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Theor Appl Genet (2010) 121:971–984 DOI 10.
and phenotyping data yielded 11 QTLs for LLS (explaining 1.70–6.50% phenotypic variation) in three environments and 12 QTLs for rust (explaining 1.70–55.20% phenotypic variation). Interestingly a major QTL associated with rust (QTLrust01), contributing 6.90–55.20% variation, was identiWed by both CIM and single marker analysis (SMA). A candidate SSR marker (IPAHM 103) linked with this QTL was validated using a wide range of resistant/susceptible breeding lines as well as progeny lines of another mapping population (TG 26 £ GPBD 4). Therefore, this marker should be useful for introgressing the major QTL for rust in desired lines/varieties of groundnut through markerassisted backcrossing.
SNPs).) and transcript sequence data using next generation sequencing technologies (SJ Knapp. is high yielding and resistant to both diseases. pers. pers. 1987. microsatellites or simple sequence repeat (SSR) markers. Recent years have seen signiWcant progress in the area of crop genomics applied to breeding (see Varshney et al. Materials and methods Mapping population The mapping population comprised of 268 RILs derived from the cross TAG 24 £ GPBD 4. RILs were sown in randomized block design (RBD) with two replications at Dharwad. Rainy 2007 (ER-IV) and Rainy 2008 (ER-V). have been extensively used in several crop species (see Gupta and Varshney 2000). Groundnut breeders across the world have developed superior varieties resistant to LLS and/or rust. Cuc et al. 2008. Anderson et al. 1981. Seeds of these RILs were treated with seed protectant before sowing. Genetic studies on LLS and rust resistance suggest that resistance to these fungal diseases is complex and polygenic in nature and probably controlled by several recessive genes (Sharief et al. Post-rainy 2007 (ER-III). Development of more SSR markers is underway in groundnut from BAC (bacterial artiWcial chromosome)—end sequences (DR Cook. Both the parental genotypes (TAG 24 and GPBD 4) were also sown after every 50 rows as controls. Ferguson et al. Rainy 2004 (ER-I).. Moretzsohn et al. 2002). 2009). single nucleotide polymorphisms. 2004. Gautami et al. Phenotyping of mapping population for rust was conducted at Dharwad in Wve environments.972 Theor Appl Genet (2010) 121:971–984 strategy to surmount additional cost of production and hazardous eVect of fungicides on the soil and environment. Mace et al. 2005). The LLS conidia and rust urediniospores were isolated by soaking and rubbing infected leaves in water for 30 min and used for inoculation. Two more genotypes namely TMV 2 and 123 . Nevill 1982. expensive. respectively. viz. The inoculums for LLS and rust were produced and maintained separately on TMV-2 (a highly susceptible variety to LLS) and mutant 28-2 (resistant to LLS but highly susceptible to rust). co-occurrence of these two diseases and defoliating. SSR-based genetic map based on recombinant inbred line (RIL) mapping population of cultivated groundnut has been developed recently (Varshney et al. However. respectively. 2004. 8 mutant lines. 11 interspeciWc derivatives. Rainy 2005 (ER-II). The mapping population was developed by employing single seed descent (SSD) method from F2. 2007. Jogloy et al. Varshney et al. Green and Wynne 1986. the leading groundnut variety in Karnataka and other southern states of India (Gowda et al.. DJ Bertioli. Several studies have demonstrated the utility of molecular markers and marker-assisted selection (MAS) to improve the eYciency of conventional breeding especially in the case of low heritable traits. 2005. 1987). Furthermore. commun. Hamid et al. the present study was undertaken to construct a genetic map and to identify the QTLs for LLS and rust in cultivated groundnut by using a recombinant inbred line (RIL) mapping population (TAG 24 £ GPBD 4). However. Phenotyping Phenotyping of mapping population along with the parental genotypes for LLS was carried out in three environments. Hopkins et al. where phenotypic selection is diYcult. a large number of SSR markers have been developed by several groups during last 10 years (e. India. validation of one SSR marker (IPAHM 103) associated using a major QTL for rust was also undertaken with several breeding lines and varieties which demonstrated the utility of this marker to breed rust resistant groundnut varieties through marker assisted selection.). commun. 