GENETIC DIVERSITY AND HETEROSIS IN WHEAT S ...

SABRAO Journal Journal SABRAO of of Breeding Breeding and and Genetics Genetics 42 96–105, 2010 2010 42 (2) (2) 95–104,

GENETIC DIVERSITY AND HETEROSIS IN WHEAT S. SUD1, N. S. BAINS2, G. S. NANDA2, K. SINGH2 and LALIT ARYA3 SUMMARY This study was conducted to find the relationship between heterosis and genetic diversity in wheat by using molecular, morphological and pedigree data. Twenty elite parental lines were crossed in all possible combinations. The F1 hybrids and parents were evaluated for agronomic performance in replicated yield trial. The parental lines were assayed for DNA polymorphism using 60 wheat microsatellite primers and 12 AFLP (EcoR1: Mse1) primers. Pedigree information at expansion level of 5 was collected and co-efficient of parentage (COP) values were calculated. Eighteen germplasm descriptors were used to estimate morphological diversity. The range of genetic diversity was highest for morphological (0.22 to 0.83) followed by pedigree (0.47 to 1.00), simple sequence repeat markers (0.09 to 0.53) and amplified fragment polymorphic markers (0.13 to 0.46). Genetic diversity estimates using SSRs (GDSSRs) was positively correlated (r = 0.283) with pedigree-based diversity. The correlation among other diversity measures was found to be low and non significant. GD SSRs and GD COPs were the only diversity measure to show association with yield heterosis as well as heterosis for yield components (thousand grain weight). Significant moderate correlations were also observed between GDAFLP and plant height and GDMOR with grain yield per spike.

Key words: Heterosis, genetic diversity, pedigree based diversity, COP, AFLPs, SSR markers Interest in hybrid wheat as a productivity enhancing option has followed an uneven trajectory. Early interest, following the discovery of cytoplasmic male sterility (CMS) system by Wilson and Ross (1962) was overshadowed by the advent of semi-dwarf wheats as a successful productivity enhancing strategy. Over the last couple of decades interest in hybrid wheat has persisted although at a low level. Presently, we are in the process of a fresh exploration of the hybrid option. This initiative is prompted by recent developments like availability of wheat specific chemical hybridizing agents (CHAs), availability of new germplasm (e.g. spring wheat x winter wheat derivatives now dominate the spring wheat growing regions) and success of hybrid rice (with a similar reproductive system) in China. A hybrid wheat development programme initiated in 1999 is presently operational in the major wheat growing zones of India involving four public sector institutes. As a

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Bhabha Atomic Research Centre, Nuclear Agriculture & Biotech. Division, Mumbai-400085, India Department of Plant Breeding, Genetics and Biotechnology, Punjab Agricultural University, Ludhiana, India 3 National Research Centre for DNA fingerprinting, National Bureau of Plant Genetic Resources, ICAR, New Delhi, India * Corresponding author: [email protected], [email protected]. 2

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parallel development a private sector company (Mahyco) has released wheat hybrids, which now occupy 40,000 hectares in India. The objective of public sector initiatives is to develop hybrid seed production systems based on both CHAs and CMS approaches and fresh assessment of heterosis. As a part of this programme more than 1000 hybrid combinations developed through manual, CMS and CHA systems have been evaluated across years at Punjab Agricultural University, Ludhiana, India. About 150 parental lines representing elite germplasm in terms of overall agronomic attributes were involved in hybrid development. Twenty parental lines from this set were chosen for the present study on the basis of their ability to give heterotic hybrids in relatively greater number of combinations. The twenty lines were involved in all possible crosses and the generated hybrids along with parents were evaluated for yield, yield components and other attributes. The parents were assayed for molecular (simple sequence repeats (SSRs) and amplified fragment length polymorphism (AFLPs)), morphological markers and coefficient of parentage (COP) based diversity indices. The question that the study sought to address was: Can we find index of genetic diversity, which has relevance for manifestation of heterosis? This question was pertinent for hybrid wheat programme. It also addressed a basic issue on which empirical information in wheat continued to be scanty. MATERIALS AND METHODS Parental lines and crosses Twenty bread wheat cultivars (Table 1) were used as parents to produce the F1 hybrids. These parents were crossed in all possible combinations excluding reciprocals. The F1 seed was produced by manually emasculating the female spikes and then dusted with anthesising male ears three times at 2 day interval. The 173 F1 hybrids along with parental lines were planted in randomized block design with three replications. The entries in yield trials were sown in 1 metre long pair rows using half of the commercial seed rate (25 seeds/row). The distance between rows within plots was 23 cm, and the distance between rows among plots was 50 cm. Other agronomic practices were based on commercial recommended package of practices. Table 1. Set of parental lines used in the study Name of PBW 343, PBW 442, PBW 445 PBW 459 PBW 474, PBW 493,HD Varieties/ 2687, CPAN4231, CATBIRD, CHILERO, LUAN, INQUILAB 91, Advanced SASIA,TIA.2/KAUZ, MUNIA//CHEN/ALTAR, DACULA breeding lines /CHAGUAL//CAZO, KAUZ*2/MNV//KAUZ, TJB368.251/BUC//TURACO, F12.71/COC// CNO79/3/KAUZ , F6.74/BUN//S15/3/VEE#7 Field data recording The data on agronomic traits including: (i) grain yield per plot; (ii) grain number per spike; (iii) grain yield per spike; (iv) thousand grain weight (v) tillers per metre row length; (vi) plant height; (vii) days to heading and (viii) spikelets per spike were recorded on parental lines and hybrid plots from all replications. Mean of hybrids and parental lines over replications were calculated for each trait and used in the analysis.

