Establishing the A. E. Watkins landrace cultivar collection as a resource for systematic gene discovery in bread wheat

Transcription

1 Theor Appl Genet (2014) 127: DOI /s Original Paper Establishing the A. E. Watkins landrace cultivar collection as a resource for systematic gene discovery in bread wheat Luzie U. Wingen Simon Orford Richard Goram Michelle Leverington Waite Lorelei Bilham Theofania S. Patsiou Mike Ambrose Jo Dicks Simon Griffiths Received: 10 October 2013 / Accepted: 5 June 2014 / Published online: 2 July 2014 The Author(s) This article is published with open access at Springerlink.com Abstract Key message A high level of genetic diversity was found in the A. E. Watkins bread wheat landrace collection. Genotypic information was used to determine the population structure and to develop germplasm resources. Abstract In the 1930s A. E. Watkins acquired landrace cultivars of bread wheat (Triticum aestivum L.) from official channels of the board of Trade in London, many of which originated from local markets in 32 countries. The geographic distribution of the 826 landrace cultivars of the current collection, here called the Watkins collection, covers many Asian and European countries and some from Africa. The cultivars were genotyped with 41 microsatellite markers in order to investigate the genetic diversity and population structure of the collection. A high level of genetic diversity was found, higher than in a collection of modern European winter bread wheat varieties from 1945 to Furthermore, although weak, the population structure of the Watkins collection reveals nine ancestral geographical groupings. An exchange of genetic material between ancestral groups before commercial wheat-breeding started would be a possible explanation for this. The increased knowledge Electronic supplementary material The online version of this article (doi: /s ) contains supplementary material, which is available to authorized users. Communicated by Andreas Graner. L. U. Wingen (*) S. Orford R. Goram M. Leverington Waite L. Bilham T. S. Patsiou M. Ambrose J. Dicks S. Griffiths John Innes Centre, Norwich Research Park, Norwich, UK luzie.wingen@jic.ac.uk regarding the diversity of the Watkins collection was used to develop resources for wheat research and breeding, one of them a core set, which captures the majority of the genetic diversity detected. The understanding of genetic diversity and population structure together with the availability of breeding resources should help to accelerate the detection of new alleles in the Watkins collection. Introduction Hexaploid bread or common wheat (Triticum aestivum L.) is an important staple crop with over 600 million tonnes being harvested annually. Wheat was originally domesticated about 10,000 years ago in the fertile crescent (see Shewry 2009 for a review). The wheat genome has derived from hybridisation of the domesticated tetraploid progenitor emmer (Triticum dicoccoides), the donor of the A and B genome, with the wild diploid species Aegilops tauschii, the donor of the D genome (Salamini et al. 2002). From its origin of domestication, which is located in today s southeastern part of Turkey, the crop was spread by the human population and cultivated in many parts of the world. It came to Europe via a route to Anatolia, then to Greece. From there, one way proceeded northward through the Balkans to the Danube. A second route went across to Italy, France and Spain, finally reaching UK and Scandinavia. In a similar way, wheat spread via Iran to central Asia, reaching China, and via Egypt into Africa. It was introduced by Spaniards to Mexico in 1529 and to Australia in 1788 (Feldmann 2001). Domestication has reportedly introduced population bottlenecks leading to a lower genetic diversity in crop plants

2 1832 Theor Appl Genet (2014) 127: in comparison to wild ancestors (Doebley et al. 2006). Following the introduction of domesticated wheat, varieties became adapted to local conditions becoming the socalled landrace cultivars (LCs). In this process, the genetic variation was further reduced by genetic drift and selection (Reif et al. 2005). However, the process is unlikely to have happened under complete isolation, rather exchange of breeding material between neighbours and by more distant trade will have occurred. Subsequently, genetic diversity of modern elite cultivars (MC) may have even become lower, if breeding was based on a narrowing germplasm base (Tanksley 1997). A recurrent theme in this research is the possibility that more diversity may have been left behind in LCs during the green revolution when breeding strategies hypothetically focused on a few target genes only (Khush 2001). However, this assertion is not generally true. A narrowing or enrichment of the germplasm diversity depends on the particular breeding programme applied. Among CIMMYT wheat varieties in the period from 1949 to 1989 a decrease in genetic diversity was found, contrary to an increase in the period from 1990 to 1997 (Reif et al. 2005). Different trends for the genetic diversity of modern wheat lines in the USA (increasing), Australia (constant) and the UK (decreasing) between 1940 and 2005 have been reported (White et al. 2008). Similarly, genetic diversity trends could be linked to wheat breeding practices in European countries (Roussel et al. 2005), where MCs from western countries show a lower number of alleles. Furthermore, it has been shown that the narrowing of the germplasm base in bread wheat can be averted through the introgression of novel materials, e.g. coming from LCs (Smale et al. 2002; Reif et al. 2005). A. E. Watkins, from the School of Agriculture in Cambridge, was interested in Vavilov s work on the origins of crop plants and plant domestication (Watkins 1933; Vavilov 1931). He thus acquired bread wheat and macaroni wheat (Triticum durum Desf.) LCs from markets predominantly in Asia and Europe, but also from other parts of the world using connections with the London Board of Trade. In this way, he established a considerable wheat collection