Combining ability study

Combining ability study in chilli, pigeon pea, bottle guard and castor

1. Combining ability study in five Crops

2. Presented by : Chaudhary Ankit R. Submitted to : Dr. S.D. Solanki Department of Genetics & Plant Breeding C .P . College of Agriculture Sardarkrushinagar , Dantiwada Reg. No. : 04-AGRMA-01571-2017 Combining ability study in five Crops

3. INTRODUCTION “ Combining ability refers to the capacity or ability of a genotype to transmits superior performance to its crosses. ” MAIN FEATURES OF COMBINING ABILITY Combining ability analysis helps in evaluation of inbreeds. In terms of their genetic value and in selection of suitable parents for hybridization, also helps in identification of superior cross combinations which may be utilize for commercial exploitation of heterosis. For combining ability analysis, crosses have to be made either in diallel, partial diallel and line x tester fashion. It is useful tool in development of synthetic varieties. COMBINING ABILITY

4. Combining ability estimates of GCA and SCA effects are based on first order statistics (mean values). It provides information on gene action involved in the expression of various quantitative characters. Thus, heterosis in deciding the breeding procedure for genetic improvement of such traits. Combining ability analysis is equally applicable in self and cross – pollinated crops. There are two types of combining Ability : 1.General combining Ability ( GCA) 2. Specific combining Ability (SCA)

5. GENERAL COMBING ABILITY ( GCA ) “It is the average performance of a genotype in a series of cross combination is termed as GCA”. It is estimated from half – sib families. The crosses which have one parent in common are used for the calculation of GCA. For example, parent P are involved in 15 different crosses, the average performance of these 15 crosses will given an estimate of GCA for parent P. main features of GCA are : GCA variance is primarily a function of additive genes variance, but if epitasis is present GCA will also inactive additive x additive type non – allelic interaction. The GCA is estimated from half – sib families. GCA variance has + Ve correlation with narrow sense heritability. GCA help in selection of suitable parents for hybridization.

6. SPECIFIC COMBING ABILITY (SCA) “The performance of a parents in a specific cross known as SCA”. Thus, it is a deviation of a particular cross form the GCA. Fro example, parent P is involved 15 different crosses; it may not give good performance in all the crosses. The main features of SCA are : SCA variance is mainly a function of dominance variance, but if epistasis is present it would also include additive x additive, additive x dominance and dominance x dominance types non – allelic interactions. SCA is estimated from full – sib families. SCA variance has + Ve association with heterosis and SCA helps in identification of superior cross for commercial exploitation of heterosis.

7. Difference between GCA & SCA General Combining Ability (GCA) Specific Combining Ability (SCA) 1. It is average performance of a strain in a series of crosses. 1. It refers to the performance of specific cross in relation to GCA. 2. GCA is due to additive genetic variance and additive x additive epistasis . 2. SCA is due to dominance genetic variance and all the three types of epistasis . 3. It is estimated from half-sib families. 3. It is estimated from full-sib families 4. It helps in the selection of suitable parents for hybridization. 4. It helps in the identification of superior cross combinations 5. It has relationship with narrow sense heritability. 5. It has relationship with heterosis.

8. Estimation of combining Ability Half – sib and full – sib mating are required for estimation of combining ability. The important steps for its estimation are : 1 ) Selection of parents : Materials for evaluation of combining ability may include strains, varieties, inbreds and germplasm lines. 2 ) Making single crosses : Selected lines are crossed in a definite fashion to obtain single crosses through diallel , partial diallel and Line x Tester mating designs. 3 ) Evaluation of materials : Single crosses are evaluated along with parents in replicated trials and observations are recorded on different trials.

9. 4 ) Biometrical analysis : Three biometrical analysis techniques are commonly used for estimation of combining Ability : Diallel Analysis : According to this technique, total no. of single crosses among n parents would be equal to n (n – 1)/ 2 excluding reciprocals. If 10 parents 45 single crosses would be obtained for evaluation. If reciprocal are included, no. of crosses would be 90. This Technique analysis is done as per Griffing (1956). This design provides estimates of both GCA and SCA variances and its effects. In this design, limited no. of parents (10 – 12) can be evaluated at a time for combining ability.

10. Partial Diallel Analysis : This technique permits inclusion of more no. of parents ( upto 20) for evaluation than diallel design. In this, total no. of crosses to be made is equal to NS/2 where, N = no. of parents and S = no. of crosses. The S should be greater than or equal to N/2. Thus, both N and S can neither be odd nor even. If N is even, S should be odd and vice versa. Partial diallel provides estimates of GCA and SCA variance and GCA effects. However, estimates of SCA effects can not be estimated by this design the result are less reliable than obtained from diallel . But this design permits some biometrical treatments of data to reach sensible conclusion about choice of parents and crosses, which is not possible by simple inspection of large incomplete diallel data. The analysis of partial diallel is done according to the method suggested by Kempthorne and Curnow ( 1961 ).