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Molecular marker assays Genomic DNA was extracted from a bulk of five individual plants from each genotype according to method of Saghai Maroof et al. (1984). For SSR analysis, SSR primers used in this study were developed by Roder et al. (1998) and Gupta et al. (2000). A set of 60 primer pairs mapping throughout the genome was selected. Polymerase chain reaction (PCR) was performed as described by Roder et al. (1998). For AFLP analysis, AFLP analysis was performed using PE Applied Biosystems fluorescent fragment detection Kit for regular plant genomes (500-6000 Mb). AFLP fragments were analyzed using GENESCANTM v.2.02 analysis software (PE Applied Biosystems) as described in the user’s manuals. Morphological Marker Based Diversity Morphological characters (qualitative traits) of the parental lines were recorded using a set of germplasm descriptors: auricle pigment, auricle pubscence, flag leaf angle, ligule pigment, ligule length, angle of ear, colour of ear, shape of ear, ear density, peduncle shape, awn colour, Shoulder shape, beak length, beak curvature, glume pubescence, keel inflection, yellow rust score, and brown rust score. These morphological traits characters serve as markers and classes within each marker taken as alleles for calculation of GDMOR. Pedigree Based Analysis Pedigree of twenty parental cultivars was obtained from cultivars descriptions (Zeven and Zeven-Hissink, 1976; Martynov et al., 1992) or from personal communications with the breeders. The pedigree trees of each of these twenty cultivars were generated using external pedigree input tool of International Crop Information System (ICIS) Software (Mclaren and White, 1999). The coefficient of parentage (COP) values was then estimated using WCOP function of IWIS software version 2.0. Statistical Analysis Analysis of variance for testing genotypic differences was performed for yield and other agronomic traits. Mid parent heterotic effects for yield and its components was computed by equation MH = (F1- MP)/MP, where MP represents the mid-parental value. The presence or absence of each single variant was coded by 0 or 1, respectively for morphological and molecular marker data and scored for a binary data matrix. Similarity values for molecular and morphological (Nei and Li, 1979) were calculated for each pair of lines and UPGMA-clustering was done using software package NTSYSpc.2.02e. Polymorphism information content (PIC) was calculated as described by Anderson et al. (1993). n PIC = 1- ∑ (Pi)2 i=1 Where Pi is the frequency of ith allele. The genetic diversity estimates for molecular markers (1-GSSSR and 1-GSAFLP), morphological markers (1-GSMOR) and pedigree (1-GSCOP) is calculated by subtracting genetic similarity values from unity. Pearson‘s correlation coefficients among different genetic diversity estimates with each other and with heterosis were calculated using Excel ver.7.0 (Microsoft. Inc., Redmond.WA).