of over 7,000 accessions (Miller et al. 2001). Unfortunately many were lost during the Second World War when the material was put into storage. The collection today consists of a current viable bread wheat LC collection of 826 accessions, here called the Watkins collection. The Watkins collection captures a snap shot of genetic diversity present before the start of modern breeding. This is compared to the diversity present in MCs adapted to Northern European climate. The EU Commission Key Action 5 project Genetic Diversity in Agriculture: Temporal Flux, Sustainable Productivity and Food Security (Gediflux) was established to investigate the impact of intensive breeding over time on the genetic diversity of different European crops, one of them winter bread wheat (Reeves et al. 2004). A panel of over 500 wheat MCs from across Europe, here called the Gediflux collection, which had been sown in major acreages in the years , has been used for this study. The panel was genotyped using 42 microsatellite (single sequence repeat, SSR) markers and the genetic diversity was assessed (Reeves et al. 2004). In general, no significant change in genetic diversity was detected over time for any of the crops studied, including the winter bread wheat panel. The threat of climate change and the growing human population paired with the observed lower rates of genetic gain calls for improved methods to obtain a sustainable increase of crop yields. Increasing crop diversity by exploiting the diversity of LCs is one strategy to approach this goal. Useful and currently rare alleles, introgressed into elite wheat lines, may help to improve grain yield or to adapt the plants to new climate conditions. Studies on the genetic diversity of bread wheat LC collections (Huang et al. 2002; Balfourier et al. 2007; Horvath et al. 2009) reveal a high level of genetic diversity and suggest a rich source of alleles not used in modern breeding. The Watkins collection has been successfully used to find new alleles or genes for leaf rust resistance (Dyck 1994), stripe rust resistance (Bansal et al. 2011), and root-lesion nematode resistance (Thompson and Seymour 2011). However, all of these are Mendelian traits, and the determination of the chromosomal locations for such traits is comparatively easy. A better genetic understanding of LC collections is necessary in order to dissect the architecture of complex traits. In order to improve allele mining in the Watkins collection it was genotyped with a set of 41 SSR markers, which was partly overlapping with the set used for the Gediflux collection. The current study reports on the results of phenotyping and genotyping, the diversity of the Watkins collection and its genetic population structure. Comparisons to other bread wheat collections, particularly the Gediflux collection, are presented. Furthermore, the development of breeding resources, particularly of a core set of LCs, is reported. Materials and methods Plant material, growing conditions, and phenotyping Seeds for the Watkins collection were received from the John Innes Centre Germplasm Resource Unit (GRU Single seed descendents (SSDs) were developed over 6 generations for all 826 LCs. Initially, four seeds were sown for each LC. In 234 cases, phenotypes from the same accession showed striking differences in the first generation and, thus, two SSD streams were produced from those accessions, here called

3 Theor Appl Genet (2014) 127: sister lines. All field trials were grown at Church Farm Bawburgh, Norwich, UK (52.63 N, 1.18 E), under standard growing conditions, if not stated differently. Trials with replicates were planted in a randomised block design The majority of SSDs, developed from original accessions of the Watkins collection, were planted in 2006 in four replicates of 1 m 1 m field plots without nitrogen fertiliser usage. The following traits were measured: mature plant height, ear emergence, grain yield, lodging, vernalisation requirement, presence of awns, thousand grain weight, grain length, grain width and grain surface area. The Watkins core set (119 LCs) and the Gediflux collection were grown in 2011 in field plots of 1.5 m 4 m (single plots) and 1 m 1m (three replicates), respectively, and assessed for mature plant height (Gediflux only), peduncle and internode lengths (Gediflux only), ear emergence, grain yield, thousand grain weight, grain length, grain width and grain surface area. A trait mean was calculated for the varieties of the core set for each trait and both years, 2006 and 2011, with exception of the Watkins 2011 data, where only single plots were measured. For comparison of phenotypic observations between those years, the 2006 data set values were adjusted to the 2011 level. Bi-parental SSD populations were developed for several LC accessions. For this the elite wheat cultivar Paragon was crossed with selected Watkins LCs followed by four rounds of self pollination. The selection criterion for the LC parent was the display of a phenotype within the extreme borders of the phenotype range. Bi-parental populations were grown in 2011 for multiplication purposes in single 1 m 1 m field plots and assessed for plant height and flowering times. Genotyping Genomic DNA was extracted from 3-week-old seedlings using the DNeasy 96 Plant Kit and protocol for fresh plant tissue (Qiagen). The genotyping of the Watkins collection was conducted using 41 publicly available SSR markers. Public primer sets were used from JIC (psp), IPK Gatersleben (gwm/gdm), Wheat Microsatellite Consortium (wmc), Beltsville Agricultural Research Station (barc) and INRA (cfd/cfa) collections, and can be found on the GrainGenes website ( Targeted markers were selected to fall on different chromosomes according to a published consensus map (Somers et al. 2004). Initial marker tests on a limited number of varieties helped to identify markers that exhibit scorable, multiple alleles. These markers were given preference. See Table 1 for the names of the markers. There were 14 common markers between the SSR marker set used