2 South American landraces and 2 advanced breeding lines) were used (ESM 1). 2009). pers. ArtiWcial disease epiphytotics were created in separate screening experiments for the two diseases using “spreader row technique”. In view of above. Dwivedi et al. while GPBD 4. For validating the utility of a candidate marker IPAHM 103. 1995). 2003. genome mapping and trait mapping is still in its infancy in groundnut. Furthermore. a set of 46 highly diverse germplasm lines (10 susceptible cultivars. 2009) and some trait mapping studies have been conducted (Herselman et al.g. 1980. viz. 2005 (EL-II) and 2006 (EL-III) at Dharwad. 2009). Ten seeds from each RIL were sown in 1 m rows with 30 cm and 10 cm inter. lack accuracy or precision (see Varshney et al. He et al. One of the main bottlenecks for slow progress in molecular mapping in groundnut is low level of genetic diversity present in the germplasm of cultivated groundnut and non-availability of critical mass (large number of markers) and adequate molecular markers (e. Rainy 2004 (EL-I). Motagi 2001. The F6 progenies were used for phenotyping and F7 progenies for genotyping. Among diVerent types of marker systems. partial and polygenic nature of LLS makes the identiWcation of resistant and susceptible lines cumbersome through conventional screening techniques (see Leal-Bertioli et al. 1978. additive genetic variance seems to contribute predominantly to the resistance (Kornegay et al. TAG 24 is a popular high yielding cultivar in India but susceptible to both diseases (Patil et al. 2002).and intra-row spacing. 1986. considered as markers of choice for breeding applications. 2006). commun.g. 1999. In case of groundnut also. For instance. 13 hybrid derivatives from NcAc (North Carolina Accessions) genotypes.
0 and maximum recombination fraction ( ) of 0.1 (Applied Biosystems. Recombination fraction was converted into map distances in centiMorgans (cM) using Kosambi mapping function (Kosambi 1944). 113 days (LR-V) and 120 days (LR-VI) after sowing in diVerent seasons by using a modiWed 9-point scale (Subbarao et al. 90 days (LR-III).2 (Voorrips 2006). Cofactors were selected using stepwise regression (Miller 1990) with an F to enter and F to delete value of 3. Lincoln et al. To assess and quantify the genetic variability among the RILs. The new marker orders were again conWrmed with the “compare” commands. The “try” command was used to determine the exact position of the new marker orders. Components of resistance to rust. compiled from several sources. viz. latent period (LP) and infection type (IT) were also assessed in the greenhouse. High humidity was maintained by irrigating the Weld in the night with sprinkler or furrow irrigation. The non-targeted disease. 1987. as mentioned in Varshney et al. Disease scoring Disease scoring for LLS was done at 70 days (LLS-I) and 90 days (LLS-II) after sowing. Statistical analysis Phenotypic data The analysis of variance (ANOVA) at diVerent stages of disease scoring for LLS and rust was performed to test the signiWcance of diVerences between RILs. The inter-marker distances calculated from MAPMAKER were used to construct linkage map by using MAPCHART version 2. as higher annealing temperature (65°C instead of 60°C) was used to increase the speciWcity of amplicons. Marker genotyping DNA of parents and RILs (F7) was isolated using SIGMA® GenElute Plant Genomic DNA Kit. The most likely marker order within each linkage group was estimated by using three point analyses (“three point” command). 2007)..1 (Utz and Melchinger 1996). All necessary computation for the Weld trials was performed with the software package GenStat 10th edition (Payne et al. i.e. PCR amplicons were separated using polyacrylamide gel electrophoresis (PAGE) or capillary electrophoresis. TAG 24 and GPBD 4. Another modiWcation was in the ampliWcation proWle. 