11. Line x Tester Analysis: This is modified form of top cross. The top crosses are half – sib progenies, where, tester parents is common to all crosses. in case of line x tester cross, more than one tester is used with same set of inbreds . Tester parent may vary from random matting population such as inbreds . This design permits evaluation of large no. of parents at a time (50). In this, same genotypes are used as female and other as a male parents. Each male is cross to each female. Total no. of cross to be made is m x f where,m = no. of male parents and f = no. of female parents.

12. Griffing’s numerical approach :- To analyze the diallel cross data, so as to partition the total genotypic variance in to additive and non- additive components is outlined by fisher (1918, 1941). In this regards the concept of general and specific combining ability which was provide firstly Sprague and Tatum (1942) as a measure of gene action has become very important to plant and animal breeders. Griffing’s (1956) has given four methods of diallel cross depending upon the three sets of materials are involved viz. parents, F 1 and reciprocal.

13. In each method two steps are involved in the analysis of data (A) testing the significance of genotypic differences (B) combining ability analysis. (A) This step consist of analysis of data for testing the null hypothesis, that there are no genotypic differences among the F1 s , parents and reciprocal. Only when the significance differences among this are established there is need to proceed for second step of analysis, i.e. the combining ability analysis.

14. The experimental material develop according to any one of these four methods could be planted in any of suitable experimental design. Generally randomized block design is used and is applicable to these type of study. For testing the null hypothesis , following statistical model is to be carried out and it should be noted that it and if the mean square due to genotypes is significant, than only there is need to proceed for further analysis. Stastical model :- Y ijkl = µ + bk + T ij + (bt) ijk + e ijkl Where, Y ijkl = the i th observation on I x j th genotype in k th block. µ = general mean T ij = the effect of I x j th genotype. (bt) ijk = the interaction effects e ijkl = the error effects It is evident from this model that the total variability may be partitioned in to treatments, blocks , treatments x block errors.

15. ( B)Analysis of variance for combining ability Model-I, Model-II. Griffing’s (1956) describes two models, each with four methods as stated above or for GCA and SCA estimates showed the relationship for diallel crossing methods to Fishers (1918, 1930) methods of covariance between relatives as expressed in terms of additive and non-additive genetic variance. Models: 1. Model-I i.e. fixed effects model: Drawn / concluded information is applicable to the genotypes involve in diallel cross when genotypes are selected from small samples. 2. Model-II i.e. random effect model: Drawn / concluded information is applicable to the whole population, from which genotypes are randomly selected for diallel crosses.

16. Test of significance: The expectations of mean square and ‘F’ test differ with each model. 1. Model- I: Both GCA and SCA mean square are tested against error mean squares. 2. Model- II: In this model gca means squares is tested against the sca means squares and sca means squares tested against the error mean squares. In case of the SCA mean square is non-significant, the mean square due to sca & error are pooled together for testing the GCA mean square. The pooled sum of squares due to sca & error are divided by sum of degrees of freedom for sca & errors. Degree of freedom changed according to different methods.

17. Degrees of freedom of 4 diallel matings : Where, p = number of parents Generally in plant breeding , plant breeder choose homozygous genotypes/lines/ inbreds . Which do not have maternal effect. Or it is assumed that there is no reciprocal differences. So it need not to attempt reciprocal crosses and generally selected parents are a representative of a limited populations, it does not represent the whole population/ genotype of the crop. Hence model- 1 is most suited. Here fore, generally model-1 and method-2 is used , in this with ‘n’ lines , total entries to be tested are n (n+1) / 2.

18. The analysis of variance for combining ability is based on the following mathematical model; Y ijk = µ +g i + g j + s ij + 1/ bc e ijkl I, j = 1,2,3,,,,,,,n , k = 1,2,,,,,,,b , l = 1,2,,,,c Where, Y ij = mean performance of hybrid of i th & j th parent µ = population mean g i = gca effects of i th parent g j = gca effects of j th parent s ij = sca effects of I x j th cross e ijkl = random error effect associated with i th location in k th replicate for I x j genotype i.e. mean error effect.