here and the markers employed on the Gediflux collection. Gene-based assays were conducted: the presence of the recessive vernalisation requirement alleles were tested with 1833 allele specific assays for Vrn-A1 [three assays (Yan et al. 2004)], Vrn-B1 and Vrn-D1 [two assays each (Fu et al. 2005)]; the presence of the recessive photoperiod sensitivity alleles were tested with allele specific assays for Ppd- A1c.1 [one assay (Wilhelm et al. 2008)], Ppd-B1a.1-3 [three assays (Díaz et al. 2012)] and Ppd-D1.a1 and Ppd- D1.c2 [two assays (Beales et al. 2007)]. Forward primers were labelled with the dyes FAM, VIC, NED, or PET (Applied Biosystems) according to Schuelke (2000). PCR mixes were in 6.25 µl volumes that consisted of µl HotstarTaq Master Mix (Qiagen), 0.75 µm of each primer, and 12.5 ng gdna. The PCR profile consisted of 15 min at 94 C, followed by 35 cycles of [95 C for 1 min, a primer pair-dependent annealing temperature according to the GrainGenes website for 1 min, and 72 C for 1 min], and concluded with 72 C for 10 min. Products were measured on an ABI 3730 DNA Analyzer with a POP- 7(TM) polymer column. Peak data were analysed using the manufacturers GeneMapper (version 4.0) software. Reactions that did not show an amplified product were repeated. A NULL allele was scored, if: (a) the repeat did not result in a PCR product; (b) the DNA quality was good, as could be seen from the scores of other markers; and (c) the number of missing amplifications for that marker was lower than 5 %. Otherwise, the data point was scored as missing. Of the 41 SSR markers, 39 markers had a good score with less than 5% missing values after one round of regenotyping of missing calls. Some of the markers detected more than one locus. If a clear separation between loci could be made, the marker was scored multiple times, otherwise only scores for the most consistent locus were taken. This resulted in genotypic information for 45 loci (see Table 1). The number of missing data per locus was on average 4.2, including eight loci with no missing data but with NULL alleles, and three loci with more than 20 % missing data. The latter loci were excluded from the advanced statistical analyses. Of the final marker set 14 markers were shared with the Gediflux collection. Statistical analysis Diversity statistics The genetic diversity of a collection of cultivars was calculated using R software (vs. 3.02) (R Core Team 2013) for different common diversity indices (compare Table S1 for equations). The Shannon Weaver Diversity Index on phenotype scores (d SWIp ) was calculated when traits were measured in all three trials. For this, the overall phenotype range was divided into 12 phenotype classes of similar size. For each trial, the frequencies of scores in each class were determined. d SWIp was calculated similar to d SWI (see Table S1) from these frequencies.

4 1834 Theor Appl Genet (2014) 127: Table 1 Summary of genotyping outcome and diversity statistics of the Watkins collection for 41 SSR markers binding to 45 loci in the bread wheat genome and six gene-based markers. Equations for diversity indices are given in Table S1. Mean and range are given over SSR marker loci only Marker name chr Missing d AR d RAR dˆr(g) d Nei d PIC d SWI barc019 3A barc021 7A barc029 7A barc032 5B barc096 6D barc097 7BD5B barc107 6A barc110 5D barc127 6B barc134 6B barc164 3B barc172 7BD5B barc240 1ABD5B cfd079.a 3ABD cfd079.b 3ABD gdm111 1D gdm129 4D gwm003* 3D gwm018* 1B gwm030.a 2D3A gwm030.b 2D3A gwm046* 7B gwm095* 2A gwm155* 1D3A gwm190* 5D gwm213* 5B gwm219* 6B gwm291* 5A gwm312* 2A gwm337 1D gwm357* 1A gwm437* 7D gwm456* 3D gwm526.a 2B gwm526.b 2B gwm539 2D gwm570* 6A gwm608.a 2D4D6B gwm608.c 2D4D6B psp3100 1B wmc093 1AD wmc105 6B wmc110 5A wmc154 2B wmc168 7A mean (SSRs) min (SSRs) max (SSRs)

5 Theor Appl Genet (2014) 127: Table 1 continued Ppd-A1 2A Ppd-B1 2B Ppd-D1 2D Vrn-A1 5A Vrn-B1 5B Vrn-D1 5D Markers shared with Gediflux collection are indicated by a * after the marker name chr putative chromosomal locations according to Gramene database ( d AR allele richness, d RAR number of rare alleles, dˆr(g) allele richness after rarefaction, d Nei Nei s gene diversity, d PIC polymorphic information content, d SWI Shannon Weaver Diversity Index Population structure To investigate the population structure of the Watkins and the Gediflux collections, the Bayesian model-based clustering method implemented in the programme STRUCTURE (Pritchard et al. 2000) was used. The full dataset from 1,054 lines was used, including all sister lines where present (234 cases). Settings for STRUCTURE were: admixture, burn-in period of 10,000 and runs of 50,000 steps. Runs for numbers of founder populations between two and 25 were performed with 10 repetitions each. The number of ancestral clusters was determined by the δk statistic (Evanno et al. 2005) using R software and package corrsieve (vs ). Core set A reduced set of LCs was initially selected from the diverse phenotypes. The diversity of the core was determined using the CoreHunter software (Thachuk et al. 2009) for different diversity indices: Cavalli-Sforza and Edwards Distance, Modified Rogers Distance, Number of Effective Alleles Index, and Shannon Weaver Diversity Index (d SWI ). For the final selection of the core set of accessions the d SWI was used, as the highest value of the genetic diversity averaged over the four diversity indices was achieved for this index. Results Phenotypic diversity The phenotypic diversity of the Watkins collection has been assessed in field trials for the majority of LCs (726 accessions). Scores for the traits adult plant height, heading date, and four grain characteristics were taken (Table S2). A wide range of phenotype scores were found for the traits scored, as indicated by high diversity values (Shannon Weaver phenotype diversity scores (d SWIp ) between 1.37 and 2.03). Watkins LCs with extreme phenotypes for any of the above traits were selected as parents for the development of bi-parental populations (Table S2). The collection was also scored