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RESULTS Evaluation of hybrids and parents Analysis of variance for yield, yield components and other traits recorded on the trial of hybrids and parents showed significant genetic differences for all the traits (Table 2). A wide range of mid parent heterosis was observed for yield and yield components. Mid parent heterosis for yield ranged from –28.0% to 43.01%. Large significant differences in the performance of set of parents and hybrids indicated the suitability of the material for further investigations involving diversity indices. Diversity indices The diversity indices employed were based on molecular markers (SSRs, AFLPs), morphological markers and coefficient of parentage. Initially SSRs were chosen for molecular marker based analysis. Out of sixty primers used only 25 turned out to be polymorphic for the set of 20 genotypes (Table 3). Moreover the number of alleles per marker was also relatively low, mostly 3or 4 with a maximum of 6 in two cases. A relatively low-resolution system based on agarose gels might have contributed to the low polymorphism observed in case of SSRs. This problem was addressed by using AFLPs. Using seven primer pairs 1156 marker loci could be generated. Out of these 1103 markers showed polymorphism i.e., at least one of genotype showing distinct presence/absence of bands at a particular locus. Primer wise information on polymorphism is given in Table 4. A comparison of average diversity and range of diversity in the set of parental combinations however showed them to be similar (Table 5). A set of 18 germplasm descriptors when used for diversity analysis showed parental combinations to vary from 0.22 to 0.83 in their diversity. Using morphological measures this range was wider than the molecular based indices. The average diversity (0.50) was also higher. Pedigree based diversity gave a much higher average value of (0.88) and wide range (0.47-1.00). A normal and overlapping frequency distribution was found for GDSSR, GDAFLP and GDMOR (Figure 1). Pedigree-based diversity data however was skewed toward greater diversity and showed a range, which was distinct from the other indices. Association among diversity indices and with heterosis Correlation in all possible combinations among the diversity indices showed a single significant value between GDSSRs and GDCOPs (Table 5). GDSSRs and GDCOPs were the only diversity measure to show association with yield heterosis as well as heterosis for yield components (thousand grain weight) (Table 6). Significant correlations were also observed between GDAFLP and plant height and GDMOR with gain yield per spike. All these correlations however were not very strong.

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DISCUSSION Germplasm used for this study emanated from an actual breeding programme. An insight into the genetic basis of heterosis was a requirement of this programme and the study was pursued to identify and test a system for analyzing diversity patterns which is relevant for predicting levels of heterosis. A heterosis relevant diversity index would have allowed to move into a larger set of germplasm and split it up into inter-heterotic parental pools. Initially CHA (CH9832, Mahajan et al., 2001) was employed for obtaining hybrid seed. Acute genotypic specificity in the response of parental lines to CHA thwarted seed production in large number of combinations (Sud et al., 2001). Subsequently, manual crossing was used so that a more complete set of crosses could be obtained though with fewer seeds resulting in smaller plots for hybrid evaluation. Manual crosses could be managed for the 20 parental lines and experience in precise testing in small plots existed. In wheat only three studies have simultaneously explored diversity and heterosis (Barbosa – Neto et al., 1996; Martin et al., 1995; Corbellini et al., 2000). Two of these studies were available to us at the start of this experiment. Martin et al. (1995) used a small number of closely related lines emerging from a single breeding programme, whereas Barbosa –Neto et al. (1996) did not employ a defined set of hybrids such as a diallel set though the number of parents and hybrids was large. Thus the present study also sought to contribute to this line of work at a basic level. Table 2. Mean sum of squares and mid-parent heterosis (Range) for yield and associated traits S.No. 1 2 3 4 5 6 7 8

Characters Yield / plot Tillers per meter Spikelets per spike Grain yield per spike Grain number per spike Thousand grain weight Plant height Days to heading

Mean Sum of Squares 6249.06 375.25 5.59 4.23 167.16 51.68 63.54 43.38

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Percent Mid- Parent heterosis (Range) -28.0-43.01 -41.50-38.44 -19.13-31.82 -29.25-13.09 -45.16-56.72 -30.86-33.74 -21.64-24.50 -5.33-7.10

Table 3. Polymorphic information content (PIC) values for each of the twenty-five microsatellite primers used for assaying diversity in wheat genotypes. Sr. No. Primer Chromosome Number of alleles PIC value location in twenty genotypes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Xgwm 33 Xgwm 67 Xgwm 99 Xgwm 108 Xgwm 112 Xgwm 114 Xgwm 136 Xgwm 169 Xgwm 219 Xgwm 249 Xgwm 257 Xgwm 259 Xgwm 261 Xgwm 264 Xgwm 273 Xgwm 296 Xgwm 410 Xgwm 448 Xgwm 499 Xgwm 512 Xgwm 533 Xgwm 639 Xgwm 642 Xgwm 681 Xgwm 746 Average