1990).8% agarose gel with known concentrations of uncut lambda DNA standard. The inoculum containing 20. Finally. QTL analysis All necessary computation for QTL mapping and estimation of their additive eVects was performed with the software package PLABQTL version 1. rust and LLS with rust was also estimated.s) were estimated.2 ml/1. Marker orders were conWrmed by comparing the Log-likelihood of the possible orders using multipoint analysis (“compare” command). subsequently were used for genotyping the mapping population.. DNA quality was checked and quantiWed on 0. were screened for polymorphism with a 1089 SSR markers. 105 days (LR-IV). the PCR products were tested on 1. Additional inoculum was provided by placing pots containing diseased plants at every 50 rows. A LOD threshold of 2.2% agarose gel to check for ampliWcation and. Estimates of the additive 123 . Allele sizing of the electrophoresis data was carried out using Genescan 3. and for rust was scored at 70 days (LR-I).000 conidia/urediniospores per ml water was mixed with Tween 80 (0. plants were inoculated uniformly in the evening with LLS/rust for a week. rust/LLS in LLS and rust experiments. subsequently depending on the use of normal or Xuorescent dyes labeled primers. (2009). respectively. Initially the parents. Jansen and Stam 1994) was used for the detection and mapping of QTLs.5 were set as threshold values for linkage group determination by using “Make Chromosome” command and a set of anchor markers was identiWed from Varshney et al. 1992). incubation period (IP). USA) and Genotyper 3. As in Varshney et al. phenotypic coeYcient of variance (PCV) and heritability in the broad sense (h2b. At 35 days after sowing.0 (Lander et al. Spreader rows were sown at every tenth row as well as border around the Weld to maintain the eVective inoculum load.5. was controlled by spraying fungicide carbendazim (bavistin) 1 g l¡1 and tridemorph (calixin) 1 ml l¡1. respectively. Correlation coeYcient (r) among the diVerent stages of LLS.000 ml of water) as a mild surfactant and atomized on the plants using a Knapsack sprayer. the marker order as per linkage group was calculated using RECORD program (van Os et al.Theor Appl Genet (2010) 121:971–984 973 mutant 28-2 were used as spreader rows for LLS and rust.1 software (Applied Biosystems. 80 days (LR-II). (2009) with some modiWcations: 5 l of reaction volume (instead of 10 l) containing reduced amount of other PCR components was used.5 was chosen to declare a putative QTL as signiWcant. Linkage map construction Marker genotyping data obtained on the mapping population were used for the linkage analysis using MAPMAKER/EXP V 3. (2009). Polymorphic SSR markers identiWed between these genotypes. USA). The method of CIM with cofactors (Zeng 1994. (2009). The genotypic data of 56 mapped markers and mean phenotypic data of 268 RILs were used for CIM QTL analysis. A minimum LOD score of 3. Polymerase chain reactions (PCRs) were performed as given in Varshney et al. 2005).
974 Theor Appl Genet (2010) 121:971–984 eVect of each detected QTL.68 3.00) showed consistently lower disease incidence than TAG 24 (LLS: 4.91 § 1.0 80.00–3.84 48.00 1. QTL £ E interaction could not be analyzed. Rainy 2005 and Rainy 2006) were averaged and analyzed together with mapping data for 56 mapped SSR markers.53 82.00 and rust: 3.49 6.53 21.58 35.60 6.71 6.49 § 0.50 5.28 31. Rainy 2005 (ER-II). High PCV (21.61 33.00 7.24 43. viz.s) for LLS and rust in TAG 24 £ GPBD 4 mapping population Environments/ traitsa LLS EL-I EL-II EL-III Rust ER-I ER-II ER-III Scoring stagea Mean TAG 24 GPBD 4 RILs § SE PCV (%) h2b.00 1.0 81.14 § 0.50 4.0 Environments and stages are abbreviated for LLS as EL-I Rainy 2004. Because of rust scoring at diVerent stages in diVerent environments.38 § 0..30 § 0. LLS-II 90 days to score. EII experiment II.0 77.94 15. For analyzing QTL £ environment (QTL £ E) interactions in case of LLS.e.61 36.00 6. It was moderate to high with very high heritability observed in the EL-II at both stages of LLS.14 § 0.87 to 82. Results Phenotyping of mapping population A set of 268 RILs derived from a susceptible female parent TAG 24 and