for vernalisation requirement: 86% of the lines showed spring growth habit, and only 14% winter growth habit. In general the phenotypic variation observed in the Watkins collection was larger than that observed in a collection of European MCs, the Gediflux collection as indicated by d SWIp values between 0.97 and 1.74 for the same traits as measured for the Watkins collection (see Fig. 1 and compare Tables S2 and S3). The two collections displayed different trends for most of the traits. In the Gediflux collection the window of flowering times was smaller than in the Watkins collection. Similarly, in the Gediflux collection the mature plant height was lower, whereas the average thousand grain weight was high. As an exception in the traits observed, grain length values did not show a very different distribution between the two collections. In contrast, grain surface area and grain width were both higher in the Gediflux collection. Genetic diversity The genotypic scores on 45 loci reveal an average allele number per locus is 22.4, ranging from 3 to 61 alleles. The allele numbers for individual markers are listed in Table 1. The average d Nei is 0.78, ranging from 0.22 to 0.96, the average d PIC is 0.75, ranging from 0.21 to 0.96, and the average d SWI is 2.09, ranging from 0.49 to The diversity ranking of the markers is in general similar for the diversity indices tested, with markers gwm539 and gwm213 being the most diverse. This indicates that all the diversity indices used were able to identify the genetic diversity present. A comparison between SSD lines stemming from the same original LC accession but which showed phenotypical heterogeneity (234 cases or 28% of accessions) was conducted. This showed that on average 15.7 (35.7 %) of the markers had different allele sets between sister lines (SD

6 1836 Theor Appl Genet (2014) 127: Fig. 1 Outlines of density functions created from phenotypic values for the following bread wheat collections or sets: Watkins 2006 (red, hashed); Gediflux 2011 (blue); Watkins core 2006 and 2011 (red, dotted and dot hashed, respectively); Watkins data fitted to 2011 conditions (orange). a Days to ear emergence [days after May 1st], b plant mature height [cm], c thousand grain weight [g], d grain length [cm], e grain surface area [cm 2 ], f: grain width [cm]. Abbreviations: W Watkins, G Gediflux, 2006 and 2011 years collections were grown (colour figure online) A C B D E F 10.6 markers, range 1 39 markers). This gives some indication of the heterogeneity of the original LC accessions. Using the genotype data generated by the Gediflux project (Reeves et al. 2004), we were able to compare the diversity of the LC collection to a modern wheat collection. In the Gediflux collection, the average d PIC value is just 0.57 (see Table S4), much lower than that found in the Watkins collection (0.75). Similar observations can be made for Nei s gene diversity (d Nei =0.62 vs. 0.78) and the Shannon Weaver Diversity Index (d SWI =1.30 vs. 2.09). Detailed results can be found in Table S4. In order to understand the origin of the diversity better, the diversity of the three different wheat genomes were determined separately, by only using markers specific for single genomes. Average diversity values for A, B and D genome markers for the Watkins collection were found as d SWI : 1.79, 2.30 and 1.93 and d PIC : 0.68, 0.81 and 0.71, respectively. Values for the Gediflux collection are d SWI :

7 Theor Appl Genet (2014) 127: , 1.47 and 1.07 and d PIC : 0.54, 0.68, 0.56, for the three different genomes, respectively. In both cases this suggests that the B genome is more diverse than the two other genomes. However, this analysis is based on 11, 13 and 11 markers, respectively, for the A, B and D genome in the Watkins collection only, and on 11, 14, and 10 markers, respectively, in the Gediflux collection. A simple bias due to analysing different marker numbers from each genome was excluded by calculating the mean diversity index from eight markers randomly selected from each genome, which resulted in near identical values (Watkins: d SWI : 1.72, 2.25 and 1.95 and d PIC : 0.67, 0.81 and 0.72 Gediflux: d SWI : 1.16, 1.43 and 1.07 and d PIC : 0.51, 0.62 and 0.50, respectively). Population structure of the Watkins collection An analysis to determine the population structure of the Watkins collection by Bayesian model-based clustering (Pritchard et al. 2000) was undertaken, using the SSR genotype data. The number of ancestral groups was determined by δk statistics (Evanno et al. 2005). This analysis indicates a split of the collection into two ancestral groups or subpopulation (Fig. S1B). 85% of sister lines fell into the same subpopulation. A total of 424 of the accessions show more characteristics of group 1 and 630 of group 2. Although more Asian LCs are found in group 1 and more European LCs are in group 2, on the whole the groups are composed of accessions from different geographic regions. A further analysis was conducted on the next hierarchical level. This analysis addressed the structure within each of the two subpopulations and revealed that the smaller group was most likely formed from four ancestral subpopulations. The structure of the larger group is more obscure, but five being the most likely number of ancestral subpopulations (Fig. S1C and D, respectively). These groupings were aligned with the geographic region from which the LCs were collected, as shown in Fig. 2. Geographic origins could then be assigned from this alignment as follows: a Russian (group 1.1), a Chinese/Indian (group 1.2), a Central/ East Asian (group 1.3), and a mixed European/Asian (group 1.4) group form the 424 LCs subpopulation. The 630 LCs strong subpopulation appear to comprise a South European/Asian (group 2.1), a Northwest European (group 2.2), an East European (group 2.3), a South Mediterranean/ African (group 2.4), and a North Mediterranean (group 2.5) group. Population structure of the Gediflux collection The determination of the population structure of the Gediflux collection by