19. For combining ability analysis the replicated the data in different treatments are to be arranged in a half matrix table after arranging i.e. in this table each value is the mean value of all the replications of a particular treatments. ability in method- II Calculation of combining ability variances: SS ( gca ) = 1/p+2 [ ∑(Yi + Yij ) 2 – 4/p Y 2 ] SS ( sca )= ∑Yij 2 -1/P+2 [∑(Yi + Yij ) 2 ] + 2/(p+1)(p+2) Y.. 2

20. ANOVA for combining Me’= error M.S of RBD/ replication.

21. Estimation of combining ability effects: General and specific combining ability effects calculated as under 1 2 gi = ------- ∑ ( Yi • + Yii ) – ------ Y.. (p+2) P 1 2 sij = Yij - ------ Yi • + Yii + Y•j + Yjj + ---------------- Y•• (p+2) (p+1) (p+2) Where, all the notations areas stated earlier. Yj = refers to the array total of jth array. Yij = stands for mean value of jth parent.    

22. Worked example: Model -II and Model-I Eight parents crossed in 8 x 8 half- diallel fashion so that n (n+1) /2, = 8(8+1)/2 = 36 progeny families comprising of 28 crosses and 8 parental genotypes were obtained. The 36 progeny families were grown in RBD with 3 replication Observations were recorded on grain yield/plant in durum wheat crop given in the below table. Table : Grain yield per plant (gm) 8x8 fashion Sr.no. Grain yield/plant P1 X P1 1 14.00 P1 X P2 2 13.83 P1 X P3 3 14.06 P1 X P4 4 16.28 P1 X P5 5 17.17 P1 X P6 6 9.29 P1 X P7 7 16.38 P1 X P8 8 9.16 P2 X P2 9 8.13 P2 X P3 10 17.68 P2 X P4 11 11.27 P2 X P5 12 11.32 P2 X P6 13 9.45 P2 XP7 14 10.42 P2 X P8 15 9.51

23. 8x8 fashion Sr.no. Grain yield/plant P3 X P3 16 10.43 P3 X P4 17 10.73 P3 X P5 18 16.82 P3 X P6 19 7.57 P3 X P7 20 15.51 P3 X P8 21 15.90 P4 X P4 22 16.93 P4 X P5 23 12.87 P4 X P6 24 12.14 P4 X P7 25 16.53 P4 X P8 26 11.81 P5 X P5 27 8.06 P5 X P6 28 13.50 P5 X P7 29 16.57 P5 X P8 30 22.26 P6 X P6 31 12.43 P6 X P7 32 15.13 P6 X P8 33 21.62 P7 X P7 34 11.60 P7 X P8 35 17.52 P8 X P8 36 12.20 ANOVA table for grain yield per plant

24. Two way table for grain yield / plant

25. Y ij 2

26. SS due to GCA = 1/p+2 [Σ (Yi.+ Yii ) 2 - 4/p Y..2] = 1/10 [(124.17) 2 +…+ (132.18) 2 - 4/8 (486.08) 2 ] = 80.09 SS due to SCA = Σ Σ Yij 2 -1/p+2 Σ ((Yi.+ Yii ) 2 + 2/p +1) (p+2) Y.. 2 ] = (14.00) 2 +(13.83) 2 +…(12.20) 2 -1/10 [(124.17) 2 +…+(132.18) 2 +2/9 x 10 (486.08) 2 = 390.3 SS due to error = 34.89 Analysis variance for combining ability D.F S.S M.S Cal.F GCA 7 80.09 11.44 22.971 SCA 28 390.3 13.94 27.992 ERROR 70 34.89 0.498

27. Genetic components Model-I Σ g 2 i =Mg-M’e/(p+2) = 11.44- 0.498/10 = 1.09 Σ s 2 ij =Ms-M’e = 13.94-0.498 = 13.442 The ratio of Σ g 2 i / Σ s 2 ij =1.09/13.442= 0.08109 Estimation of Genetic effects gi = 1/p+2 [(Yi.+Yii) - 2/p Y..] = 1/10[(124.17)-2/8 (486.08)] = -0.265 (Parent-1) P1 P2 P3 P4 P5 P6 P7 P8 GCA effect 0.265 -2.178** -0.239 0.397 0.511* -0.796** 1** 1.092**

28. Significance of gca effects SEgi =√(p-1) M’e /p(p+2) = √ 7 x 0.498/8x10 = 0.21 Calculated t= gi/SEgi = -0.265/0.21 = |1.26 | = 1.26 Table t for 0.05@ 70 d.f =1.98 and for 0.01 =2.62

29. Estimation of Genetic effects (2) SCA effects of hybrids Sij = Yij -1/p+2 ( Yi.+ Yii + Yj .+ Yjj ) + 2/(p +1) (p+2) Y..] = P1x P2 -1/p+2 ( Y1+Y11 +Y2+Y22) + 2/(p +1) (p+2) Y..] = 13.83-1/10 (124.17+ 99.74) +2/90 (486.45) = 2.25(P1 xP2) =0.54(P1xP3) =2.124(P1XP4)…..