1A/1B/1D 5B 1A 3B 3B/7B 3B/3D 1A 6A 6B 2A/2D 2B 1B 2D 1B/3B 1B 2D 2B/5A 2A 5B 2A 3B 5A/5B/5D 1D A A

4 3 3 3 4 4 3 4 3 3 4 2 5 5 2 6 3 5 4 2 4 3 3 4 6

0.55 0.61 0.54 0.58 0.71 0.56 0.66 0.67 0.50 0.60 0.66 0.18 0.69 0.69 0.17 0.76 0.71 0.77 0.65 0.42 0.41 0.54 0.58 0.57 0.67 0.58

Table 4. Number of polymorphic vs total AFLP markers using seven primer combinations among wheat genotypes. Primer combination Polymorphic Markers AFLP Markers (Total) M+CTT/E+AGG M+CAC/E+ACA M+CAC/E+AGG M+CAG/E+ACC M+CTT/E+ACA M+CAG/E+ACA M+CAG/E+AGG

212 152 113 80 202 193 204

212 148 113 78 160 193 199

Total

1156

1103

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Table 5. Average diversity and percentage dissimilarity (range) among all 190 pair wise combinations and correlation coefficients among different diversity measures. GDMOR GDCOP Diversity Average Percent GDSSR measures diversity dissimilarity (Range) GDAFLP 0.27 0.13-0.46 0.115 -0.097 0.030 GDSSR 0.30 0.09-0.53 0.092 0.283* 0.50 0.22-0.83 0.058 GDMOR 0.88 0.47-1.00 GDCOP *Significant at 5 per cent level Table 6. Correlation coefficients of mid parent heterosis for yield and yield components and estimates of genetic distances of parents based on coefficient of parentage (COP), AFLPs, SSRs and morphological markers. Mid parent heterosis Yield / plot

GDAFLP GDSSR GDMOR

Grain no. Grain yield / spike / spike

1000 grain wt

Tillers/ metre

Days to heading

Spikelets per spike

Plant height

0.074

0.100

0.038

0.144

0.092

0.037

-0.014

0.202*

0.231*

0.057

0.087

0.248*

-0.018

-0.149

0.066

-0.007

0.062

-0.130

0.187*

0.048

-0.150

-0.047

0.052

0.107

0.089 -0.047 GDCOP *Significant at 5% level

-0.047

0.166*

-0.061

0.070

-0.022

0.004

GDAFLP GDSSR GDMOR GDPED

Number of observations

100 80 60 40 20 0

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0

Genetic Distance

Figure 1. Frequency distribution of genetic distance values calculated for 190 pair wise combinations of 20 cultivars based on AFLP, SSR, COP and morphological markers

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The inability of the present study to arrive at strong correlations among diversity indices as well as with heterosis may be due to either inappropriate choice of parents or analysis lack the enough data. Pedigree based approach can not provide much data, morphological approach is very limited and the molecular approach has employed a relatively small number of markers. In this study initial COP analysis had indicated sufficient diversity in the set. Though these lines had passed through the dwarfing step, a diversity curbing influence because of small number of initial dwarf donors, diversifying influences had come in later. Many of the lines (PBW343, PBW445, PBW493, CPAN4231, HD2687, Chilero, Luan, Sasia, Tia.2/Kauz, Munia/Chen//Altar, F12.71/COC//CNO79 /3/KAUZ, F6.74/BUN//S15/3/VEE#7, Kauz*2/MNV//Kauz) received diversifying input from winter wheat whereas others have traditional Indian wheats in their parentage (PBW442 and PBW474). CPAN4231 and Catbird have ancestors of Chinese origin. Others like Sasia, Munia/Chen//Altar, and Dacula/Chagual//Cazo carried durum wheats in the parentage. Moreover the set of 20 lines used had consistently manifested heterosis when used as parents in a wider set of crosses. Thus the choice of materials seems appropriate though a drawback exists in absence of lines deliberately chosen to serve as diversity benchmarks or reference materials. Winter wheats or synthetic hexaploids could have served this purpose in the context of the current set of spring wheats. With respect to diversity indices the problem of poor genome coverage and low allelic resolution in case SSRs were sought to be tackled with AFLPs and a high-resolution system. Morphological and COP based indices were employed to arrive at a holistic overview of genetic diversity. Results from the present study on heterosis and its relationship with genetic diversity are in accordance with previous studies. For instance: Barbosa-Neto et al. (1996) reported non-significant correlation (0.33) of RFLP based genetic distance estimates with COP and no correlation with heterosis in 722 hybrids tested across multiple locations for four years. Martin et al. (1995) found poor correlation of genetic distance estimates with grain yield, SCA effects and heterosis. Another study by Corbellini et al. (2002) reported low positive correlation of MPH effects for grain yield with RFLP distance (0.23) and with COP based genetic distance (0.170) in wheat. Poor correlations of GDSSRs with GDAFLP (0.115) and with GDMOR (0.092) in this study indicated that the methods of measuring genetic diversity may be inherently different for different indices. The poor association between the two molecular marker based distance measures (AFLPs and SSRs) may be attributed to the fact that the two systems scan the genome in different ways. Diversity in SSR profile is based on differences in repeat number in microsatellite whereas diversity in AFLP patterns across genotypes depends upon presence/absence of specific restriction sites. AFLPs in general show low correlation with all the diversity measures. The enzymes used in AFLP analysis methylation insensitive. Therefore, most of the amplified fragments are not likely to have originated from the coding regions of genome. This might have contributed to its low correlation with morphological and pedigree based indices of diversity. There is only one other study in wheat (Manifesto et al., 2001) where both AFLPs and SSRs have been studied in context of genetic diversity. It indicated low but statistically significant correlation of 0.27 between AFLP and SSR marker based diversity indices in Argentine based wheat germplasm. Diversity estimates based on methylation sensitive enzyme combination (Pst1:Mse1) have shown to be differ from methylation insensitive (EcoR1:Mse1) enzyme combination (Barret and Kidwell, 1999). The correlation of GDCOP with other two distance measures i.e. GDAFLP and GDMOR was found to be 0.030 and 0.058. One of the reasons for the low correlation may be fundamental difference between two