Bayesian model-based clustering, similar to the analysis performed for the Watkins collection, revealed a main subdivision of the population into two 1837 clusters (Fig. 3, top panel, and Fig. S2). These ancestral subpopulations were mainly supported by accessions either coming from the EU recommended list or from the UK national list, respectively. The analysis did also hold some support for the presence of 15 ancestral populations. (Fig. S2 B). The differences between these groups seem to be a combination of the decade of breeding and the geographic region, UK or EU. A further hierarchical analysis was not undertaken. Core set A core set of LCs from the Watkins collection was chosen to preserve the majority of the genetic diversity while reducing the numbers of LCs necessary to conduct trials. The selected core set contains 119 LCs and preserves 98 or 96% of the total genetic diversity, as detected by diversity measurements for the employed markers, d Nei or d SWI, respectively. However, the number of alleles, particularly of rare alleles, is strongly reduced in the core set as can be seen from the D AR and D RAR values in Table 1 and S6. This could mean that the Core Set is not a suitable tool to identify very rare alleles. A detailed list of the accessions included in the core set can be found in Table S5 and the genetic diversity levels are summarised in Table S6. Discussion The present study reports on the phenotypic and genotypic diversity of the Watkins bread wheat LCs collection of 826 accessions. Regarding the former, a scoring of phenotypic values for basic traits was conducted. The phenotypic variation observed in the Watkins collection was larger than that observed in a collection of European MCs, the Gediflux collection (compare Table S2 and S3 and Fig. 1). For most of the traits a clear trend between the two collections was observed. These trends will be the result of modern breeding strategies, which were employed in the development of MCs. Partly, trends will also reflect the differences in geographic distribution. The Gediflux collection of winter wheat was adapted for a narrow geographic region, Northwestern Europe, in comparison to the Watkins collection, which encompasses a near-global scale. Traits with a low genetic variability are not expected to show a trend. Trends were observed for plant height, flowering time and several grain characteristics, but not for grain length. A low genetic variability for this trait must be assumed. The window of flowering times was narrower in the Gediflux collection, and the mature plant height was reduced. These characteristics make the plants more adapted to modern farming under European growing conditions. In contrast, the average thousand grain weight, grain surface area and

8 1838 Theor Appl Genet (2014) 127: Fig. 2 a The world map. Countries from which LCs were acquired are coloured. Colours are organised in geographic regions. b STRUCTURE assignment of the Watkins LCs to ancestral populations. Three panels shown. Top panel whole collection; middle and lower panel the 424 and 630 subpopulations of the whole collection, respectively. Each panel is divided into three rows. Top row assignment to ancestral population; middle row ancestral characteristics of each line; bottom row colour code of country/ region of origin. Abbreviated names of the LCs are given below the bottom row. c Colour code of the countries, according to geographic regions A B Afghanistan Australia Bulgaria Brazil Canary Islands China Crete Cyprus Algeria Egypt Spain Ethiopia Finland France UK Greece Hungary India Iran Iraq Italy Morocco Yugoslavia Myanmar Israel Poland Portugal Romania Syria Tunisia Turkey USSR AE Watkins: Countries of Origin Regions 193.1FRA 313.1MMR 313.2MMR 312.1IRN 164.1IND 77.1YUG 48.1ESP 49.1ESP 165.1IND 161.1ESP 158.2GRC 311.2IRN 50.1ESP 194.1FRA 312.2IRN 13.2BGR 13.1BGR 165.3IND 144.1ESP 159.1BGR 158.1GRC 315.1CHN 84.4ESP 162.1ESP 84.1ESP 311.1IRN 107.1FRA 74.2YUG 603.1ESP 307.1IRN 307.3IRN 735.1DZA 69.1ESP 134.1AUS 553.2CAI 105.2FRA 197.1IND 200.1FRA 110.4FRA 41.4FRA 76.3YUG 76.1YUG 97.1POL 149.1GBR 101.1ITA 110.1FRA 150.1GBR 336.1HUN 196.4IND 82.1IND 190.2FRA 191.1FRA 231.3HUN 190.1FRA 732.1IND 42.1FRA 150.4GBR 40.1FRA 756.2ITA 197.4IND 736.3SUN 736.2SUN 40.3FRA 716.1IND 725.4CHN 231.2HUN 480.1POL 166.4IND 189.2FRA 724.1IND 757.4ITA 92.2IND 92.1IND 195.1IND 799.2SUN 210.1IND 468.1AFG 189.1FRA 207.4IRN 52.1ESP 804.1SUN 95.1CHN 188.4FRA 799.4SUN 357.3YUG 96.3ROU 75.1YUG 52.3ESP 608.2YUG 39.1ITA 134.3AUS 460.1AFG 75.4YUG 100.1POL 670.4POL 355.1YUG 171.1CHN 104.1ITA 51.1ESP 181.1POL 181.2POL 472.1AFG 96.1ROU 38.1POL 35.1POL 37.3POL 36.4POL 36.2POL 37.1POL 331.4BRA 329.3EGY 331.2BRA 330.1EGY 634.1HUN 636.2TUN 330.4EGY 329.2EGY 637.1TUR 800.1SUN 801.3SUN 750.3SUN 750.1SUN 27.1AUS 751.2SUN 800.3SUN 801.2SUN 751.1SUN 744.1IND 636.3TUN 766.1DZA 744.4IND 15.2YUG 15.4YUG 337.2HUN 309.2IRN 337.1HUN 309.1IRN 98.1POL 796.2SUN 450.3ROU 447.4CHN 672.1POL 779.1SUN 451.1ROU SUN 765.1SUN 343.2ESP 348.1BGR 26.1AUS SUN 156.4YUG 338.1HUN 345.1BGR 481.1POL 911.1HUN 345.3BGR 622.2BGR 346.1BGR 113.1ESP 519.1IND 622.1BGR IND 143.4ESP 912.3HUN 912.4HUN 372.2YUG 519.3IND 367.3BGR 365.1YUG 534.1MAR 71.1BGR 635.1HUN 249.1IND 310.1IRN 359.1YUG 355.2YUG 450.4ROU 662.1ROU 737.1ITA 364.1YUG 315.2CHN 484.1ITA 310.2IRN 46.3CRE 642.3IND 757.1ITA 356.1YUG 157.1YUG 457.1AFG 620.4YUG 620.1YUG 470.1AFG 79.2IND 753.2SUN 590.2PRT 72.1YUG 666.4AFG 78.1YUG 374.2IRN 186.1ITA 530.1AFG 90.1AUS 63.4ESP 665.2AFG 712.4IND 590.1PRT 182.1POL 753.1SUN 72.9YUG 22.1AUS 167.1IND 822.2CHN 638.1CRE 663.1ROU 91.2IND 67.1ESP 821.1CHN 664.1AFG 125.1IND 169.1CHN 132.1AUS 21.1IND 85.1ESP 18.1IND 63.2ESP 123.1AUS 20.1IND 73.1BGR 122.1AUS 81.1IND 722.1CHN 89.1AUS 19.1IND 28.1AUS 723.1AFG 184.1POL 206.1IND 21.2IND 206.3IND 203.1IND 204.3IND 205.1IND 204.4IND 252.1DZA 251.1AUS 280.1ESP 218.1TUN 258.3MAR 459.1AFG 326.1AUS 25.1AUS 259.1MAR 260.1CAI 217.1MAR 257.2TUN 8.1PRT 431.1IND 245.1AUS 257.3TUN 5.1AUS 477.1PRT 202.1ESP 444.3CHN 262.1CAI 201.1ESP 444.4CHN 222.1CRE 442.1CHN 247.1IND 261.1CAI 