highly resistant male parent GPBD 4 for LLS and rust.00 1..0 75. was also performed to tag and conWrm potential SSR markers linked to the trait.71–8.00 3.00 2.58 44.75–9.69 6.71 33. along with the parental genotypes. EL-III Rainy 2006.00 3.09 § 0. was pheno- typed for LLS in three environments.10 52.00 7. and for rust as ER-I Rainy 2004.55 § 0.83 26.71 4. Post-rainy 2007 (ER-III). A few RILs exhibited transgressive variation for susceptible reaction.35 4.02 15. EI experiment I. The majority of RILs showed variation for both diseases. The phenotypic coeYcient of variation (PCV) estimates for LLS were high at LLS-I compared to LLS-II (Table 1).60 47.75 9.75 3.82 21.75 4. Rainy 2004 (ER-I).00 1.0 70.00) at all the scoring stages and environments (Table 1).00 2.00 4. the total proportion of phenotypic variation explained (PVE) by all the detected QTLs were obtained by Wtting a multiple linear regression model that simultaneously included all the detected QTLs for the traits in question.81%.55%) at diVerent stages and environments revealed substantial variation for LLS in the RIL population.00 4. ER-II Rainy 2005.24 26. LP latent period (in days).07 § 0.80 5.63 5. LR-V and LR-VI.46 2.00 5. ER-III Post-rainy 2007.0 68. Analysis of variance on these phenotyping data revealed signiWcant diVerences between recombinant inbred lines in the reaction to LLS and rust.61 34.55 § 0.72 3.45 5.87 73.00 2.50 7.62 § 0. Single marker analysis (SMA).50 3. LLS-II. IT infection type (in days) a ER-IV EI ER-IV EII LR-II LR-III LR-I LR-II LR-III ER-V IP LP IT 123 .00 3. EL-II Rainy 2005.34 § 0.50 7.. the total LOD score.57 10.16 37.71–33.0 89. 2005 (EL-II) and 2006 (EL-III) at Dharwad and for rust in Wve environments. The PCV for rust was Table 1 Mean of parents and recombinant inbred lines (RILs) and estimates of phenotypic coeYcient of variation (PCV) and broad sense heritability (h2b. viz.50) and rust (2.40 3.00 4.56 38.60 3. within the parental limits.95 § 0. LR-III. ER-V Rainy 2008. IP incubation period in (days).75 2.09 36.84 25.42 § 0.00 4.86 5. i.56 3.36 § 0.37 4. ER-IV Rainy 2007.61 § 0. Rainy 2007 (ER-IV) and Rainy 2008 (ER-V).48 81.93 9.81 55.31 § 0.02 47.33 59. Rainy 2004 (EL-I). The mean disease score of parent GPBD 4 for LLS (1. phenotyping data for LLS-I and LLSII scored in all three environments (Rainy 2004.50 3.00 25.40 3.73 5. but slightly skewed toward susceptibility to LLS and rust at later stages.51 6.51 4.13 § 0.s (%) LLS-I LLS-II LLS-I LLS-II LLS-I LLS-II LR-III LR-I LR-III LR-IV LR-V LR-VI 6.38 § 0.00 22.35 3.92 § 0. LLS-I 70 days to score.00 11.00 8.40 64.51 21. which is based on simple linear regression method (Haley and Knott 1992).75 8.70 67.00–4.50 40.55 23. The heritability ranged from 40.25 3.50 18.0 69. Phenotypic data on 268 RILs for LLS and rust showed near normal distribution for both diseases.
e.01) stages of scoring (data not shown). 4 QTLs were observed for averaged data for LLS-II. 80 days (LR-II). i. LG 13 and LG 14) to ten (LG 8).85%) showed segregation distortion (SD). P < 0. As a result. only 67 SSR markers showed polymorphism between parental genotypes.70 cM (LG 14) to 87. Three QTLs (QTLLLS05. ESM 2). 90 days (LR-III).09. QTL analysis revealed one QTL (QTLLLS03) for LLS-I and accounted for 3.1 (Utz and Melchinger 1996). IdentiWcation of QTLs for LLS resistance Mapping data for all 56 SSR markers assigned to the genetic map were analyzed together with phenotypic data obtained at both stages (LLS-I and LLS-II) in all three environments (Rainy 2004.. However. P < 0. 2005). Rainy 2005 (ER-II).58%) was observed at diVerent stages of rust. 70 days (LR-I). High to very high heritability (34. LG5. were contributed by the resistant parent. i.50 and 4. Eleven SSR markers. Linkage map analysis of 67 markers assigned a total of 56 markers to 14 linkage groups (LGs) spanning 462. these QTLs showed 3.30% PVE. a total of 11 QTLs were detected on ten linkage groups at LLS-I and LLS-II stages in all three environments (Table 2).19. the distorted markers were also used for linkage map construction. a QTL (QTLLLS03) was found for LLS-II in all the three screening environments (EL-I.50 to 3.01).98. QTLLLS10.37–0. The lengths of linkage groups ranged from 5. Of these.20% PVE. Of these. were screened on two parental genotypes of the mapping population of which 907 markers showed high quality ampliWcation in both genotypes. LG8.80–4. QTLLLSQE03) identiWed in LLS-II were co-mapped with the same QTLs identiWed for LLS-I. LG2.40% PVE in LLS-II. 1). QTLLLS02) were found to be common at both the stages (LLS-I and LLS-II). Comparison of these two maps showed 28 common markers on 13 linkage groups. LG 4.70 and 3. 