greater than unity which confirmed that all the traits were controlled by non-additive gene effects. Non-additive gene action suggested that selection of populations in terms of maturity and yield related traits should be delayed to later segregating generations for further improvement in these traits of wheat. Therefore, these populations could be used in future breeding programs to develop early maturing and high yielding wheat genotypes.

Acknowledgements: Authors acknowledged with thanks the Director, and Wheat Breeding Section, Cereal Crops Research Institute(CCRI), Pirsabak - Nowshera, Khyber Pakhtunkhwa, Pakistan for providing the breeding material and land to carry out the present investigations.

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Author:K. Din, N.U. Khan, S. Gul, S.U. Khan, I. Tahir, Z. Bibi, S. Ali, S.A. Khan, N. Ali, I.A. Khalil and
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Date:Jul 23, 2020
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