496.1MAR 145.1ESP 495.1MAR 328.4EGY 286.1GRC 328.1EGY 453.1AFG 388.1ESP 9.4PRT 216.1MAR 118.1PRT 119.1IND 9.3PRT 119.2IND 454.1AFG 172.2IND 498.1TUR 55.1ESP 432.1IND 387.1ESP 7.1AUS 80.1IND 305.1EGY 221.1ESP 742.1DZA 10.2IND 256.2TUN 147.1IRQ Com1ESP 224.4CHN 136.1AUS 263.1CAI 223.1MMR 143.1ESP 546.1ESP 510.1PRT 389.1PRT 741.1AFG 29.1AUS 497.1MAR ITA 141.2CHN 253.1MAR 387.2ESP 293.1TUR 455.2AFG 422.1IND 534.2MAR 295.1TUR 33.1IND Com4TUR 682.1CRE 224.3CHN 23.1AUS 686.1MAR 256.1TUN 66.2ESP 288.1CRE 169.4CHN 250.1CHN 687.1TUN 458.4AFG 425.1IND 69.2ESP 174.1IND 491.2FRA 491.1FRA 146.1ESP 228.1ESP 219.1ESP 174.4IND 220.1ESP 6.3AUS 10.1IND 170.1CHN 2.1CRE 494.1ESP 6.4AUS 514.2IRN 173.1AUS 401.1PRT 618.2YUG 133.1AUS 623.3BGR 53.1ESP 1.2MAR 70.1ESP 176.1AFG 443.1CHN 54.1ESP 535.3TUN 667.1AFG 233.1IND 120.1AUS 121.1AUS 24.1AUS 553.4CAI 283.1GRC 66.1ESP 782.3SUN 630.1IRN 374.3IRN 762.2DZA 520.1IND 183.1POL 609.1YUG 452.2AFG 467.1AFG 46.1CRE 178.3YUG 351.1YUG 138.2AUS 754.1SUN 213.1MAR 105.1FRA 368.1YUG 277.1ESP 178.4YUG 123.2AUS 128.2IND 154.1PRT 780.4SUN 264.1CAI 156.1YUG 447.3CHN 102.1ITA 17.4ESP 258.1MAR 130.1ESP 17.2ESP 43.1TUN 131.1ESP 544.1ESP 272.1ESP 550.2ESP 139.1FRA 57.4PRT 16.1ESP 691.1GRC 111.1MAR 187.1ITA 598.1PRT 533.2MAR 140.1TUR 693.1IRQ 417.1IND 276.1ESP 266.1ESP 103.1ITA 533.1MAR 115.3YUG 265.1CAI 391.1PRT 599.1PRT 275.1ESP 555.1ESP 542.1ESP 215.1MAR 44.3MAR 607.1YUG 45.1SYR 229.1PRT 60.2PRT 44.1MAR 270.1ESP 483.1POL 412.3IND 340.1ESP 685.1ESP 115.1YUG 411.1IND 274.1ESP 462.2AFG 192.1FRA 412.1IND 339.1PRT 690.1GRC 179.1GRC 369.2YUG 904.1ESP 214.1MAR 548.1ESP 180.1ESP 612.1YUG 225.1CAI 395.1PRT 617.1YUG 390.1PRT 551.1ESP 640.1IRN 571.1TUR 624.3BGR 414.1IND 601.1ESP 549.1ESP 541.3ESP 373.1IRN 163.1ESP 624.4BGR 597.1PRT 464.1AFG 592.1PRT 230.1PRT 244.2IND 370.1YUG 211.3FRA 635.4HUN 384.1GRC 239.1ESP 677.2ESP 621.1BGR 641.1IND 62.1ESP 58.1PRT 349.1BGR 558.1CAI 301.1TUR 198.3IND 255.1TUN 254.1MAR 396.1PRT 116.1ESP 676.3TUN 289.1CRE 383.1GRC 600.1ESP 211.4FRA 529.1AFG 688.1ESP 131.2ESP 515.1IRN 268.1ESP 679.2IRN 581.1IRN 397.1PRT 106.1FRA 683.1ESP 185.1ITA 284.1GRC 117.1ESP 538.1TUN 681.1IRN 541.4ESP 676.4TUN 492.1ESP 531.1ESP 644.4IND 463.1AFG 396.4PRT 112.2TUR 240.3IRN 226.1ESP 602.4ESP 248.2IND 232.1IND 527.2IND 543.2ESP 616.1YUG 347.1BGR 616.2YUG 743.1SUN 398.1PLE 712.2IND 385.2ESP 494.3ESP 294.1TUR 545.1ESP 269.1ESP 445.1CHN 151.1PRT 639.1CRE 179.4GRC 94.1IND 527.3IND 361.1YUG 372.1YUG 167.2IND 602.2ESP 285.1GRC 567.1GRC 65.1ESP 138.1AUS 540.1ESP 455.3AFG 99.1POL 380.1IRN 545.4ESP 287.1CRE 68.1ESP 762.4DZA 292.1CYP 58.2PRT 525.1IND 756.1ITA 539.1ESP 488.3SUN 578.1IRN 358.1YUG 614.3YUG 366.1YUG 341.1ESP 11.2IND 354.1YUG 670.1POL 619.1YUG 537.1TUN 287.2CRE 614.1YUG 507.1AUS 717.1CHN 552.2CAI 437.3CHN 562.1GRC 482.1POL 11.1IND 652.1CHN 160.2ESP 770.1SUN 490.1FRA 291.1CYP 338.3HUN 471.1AFG 654.4CHN 93.1IND 436.1CHN 175.2CHN 560.1GRC 212.1MAR 436.2CHN 148.1AUS 108.1FRA 669.4CHN 586.2CHN 47.1ESP 175.3CHN 317.1CHN 83.1ESP 437.1CHN 654.3CHN 129.2IND 608.1YUG 438.4CHN 142.1MAR 759.4SUN 651.1CHN 652.2CHN 109.1FRA 64.1ESP 563.1CRE 60.1PRT 61.1ESP 227.3ESP 438.3CHN 759.2SUN 129.1IND IND 775.3SUN 789.2SUN 775.2SUN 780.2SUN 783.1SUN 789.3SUN SUN 605.1GRC 475.1AFG 769.4DZA 779.4SUN 605.2GRC 792.1SUN 476.2AFG 786.1SUN 671.1SUN 671.2SUN 720.4CHN 241.2IND 308.1IRN 376.2IRN 747.1ETH 748.1SUN 749.2SUN 749.1SUN 379.1IRN 787.1SUN 793.4SUN 798.1SUN 746.1SUN 808.4SUN 797.1SUN 761.1SUN 760.1SUN 787.3SUN 807.1SUN 793.3SUN 576.1IRN 808.2SUN 788.1SUN 375.1IRN 828.1CHN 829.3CHN 812.1CHN 659.1CHN 827.1CHN 647.1CHN 776.1SUN 819.3CHN 819.1CHN 777.1FIN 820.1CHN 825.3CHN 825.4CHN 660.1CHN 645.2IND 813.1IND 655.1CHN 701.1IRN 715.1IND 824.1CHN 771.1SUN 316.1CHN 715.3IND 784.1ITA 823.1CHN 446.1CHN 818.2CHN 826.1CHN 629.1IRN 657.1CHN 658.1CHN 645.1IND 440.4CHN 656.1CHN 818.3CHN 39.3ITA 583.1CHN 811.1TUN 653.1CHN 448.1ROU 649.1CHN 694.2IND 646.1IND 694.3IND 440.1CHN 813.3IND 828.4CHN 829.4CHN 734.3SUN 734.1SUN 316.4CHN 643.1IND 128.1IND 627.1IRN 400.1CHN 449.1ROU 399.1CHN 816.1ITA 803.1IND 804.4SUN 523.3IND 320.3CHN 684.1IRN 168.1CHN 580.1IRN 522.1IND 720.1CHN 802.1SUN 593.1PRT 615.4YUG 565.1GRC 406.1IND 406.3IND 271.1ESP 615.2YUG 674.1IRQ 568.4CHN 569.1GRC 589.1CHN 506.1IRN 577.2IRN 282.1GRC 278.2ESP 299.1TUR 588.1CHN 564.3GRC 631.1IRN 499.1TUR 300.1TUR 564.1GRC 505.1IRN 625.1IRN 626.1IRN 719.1CHN 279.1ESP 721.1CHN 419.1IND SUN 177.1PRT 570.1GRC 273.1ESP 435.1CHN 673.1SUN 568.1CHN 585.2CHN 497.4MAR 246.1IND 378.4IRN IND 704.1IRN 407.2IND 773.2SUN 703.1CHN 526.3IND 322.1CHN 596.4PRT 502.3MMR 584.1CHN 290.1CRE 718.1CHN 586.4CHN 596.3PRT 711.1IND 420.1IND 814.1TUN 767.1SUN 504.1CHN 378.1IRN 153.1PRT 526.2IND 582.1CHN 648.1CHN 318.1CHN 407.1IND 428.2IND 509.1PRT 644.1IND 434.1CHN 381.1IND 441.1CHN 321.1CHN 209.1EGY 314.1CHN 591.1PRT 243.2IND 413.1IND 198.4IND 587.1CHN 413.3IND 769.2DZA 679.1IRN 702.1IRN 426.1IND 415.1IND 317.2CHN 678.1IRN 633.2IND 594.3PRT 474.1AFG 707.2IND 473.1AFG IND 377.1IRN 697.1IND 240.2IRN 574.1TUR 238.1IRN 297.1TUR 675.1TUN 774.2ETH 244.3IND 774.4ETH 628.3IRN 56.1ESP 628.2IRN 675.3TUN 242.3IND 404.2IRN 403.4ESP 487.1SUN 633.3IND 296.1TUR 376.3IRN 327.4PLE 632.2IRN 469.1AFG 242.1IND IRN POL 606.1GRC 234.3YUG 235.4GRC 234.4YUG 402.1GRC 235.3GRC 613.1YUG 237.4IRN 208.1IND 236.1GRC 208.2IND 137.1AUS 604.2ESP 745.1SUN 424.4IND 237.2IRN 680.1ITA 457.2AFG 730.4IND 124.1IND 572.1TUR 207.2IRN 708.1IND 488.1SUN 733.1IRN 155.4PRT 572.2TUR 429.1IND 126.1IND 155.1PRT 731.1IND 424.1IND 409.1IND 14.1YUG 14.2YUG 523.1IND 485.2DZA 353.1YUG 325.1GBR 794.3SUN 324.1CHN 399.2CHN 729.1IRN 323.1CHN 521.2IND 353.3YUG 352.1YUG 794.1SUN 32.1IND 34.1IND 512.1IND 903.1IND 713.1IND 730.1IND 421.1IND 696.1IND 732.2IND 610.1YUG 522.4IND 319.1CHN 528.1CHN 817.1CHN 166.3IND 403.1ESP 772.1SUN 579.1IRN 772.4SUN 405.1IRN 641.2IND 456.1AFG 642.2IND 594.2PRT 706.1IND 503.1MMR 728.2IRQ 486.1SUN 500.1IRQ 501.1IRQ 243.4IND 502.2MMR 817.2CHN 727.1CHN 298.1TUR 433.1IND 199.1IND 30.1AUS 595.1PRT 342.2ESP 752.1SUN 227.4ESP 278.3ESP 738.1SUN 410.1IND 87.1IND 739.1IND 358.3YUG 725.1CHN 532.1TUN 692.1IND 371.2YUG 561.1CRE 135.1AUS 363.3YUG 