20 markers (29. EL-II and EL-III) for LLS-I and LLS-II were used with mapping data and a signiWcant QTL £ E interaction was observed at LLS-I (3.87. GPBD 4.80% PVE.0%) was observed in diVerent screening environments (Table 1).01) and rust (r = 0.70–2.Theor Appl Genet (2010) 121:971–984 975 high at later (LR-II and LR-III) stages. except for four markers that were mapped on diVerent linkage groups (Fig. Of these 907 markers. Of these.70 to 2. Subsequently.00–4.90% PVE.11 with 3. all these QTLs were minor as all of them explained <10% PV (Collard et al. In the case of the third environment (EL-III).70 to 4. data from individual environment and means across the environments (EL-I. however. QTLLLS06.30% PVE.70 to 7.65 to 5. a total of six QTLs were detected for both stages (LLS-I and LLS-II) on linkage groups LG1.90. One QTL (QTLLLS03) was co-mapped in LLS-I and LLS-II with 3. Molecular marker and linkage map analysis A total of 1089 SSR markers. Moderate to high PCV (15. QTLLLS07) were co-localized at both the stages having 1.0–89. 11 QTLs had small eVects with PVE ranging from 1. 2.51 with 1. Detailed analysis of these data sets showed highly signiWcant and positive correlation between stages in each of the environment for LLS (r = 0. However. 105 days (LR-IV). (2009). QTLLLS11) were detected for LLS-II and revealed 2. The order of these common markers is congruent in the majority of the cases. the favorable alleles for resistance to LLS in all QTLs came from the resistant parent GPBD 4 (Table 3).24 cM with an average marker interval of 8. IdentiWcation of QTLs for rust QTL analysis for rust data for a total of six stages. The correlations were high even across the environments in LLS. QTLLLS07.90%.80 cM (LG 8). QTL L L S Q E 02. but negative correlation was observed between LLS and rust (data not shown). Chi-square ( 2) test was performed on the genotypic data to test the null hypothesis for the expected 1:1 Mendelian segregation on all the scored markers.80% PVE. The linkage map obtained in the present study was also compared with the reference map for cultivated groundnut species developed based on mapping population TAG 24 £ ICGV 86031 (Varshney et al. P < 0.25 cM (Fig. Four QTLs were detected for LLS-I (resistance values scored at 70 days after sowing) and LLS-II (resistance values were scored at 90 days after sowing) stages in the EL-I environment (Rainy 2004) on linkage groups LG1. LG9 and LG13 with PVE ranging from 1. two QTLs (QTLLLS01. 2. Interestingly.37–0. Rainy 2004 (ER-I). For analyzing QTL £ E interactions. This QTL was detected at LOD values ranging from 2. In the EL-II environment (Rainy 2005). LG10 and LG11..e. Four QTLs (QTLLLS03. However. as mentioned in Varshney et al.40 and 3. except one detected at LLS-II (QTLLLS10). While three QTLs were observed for averaged data of LLS-I at LOD value ranging from 2. EL-II and EL-III).90–4. remained unlinked. The resistant alleles for all QTLs. Rainy 2007 (ER-IV) and Rainy 2008 (ER-V) revealed a total of 12 putative QTLs on 8 diVerent linkage groups (Table 4). P < 0. Post-rainy 2007 (ER-III). Rainy 2005 and Rainy 2006) by using PLABQTL version 1. However. 113 days (LR-V) and 120 days (LR-VI) after sowing collected under Wve diVerent environments.00–4. The number of markers mapped per linkage group ranged from two (LG 3. three QTLs (QTL L L S Q E 01.01) and LLS-II (8. Due to the limited number of polymorphic markers.90–4.00% on seven diVerent linkage groups. one major QTL (QTLrust01) was detected 123 .10% PVE in LLS-I and 6. 2009). Here again. LG9. genotyping data were obtained on the complete set of 268 RILs for 67 markers.83–59.