30. SCA effects of hybrids P1 P2 P3 P4 P5 P6 P7 P8 P1 2.25** 0.54 2.124** 2.9** 3.673** 1.647** -5.645** P2 6.603** -0.443 -0.507 -1.07 -1.87** -2.872** P3 -2.922** 3.054** -4.889** 1.281* 1.579** P4 -1.532** -0.955 1.665** -3.14** P5 0.291 1.591** 7.189** P6 1.458* 7.856** P7 1.986** P8

31. Significance of SCA effects SEsij =√p(p-1) M’e /(p+1) (p+2) = √ 56 x 0.498/9x10 = 0.56 Calculated t for P1 X P2= sij / SEsij = 2.25/0.56= |4.02 | = 4.02 Table t for 0.05@ 70 d.f =1.98 and for 0.01 =2.62 P1XP3= 0.96 P1XP4=3.79 P1XP5= 5.18….

32. conclusion of SCA effects for Grain Yield /plant Total nu. Of parents Good parent 1 Average & non significant parent 5 Poor parent 2 Conclusion of GCA effects for grain yield / Plant significant @ 1% @ 5% ve + 12 1 ve - 8 1 Total 20 2 Non significant - 6

33. Worked example: Model-II and Model-I Seven parents crossed in half-diallel fashion so that n (n+1) /2, = 7(7+1)/2 = 28 progeny families comprising of 21 crosses and 7 parental genotypes were obtained. The 28 progeny families were grown in RBD with two replications. Observations were recorded on number of capsules on main raceme in castor crop given in the below table. Table : Grain yield per plant (gm)

34. ANOVA table for number of capsules on main raceme : Diallel table for number of capsules on main raceme

35. ANOVA for combining ability analysis Model-2 , Model-1

36. Table: gca and sca effects ( Model -2, Model-1) for number of capsules on main raceme. Diagonal entries are gca effects and off diagonal entries are sca effects. *, ** significant at 5 & 1 % levels respectively.

37. The analysis of variance showed significant differences due to treatments for all the characters. This indicates presence of sufficient amount of variation for all the traits and selection will be effective to improve them. The analysis of variance for combining ability (Table 1) indicated that mean square due to GCA and SCA were highly significant for all the traits. This indicated variation in parents and crosses and significant combination of additive and non additive effects in the expression of the characters.

38. Estimates of GCA effects for lines and testers in Pigeon pea

39. The parent PRG-100 was found to be a good general combiner for all the characters except days to 50% flowering and plant height while LRG-30 was good general combiner for number of primary branches per plant, number of pod clusters per plant, number of pods per plant and seed yield per plant. The lines ICPL 85034 and LRG 38 were identified as best general combiners for earliness. Among the testers ICP 8863 was good general combiner for days to 50% flowering, days to maturity; number of pods per plant, 100- seed weight and seed yield per plant and ICP 84036 and ICP 89044 were good general combiners for earliness. The tester ICP 87119 was good general combiner for number of pod clusters per plant, number of pods per plant, 100- seed weight and seed yield per plant but not for earliness

40. Estimates of SCA effects in pigeon pea

41. The crosses viz ., PRG 100 x ICP 8863,PRG 100 x ICP 87119, LRG 30 x ICP 8863, LRG 30 x ICP 87119 and ICPL 85063 x ICP 87119 exhibited significant sca effects for seed yield perplant . It was observed that, these crosses also exhibited significant sca effects for number of pods per plant and 100- seed weight. The cross combinations of high gca lines x high gca testers manifested in to higher sca combinations except in the cross ICPL 85063 x ICP 87119 which shows low x high combination based gca and it was identified as best specific combiner for seed yield.

42. Estimates of GCA of chilli parents

43. SCA effect of crosses for all the characters in chilli

44. ANOVA for combining ability In respect to plant height, the mean sum of squares due to gca , sca and reciprocal were found highly significant. P6 (12.95) recorded the highest significant positive gca effect and P2 (-8.69) recorded the highest significant negative gca effect. The sca effect was positive and maximum in P5 x P6 and P6 x P5. Among 30 hybrids, 17 exhibited significant sca effects towards positive direction.

45. A positive correlation between host resistance and disease incidence and an in-depth knowledge in the relationship of disease incidence and biochemical components will be useful to carry out breeding for resistant varieties to overcome the menace posed by pathogen in host plants to develop superior hybrids from parental screening. Conclusion

46. Thank You