127.1IND 700.1CHN 511.1IND 363.1YUG 466.3AFG 86.2IND 536.2MAR 524.2IND 423.1IND 714.1IND 728.1IRQ 695.1CHN 461.1AFG 357.2YUG 516.1IND 392.1PRT 710.1IND 584.2CHN 668.1YUG 248.1IND 393.1PRT 114.2YUG 524.3IND 458.2AFG 493.1TUN 410.2IND 689.1ESP 394.1PRT 573.1TUR 547.1ESP 423.2IND 559.1ESP 661.1SUN 503.2MMR 763.1IND 557.1CAI 513.1IRN 507.3AUS 344.1ESP 765.4SUN 30.3AUS 362.1YUG 360.1YUG 556.4CAI 566.1GRC 556.1CAI 764.1IND 489.1SUN 650.2CHN 430.1IND 382.2IND 465.1AFG 327.1PLE 478.1AFG 408.2IND 577.1IRN 319.2CHN 79.1IND 386.1ESP 59.1PRT 466.4AFG 408.4IND 4.1IRQ 795.1SUN 795.2SUN 416.1IND 465.4AFG 755.1SUN 88.1POL 611.1YUG 806.1ITA 152.1PRT 439.1CHN 382.1IND 418.1IND 810.3SUN 810.1SUN 726.1CHN 805.1SUN 650.3CHN 809.1SUN 281.1GRC 316.4CHN 627.1IRN 505.1IRN 632.1IRN 632.2IRN 810.1SUN 707.2IND 316.1CHN 810.3SUN 769.4DZA 805.1SUN 242.1IND 828.1CHN 809.1SUN 812.1CHN 485.2DZA 731.1IND CHN IND 501.1IRQ 506.1IRN 319.1CHN 568.1CHN 829.3CHN 827.1CHN 823.1CHN 629.1IRN 658.1CHN 671.1SUN 671.2SUN 824.1CHN 694.3IND 589.1CHN 702.1IRN 657.1CHN 829.4CHN 828.4CHN 449.1ROU 813.1IND 588.1CHN 392.1PRT 704.1IRN IND 399.1CHN 625.1IRN 786.1SUN 429.1IND 633.3IND 695.1CHN 706.1IND 678.1IRN 577.2IRN 813.3IND GRC 591.1PRT 199.1IND 773.2SUN 734.3SUN 734.1SUN 502.3MMR 816.1ITA 503.1MMR 433.1IND 794.1SUN 479.1POL 626.1IRN 729.1IRN 794.3SUN 768.1IND 377.1IRN 806.1ITA 773.1SUN 475.1AFG 400.1CHN 696.1IND 605.1GRC 631.1IRN 721.1CHN 241.2IND 745.1SUN 788.1SUN 308.1IRN 489.1SUN 674.1IRQ 700.1CHN 469.1AFG 673.1SUN 675.3TUN 825.3CHN 767.1SUN 430.1IND 643.1IND 580.1IRN 88.1POL 793.3SUN 419.1IND 668.1YUG 711.1IND 516.1IND 780.2SUN 710.1IND 787.3SUN 423.2IND 476.2AFG 714.1IND 649.1CHN 421.1IND 732.2IND 747.1ETH 413.1IND 415.1IND 246.1IND 692.1IND 424.4IND 59.1PRT 441.1CHN 775.3SUN 763.1IND 825.4CHN 564.1GRC 435.1CHN 789.2SUN 300.1TUR 413.3IND 513.1IRN 819.1CHN 645.1IND 819.3CHN 775.2SUN 776.1SUN 769.2DZA 646.1IND 748.1SUN 771.1SUN 466.3AFG 404.2IRN 783.1SUN 427.1IND 703.1CHN 473.1AFG 583.1CHN 478.1AFG 528.1CHN 726.1CHN 733.1IRN 418.1IND 728.2IRQ 701.1IRN 152.1PRT 655.1CHN 777.1FIN 811.1TUN 474.1AFG 661.1SUN 521.2IND 746.1SUN 426.1IND 797.1SUN 798.1SUN 487.1SUN 633.2IND 749.2SUN 752.1SUN 586.4CHN 715.3IND 325.1GBR 675.1TUN 738.1SUN 511.1IND 584.2CHN 457.2AFG 790.1SUN 273.1ESP 564.3GRC 198.4IND 720.1CHN 730.1IND 779.4SUN 653.1CHN 728.1IRQ 659.1CHN 409.1IND 792.1SUN 679.1IRN 363.1YUG 697.1IND 318.1CHN 375.1IRN 357.2YUG 410.2IND 234.3YUG 407.1IND 235.4GRC 234.4YUG 321.1CHN 715.1IND 290.1CRE 327.1PLE 440.4CHN 155.1PRT 279.1ESP 499.1TUR 278.2ESP 650.3CHN 803.1IND 504.1CHN 155.4PRT 526.3IND 458.2AFG 393.1PRT 376.3IRN 582.1CHN 596.3PRT 656.1CHN 772.1SUN 363.3YUG 394.1PRT 818.2CHN 378.1IRN 502.2MMR 14.2YUG 353.1YUG 660.1CHN 378.4IRN 86.2IND 584.1CHN 817.1CHN 727.1CHN 719.1CHN 645.2IND 604.2ESP 381.1IND 440.1CHN 87.1IND 610.1YUG 772.4SUN 466.4AFG 243.4IND 628.2IRN 556.4CAI 353.3YUG 713.1IND 749.1SUN 324.1CHN 14.1YUG 606.1GRC 360.1YUG 817.2CHN 382.1IND 299.1TUR CHN IND IRN 56.1ESP 486.1SUN 648.1CHN 30.3AUS 235.3GRC 647.1CHN 576.1IRN 522.4IND 641.2IND 407.2IND 680.1ITA 406.3IND 507.3AUS 903.1IND 573.1TUR 500.1IRQ 518.1IND 808.2SUN 522.1IND 386.1ESP 243.2IND 568.4CHN 615.2YUG 594.2PRT 362.1YUG 209.1EGY 594.3PRT 298.1TUR 39.3ITA 127.1IND 208.2IND 319.2CHN 408.2IND 497.4MAR 320.3CHN 399.2CHN 403.1ESP 556.1CAI 424.1IND 596.4PRT 644.1IND 327.4PLE 166.3IND 789.3SUN 244.3IND 793.4SUN 784.1ITA 208.1IND 572.2TUR 408.4IND 761.1SUN 296.1TUR 237.4IRN 379.1IRN 446.1CHN 420.1IND 615.4YUG 371.2YUG 720.4CHN 456.1AFG 524.2IND 718.1CHN 382.2IND 611.1YUG 30.1AUS 403.4ESP 153.1PRT 574.1TUR 376.2IRN 570.1GRC 428.2IND 802.1SUN 281.1GRC 493.1TUN 344.1ESP 826.1CHN 278.3ESP 271.1ESP 566.1GRC 684.1IRN 650.2CHN 405.1IRN 593.1PRT 818.3CHN 242.3IND 808.4SUN 689.1ESP 595.1PRT 523.1IND 577.1IRN 168.1CHN 126.1IND 523.3IND 579.1IRN 79.1IND 760.1SUN 135.1AUS 572.1TUR 448.1ROU 642.2IND 512.1IND 585.2CHN 628.3IRN 569.1GRC 587.1CHN 439.1CHN 814.1TUN 764.1IND 565.1GRC 358.3YUG 774.2ETH 177.1PRT 526.2IND 297.1TUR 524.3IND 423.1IND 739.1IND 765.4SUN 237.2IRN 488.1SUN 561.1CRE 282.1GRC 416.1IND 613.1YUG 730.4IND 547.1ESP 238.1IRN 795.2SUN 342.2ESP 795.1SUN 465.1AFG 557.1CAI 807.1SUN 34.1IND 128.1IND 32.1IND 532.1TUN 322.1CHN 509.1PRT 804.4SUN 137.1AUS 774.4ETH 314.1CHN 4.1IRQ 755.1SUN 124.1IND 503.2MMR 725.1CHN 787.1SUN 536.2MAR 410.1IND 114.2YUG 240.2IRN 434.1CHN 227.4ESP 236.1GRC 352.1YUG 465.4AFG 402.1GRC 317.2CHN 461.1AFG 248.1IND 559.1ESP 38.1POL 799.2SUN 37.1POL 254.1MAR 37.3POL 313.2MMR 312.1IRN 313.1MMR 13.2BGR 13.1BGR 91.2IND 911.1HUN 799.4SUN 131.2ESP 337.1HUN 309.2IRN 77.1YUG 35.1POL 181.1POL 311.1IRN 311.2IRN 40.3FRA 309.1IRN 193.1FRA 187.1ITA 76.1YUG 759.2SUN 36.4POL 312.2IRN 736.2SUN 52.1ESP 355.1YUG 736.3SUN 634.1HUN 636.2TUN 181.2POL 156.1YUG 245.1AUS 800.1SUN 652.2CHN 326.1AUS 268.1ESP 217.1MAR 40.1FRA 445.1CHN 188.4FRA 130.1ESP 191.1FRA 6.3AUS 25.1AUS 6.4AUS 134.1AUS 131.1ESP 93.1IND SUN 78.1YUG 756.2ITA 759.4SUN 76.3YUG 178.4YUG 780.4SUN 156.4YUG 154.1PRT 622.2BGR 165.1IND 115.3YUG 57.4PRT 753.2SUN 213.1MAR 249.1IND 74.2YUG 50.1ESP 801.2SUN 67.1ESP 84.1ESP 134.3AUS 41.4FRA 387.2ESP 801.3SUN 105.2FRA 200.1FRA 216.1MAR 373.1IRN 36.2POL 42.1FRA 60.2PRT 75.1YUG 172.2IND 185.1ITA 73.1BGR 368.1YUG 75.4YUG 211.4FRA 174.1IND 307.3IRN 197.4IND 52.3ESP 280.1ESP 162.1ESP 258.3MAR 84.4ESP 612.1YUG 331.4BRA 266.1ESP 180.1ESP 141.2CHN 174.4IND 652.1CHN 741.1AFG 261.1CAI 307.1IRN 54.1ESP 551.1ESP 10.1IND 205.1IND 5.1AUS 192.1FRA 330.1EGY 165.3IND 163.1ESP 496.1MAR 599.1PRT 494.1ESP 39.1ITA 438.4CHN 329.2EGY 331.2BRA 541.4ESP 270.1ESP 438.3CHN 7.1AUS 411.1IND 204.4IND 225.1CAI 27.1AUS 436.1CHN 754.1SUN 490.1FRA 204.3IND 602.4ESP 206.3IND 259.1MAR 161.1ESP 96.1ROU 116.1ESP 48.1ESP 495.1MAR 110.1FRA 621.1BGR 44.3MAR 255.1TUN 592.1PRT 195.1IND 158.2GRC 636.3TUN 272.1ESP 211.3FRA 355.2YUG 735.1DZA 16.1ESP 176.1AFG 1.2MAR 284.1GRC 460.1AFG 285.1GRC 61.1ESP 688.1ESP 640.1IRN 89.1AUS 372.1YUG 11.1IND 178.3YUG 491.2FRA 179.1GRC 250.1CHN 194.1FRA 2.1CRE 345.1BGR 712.4IND 49.1ESP 123.2AUS 129.1IND 105.1FRA 95.1CHN 201.1ESP 159.1BGR 94.1IND 766.1DZA 744.4IND 515.1IRN 182.1POL 99.1POL 