Asterisk represents the markers showing segregation distortion.85 pPGSseq11C8 QTLrust05 18.70 GM692*a GM699 QTLLLS09 0. have been shown using the following boxes: Open rectangle indicates QTL for LLS.39 55. closed rectangle indicates QTL for rust 123 .00 2.00 5.71 TC9F04 38.94 LG 2 PM137 GM670 TC1A02*a TC11A04*a IPAHM524a 0.59 12.75 30.67 TC5A06a PM436 a Lec-1*a gi-1107a 31.00 TC9B08 0.40 IPAHM395 Fig.81 47.37 40. Seven linkage groups each have been shown in a (LG 1–LG 7) and b (LG 8–LG 14).55 13.00 LG 1 TC3H02 0.00 9.59 37.79 pPGSseq17C9* QTLLLS01 QTLLLSQE01 QTLLLS05 LG 4 0.00 QTLrust03 QTLLLS06 LG 3 QTLrust04 TC1B02* 20.94 55. Numbers on the left of each linkage group are Kosambi map distances.00 8.66 34.79 7. QTLs for LLS and rust. Distorted markers are indicated with suYx “a” indicating markers from tetraploid reference map (Varshney et al. as mentioned in Tables 2.24 PM3 PM434 TC4F02* Ah4-04 AC3D07 pPGSseq18A5b a GM745a pPGSseq19G7 a QTLrust10 QTLrust07 41.00 QTLLLS02 LG 12 0.16 14. rectangle with diagonal lines indicates QTL across the environment for LLS.80 TC2C07a LG 11 0.00 6.63 QTLLLS04 QTLrust11 QTLrust09 26.06 PM377*a TC1A01a QTLrust12 pPGSseq13E6 0. 1 Genetic linkage map based on TAG 24 £ GPBD 4 population showing QTL positions for LLS and rust.86 TC4D09a QTLrust02 pPGSseq7G2 a 79. 3 and 4.28 24.62 pPGSseq18G1 a 87.976 Theor Appl Genet (2010) 121:971–984 a 0.00 LG 8 QTLrust06 QTLLLS08 LG 9 QTLLLSQE02 QTLrust08 QTLLLS03 LG 10 TC2G05* TC9H09 a GM624*a TC4G10* 0.46 44.56 TC4E09* IPAHM121* 49.00 6.00 5.56 TC3E05* TC7H11*a QTLLLS11 LG 13 QTLLLSQE03 LG 14 0.09 44.00 TC5A07 IPAHM176 16.80 TC3B05 LG 5 PM179 a GM633a QTLLLS07 QTLLLSQE04 LG 6 QTLLLS10 LG 7 0.16 47.00 IPAHM103 14. On the right hand side of the linkage groups.74 81.29 PM50 PM183 a b 0.00 IPAHM108*a QTLrust01 0.60 pPGSseq19D6 a IPAHM272 75.71 43.25 7. 2009).44 16.45 10.23 TC9F10a TC6H03 a GM660a Ah4-101 19.86 57.
three QTLs were identiWed for LR-IV.50 3. In case of Post-rainy 2007 environment (ER-III). details about these abbreviations are given in “Material and methods” as well as in Table 1 at an LOD score ranging from 4.03 2.95 2.342 ¡0.75 30.16 47.00 pPGSseq13E6 0.32 on linkage group 6 contributing 6.3 97.24 PM3 PM434 TC4F02* Ah4-04 AC3D07 pPGSseq18A5b GM745 pPGSseq19G7 58.40% PVE.80 2.65–5.11 5.10– 30. Similarly in the case of Rainy 2005 environment (ER-II).11 2.7 19. These common markers in both linkage maps are in the same order Table 2 Features of QTLs for late leaf spot (LLS) identiWed in TAG 24 £ GPBD 4 population QTL Marker intervalA PM436-Lec-1ab TC9F10-GM660ab TC2G05-TC9H09bdef TC1A01-pPGSseq18G1 IPAHM524-TC4D09 cd PM179-GM633cdf pPGSseq13E6-PM3c TC5A07-IPAHM395 d b LG Position (cM) LOD Phenotypic 2 variation (RP .1 TC5A06 pPGSseq14H6 GM694 PM436 Lec-1 gi-1107 26.9 pPGSseq9H8b TC1E06 TC11F12 38.0 pPGPseq2B9 0. Among them. fLLS-II EL-III. 2 Comparison of two linkage groups of the developed map with the tetraploid reference map (Varshney et al.0 25.68 2.80 1.90–55.231 to ¡282 ¡0.65–2.03 2.8 pPGPseq7G2 75.00-4.66–3.79 pPGSseq17C9* 87.81 47. eLLS-I EL-III.3 88.70-6.219 to ¡286 ¡0.213 to ¡0. LG1 and LG 8 of TAG 24 £ GPBD 4 aligned through common markers with LG_AhIX and LG_AhIII of TAG 24 £ ICGV86031.61 5. 