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approaches. Coefficient of parentage measures genetic similarity on the basis of number of parents common in the ancestry. These COP estimates are based on certain assumptions (Cox et al., 1985; Messmer et al., 1993), which may not be fulfilled in real situations. The assumption that a genotype receives half of its genes from each parent does not take into account the fact that during selection the breeder modifies this theoretical situation so that frequency of the alleles of one of parent in the selected progeny may be greater than that of the other. Moreover, it should be borne in mind that the collection of pedigrees is a very tedious task and that some cultivars lack complete pedigree information. Finally unintentional mistakes or the decision of breeder not to disclose the identity of the parents used may be further source of uncertainty concerning the pedigree. COP values were shown to have a highly negative correlation with SSRs (r = -0.75) and AFLPs (r = -0.79) when genetic diversity estimates by Manifesto et al. (2001). Poor association of pedigree based and AFLP based diversity in 43 wheat lines was also reported by Barrett et al. (1998). If we analyze the relation of diversity with heterosis across species as maize, the correlations were found to be low in most of studies where inbreds were taken from same population (Melchinger et al., 1990; Boopenmaier et al., 1993). However, correlations were reported to be improved in crosses among lines originated from well-demarcated pools (Smith et al., 1990; Lanza et al., 1997). In our study we found a weak correlation, which indicated less diverse base of parental lines used for finding these associations. This takes us to a larger question: Is wheat as a crop inherently unsuitable for this diversity based approach to heterosis? Further, Can the use of deliberately diversified gene pools change this situation? At present, with the absence of such gene pools in any given set of working germplasm, the search for heterosis has to trudge an empirical path. ACKNOWLEDGEMENTS The research was conducted as a part of NATP Project entitled as “Basic and Strategic Research to commercialize hybrid wheat” funded by Indian Council of Agricultural Research, New Delhi. Research facilities for AFLP analysis provided by NBPGR is great fully acknowledged. REFERENCES Andreson, J.A., G.A. Churchill, J.E. Autrique, S.D. Tanksley, and M.E. Sorrells.1993. Optimizing parental selection for genetic linkage maps. Genome 36:181-186. Barbosa-Neto, J.F., M.E. Sorrells, and G. Cisar, 1996. Prediction of heterosis in wheat using coefficient of parentage and RFLP-based estimates of genetic relationship. Genome. 39:1142-1149. Barrett, B.A., and K.K. Kidwell, 1998. AFLP based genetic diversity assessment among wheat cultivars from Pacific Northwest. Crop Sci. 38:1261-1271. Barrett, B.A., K.K. Kidwell, and P. N. Fox, 1998. Comparison of AFLP and pedigree based genetic assessment methods using wheat cultivars from the Pacific Northwest. Crop Sci. 38: 1271-1278. Boppenmair, J., A.E. Melchinger, G. Seitz, H.H. Geiger, and R.G. Herrmann, 1993. Genetic diversity for RFLP in European maize inbreds II. Performance of crosses with in versus between heterotic groups for grain traits. Plant Breeding. 111: 217-226.

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