357.3YUG 345.3BGR 167.1IND 202.1ESP 276.1ESP 104.1ITA 115.1YUG 107.1FRA 391.1PRT 129.2IND 548.1ESP 17.2ESP 215.1MAR 651.1CHN 607.1YUG 138.1AUS 340.1ESP 169.1CHN 17.4ESP 447.3CHN 365.1YUG 330.4EGY 367.3BGR 804.1SUN 622.1BGR 602.2ESP 149.1GBR 190.1FRA 395.1PRT 197.1IND 447.4CHN 753.1SUN 491.1FRA 167.2IND 370.1YUG 206.1IND 28.1AUS 329.3EGY 227.3ESP 343.2ESP 190.2FRA 315.1CHN 744.1IND 781.1SUN 666.4AFG 220.1ESP 454.1AFG 44.1MAR 283.1GRC 79.2IND 158.1GRC 173.1AUS 70.1ESP 139.1FRA 19.1IND 310.1IRN 756.1ITA 214.1MAR 21.1IND 617.1YUG 437.1CHN 638.1CRE 96.3ROU 450.3ROU 21.2IND 72.9YUG 69.2ESP 635.4HUN 18.1IND 560.1GRC 212.1MAR 169.4CHN 160.2ESP 146.1ESP 11.2IND 451.1ROU 388.1ESP 100.1POL 437.3CHN 164.1IND 251.1AUS 122.1AUS 110.4FRA 274.1ESP 252.1DZA 257.3TUN 140.1TUR 609.1YUG 218.1TUN 337.2HUN 239.1ESP 20.1IND 253.1MAR 637.1TUR 669.4CHN 722.1CHN 72.1YUG 537.1TUN 519.3IND 553.4CAI ROU 97.1POL 175.3CHN 184.1POL 742.1DZA 264.1CAI 90.1AUS 256.2TUN 142.1MAR 672.1POL 354.1YUG 262.1CAI 542.1ESP 639.1CRE 47.1ESP 510.1PRT 10.2IND 288.1CRE 22.1AUS 385.2ESP 553.2CAI 682.1CRE 494.3ESP 590.1PRT 63.4ESP 549.1ESP 145.1ESP 289.1CRE 125.1IND 530.1AFG 117.1ESP 390.1PRT 635.1HUN 144.1ESP 338.3HUN MAR 269.1ESP 15.2YUG 359.1YUG 66.1ESP 23.1AUS CAI 670.4POL 15.4YUG 179.4GRC 545.1ESP 170.1CHN 630.1IRN 348.1BGR 45.1SYR 175.2CHN 654.3CHN 751.2SUN 396.1PRT 203.1IND 346.1BGR 397.1PRT 222.1CRE 310.2IRN 287.2CRE 750.3SUN 904.1ESP 618.2YUG 750.1SUN 257.2TUN 103.1ITA 230.1PRT 63.2ESP 498.1TUR 119.2IND 600.1ESP 374.3IRN 467.1AFG 545.4ESP 53.1ESP 248.2IND 186.1ITA 740.2SUN 24.1AUS 751.1SUN 98.1POL 620.1YUG 147.1IRQ 581.1IRN 737.1ITA 33.1IND 338.1HUN 138.2AUS 546.1ESP 123.1AUS 481.1POL 277.1ESP 295.1TUR 66.2ESP 260.1CAI 256.1TUN 484.1ITA 301.1TUR 120.1AUS 349.1BGR 396.4PRT 71.1BGR 608.2YUG 60.1PRT 562.1GRC 221.1ESP 229.1PRT 462.2AFG 762.4DZA 712.2IND 207.4IRN 55.1ESP IND 425.1IND 732.1IND 603.1ESP 46.3CRE 624.3BGR 12.1IND 779.1SUN 520.1IND 112.2TUR 102.1ITA 608.1YUG 670.1POL 624.4BGR 519.1IND 665.2AFG 65.1ESP Com1ESP 133.1AUS 85.1ESP 101.1ITA 68.1ESP 224.3CHN 9.3PRT 477.1PRT 189.2FRA 401.1PRT 219.1ESP 210.1IND 291.1CYP 121.1AUS 455.3AFG 459.1AFG 183.1POL 535.3TUN 453.1AFG 143.1ESP 716.1IND 717.1CHN 497.1MAR 369.2YUG 468.1AFG 578.1IRN 455.2AFG 43.1TUN 616.1YUG 228.1ESP 108.1FRA 389.1PRT 616.2YUG 444.4CHN 69.1ESP 598.1PRT 527.2IND 347.1BGR 232.1IND 157.1YUG 223.1MMR 641.1IND 119.1IND 531.1ESP 244.2IND 150.1GBR 571.1TUR 663.1ROU 81.1IND 538.1TUN 64.1ESP 667.1AFG 46.1CRE 150.4GBR 555.1ESP 361.1YUG 62.1ESP 492.1ESP 189.1FRA 620.4YUG 336.1HUN 106.1FRA 514.2IRN 527.3IND 778.1ITA 9.4PRT 436.2CHN 398.1PLE 412.3IND 143.4ESP 822.2CHN 417.1IND 782.3SUN 136.1AUS 654.4CHN 132.1AUS 226.1ESP 586.2CHN 8.1PRT 685.1ESP 642.3IND 463.1AFG 676.3TUN 457.1AFG 664.1AFG 26.1AUS 109.1FRA 233.1IND 58.2PRT 452.2AFG 590.2PRT 113.1ESP 597.1PRT 171.1CHN 414.1IND 539.1ESP 543.2ESP 471.1AFG 544.1ESP 534.1MAR 676.4TUN 757.4ITA 757.1ITA 601.1ESP 80.1IND 317.1CHN 770.1SUN 563.1CRE 231.2HUN 541.3ESP 529.1AFG 148.1AUS 118.1PRT 196.4IND 550.2ESP 679.2IRN 51.1ESP 723.1AFG 198.3IND 533.1MAR 305.1EGY 796.2SUN 240.3IRN 224.4CHN 442.1CHN 372.2YUG 29.1AUS 92.2IND 525.1IND 58.1PRT 619.1YUG 480.1POL 151.1PRT 690.1GRC 294.1TUR 231.3HUN 387.1ESP 472.1AFG 443.1CHN 507.1AUS 366.1YUG 292.1CYP 444.3CHN 534.2MAR 488.3SUN 111.1MAR 614.1YUG 293.1TUR 351.1YUG 374.2IRN 383.1GRC 412.1IND 540.1ESP 275.1ESP 614.3YUG 683.1ESP 912.3HUN 687.1TUN 83.1ESP 422.1IND 558.1CAI 328.4EGY 339.1PRT Com4TUR 821.1CHN 458.4AFG 263.1CAI 286.1GRC 92.1IND 287.1CRE 762.2DZA 356.1YUG 483.1POL 341.1ESP 380.1IRN 450.4ROU 693.1IRQ 681.1IRN 567.1GRC 247.1IND 384.1GRC 533.2MAR 470.1AFG 431.1IND 315.2CHN 328.1EGY 364.1YUG 358.1YUG 166.4IND 765.1SUN 724.1IND 623.3BGR 432.1IND 686.1MAR 482.1POL 691.1GRC 677.2ESP 725.4CHN 743.1SUN 912.4HUN 128.2IND 464.1AFG 552.2CAI 517.4IND 82.1IND 554.1CAI C AFG AUS BGR BRA CAI CHN CRE CYP DZA EGY ESP ETH FIN FRA GBR GRC HUN IND IRN IRQ ITA MAR MMR PLE POL PRT ROU SUN SYR TUN TUR YUG grain width were all increased in the modern collection, which seems likely to be a result of modern breeding for higher yields via larger seeds (Gegas et al. 2010). A spring growth habit under the North European spring sowing conditions was observed for 86% of the Watkins LCs studied. Of those LCs which showed spring growth habit, 178 accessions (24%) did not carry a spring type alleles at any of the three Vrn-1 loci. This may be partly due to the UK conditions allowing weak winter types to get vernalised due to cold nights in March. However, given the high number of cases, this suggests that another pathway leading to a spring growth habit may be responsible in some of those LCs. Up to five vernalisation genes have been reported for winter wheat to date, but so far mainly the three Vrn-1 homeologues have been well investigated (Distelfeld et al. 2009). The identification of LCs with Vrn-1-independent spring growth habit could help to discover more details of the vernalisation pathway in wheat. The genetic diversity found for the Vrn-1 and Ppd-1 genes was low. This is expected as these genes were most likely under selection as they play a major role in adaptation to the local climate. Due to selection pressure, the number of alleles would be low in the progenitor plants. Moreover, the genotyping using gene-based markers only revealed the presence of known alleles. Further alleles present, will not be detected. The genotyping of the Watkins collection, using 41 SSR markers was followed by an analysis of the genetic diversity levels present at the marker loci. A high level of genetic diversity was detected irrespectively of the index of diversity used (see Table 1). An average allele number of 22.4 and a gene diversity index (d PIC ) of 0.75 were found. These values are at the higher end of those found in other wheat LC collections (see Table S7 for a list of published studies). The genetic diversity of the IPK bread wheat collection of 998 LCs from 68 countries was reported as an average allele number of 18.1 per locus and a d PIC of 0.77 (Huang et al. 2002) (see also Table S7). The INRA collection