3).80 1.30 2.256 0.163 to ¡0. %) 3.09 44.90-4. 2009). LLS-II EL-II.183 gi-1107-pPGSseq7G2 cd IPAHM103-pPGSseq19D6f TC7H11-IPAHM176f Superscripts on group of markers associated with QTLs represent environment and stages as follows: aLLS-I EL-I. respectively.193 ¡0.Theor Appl Genet (2010) 121:971–984 LG 1 (TAG 24 x GPBD 4) LG_AhIX* (TAG 24 x ICGV 86031) LG_AhIII* (TAG 24 x ICGV 86031) 977 LG 8 (TAG 24 x GPBD 4) 0. three QTLs (QTLrust01.5 15.10-4.2 78.79 ¡0.223 ¡0.325 ¡0.96 4.90 4.50% PVE.208 ¡0.35 to 44.00 Additive eVect QTLLLS01 QTLLLS02 QTLLLS03 QTLLLS04 QTLLLS05 QTLLLS06 QTLLLS07 QTLLLS08 QTLLLS09 QTLLLS10 QTLLLS11 A d 1 11 9 10 1 2 5 8 13 6 12 44–46 0 0 12 48–50 18 0–4 2 0 0 18 2.9 pPGSseq19G7 44.40 3.0 26.80 TC2C07 119. In case of Rainy 2004 environment (ER-I).40 3.273 ¡0.67 TC5A06 PM436 Lec-1* gi-1107 pPGSseq7G2 12. 123 .46 44.241 to ¡0.70-2.80-4. four QTLs were detected for LR-I and LR-III with 2.60 2.89–2.90 2.86 57. cLLS-I EL-II.59 37.0 11.32 2.70–6.260 to ¡0.94 55.8 TC4G02 Fig.4 23.37 40.64–6.20% PVE (Fig.9 GM618 148.66 34.209 ¡0. QTLrust02 and QTLrust04) were common between LR-I and LR-III stages of rust.00 TC3H02 0.80 4. bLLS-II EL-I.8 17. one QTL (QTLrust01) was detected for LR-III with 18.71 43.3 pPGSseq18A5 GM745 GI4925 TC2C07 79.
iLR-I ER-IV EII.90 2.59 4.105 ¡0. Interestingly.) were co-mapped in all three diVerent stages with PVE ranging from 33. QTLrust10 and QTLrust12).74 5.209 ¡0.00% and one QTL (QTLrust01) was present in the three components of rust. the potential and utility of this candidate marker IPAHM 103 was further validated using a set of resistant and susceptible germplasm lines with diVerent genetic background and another mapping population (TG 26 £ GPBD 4) segregating for rust resistance.20% PV.00 6. Therefore.53 0.258 ¡0.51 3. QTLrust06. eLR-V ER-III.50 2.60 4.522 to ¡1. identiWed through CIM analysis. hybrid derivatives from NcAc (North Carolina Accessions).20%.20–4.00 3.68 Phenotypic 2 variation (RP . Validation of a candidate marker: IPAHM 103 for molecular breeding of rust As IPAHM 103 marker was found associated with the major QTL (QTLrust01).143–0. QTLrust08.328 ¡0.22 2. jLR-II ER-IV EII.70–5.20 2. contributing up to 55.718 0. nine QTLs were identiWed for LR-I. lIP ER-V.90 a b Additive eVectB QTLrust01 QTLrust02 QTLrust03 QTLrust04 QTLrust05 QTLrust06 QTLrust07 QTLrust08 QTLrust09 QTLrust10 QTLrust11 QTLrust12 A d 6 1 2 3 7 8 4.22–3.87 3. QTLrust04. fLR-VI ER-III.50 4.20 2.04 2. cLR-III ER-II.120 0. this marker seems to be a candidate marker for deployment in MAS for molecular breeding.16 2. nIT ER-V. QTLrust04. QTLrust05.134 ¡0.00 2.978 Table 3 IdentiWcation of QTLs across environments for LLS in TAG 24 £ GPBD 4 population Theor Appl Genet (2010) 121:971–984 Scoring stage LLS-I QTL LG Marker interval Position (cM) 56 0 0 54 2 0 0 LOD value 3.10 1. LR-II and LR-III stages of rust with 1.80% and 3. three QTLs (QTL rust01.20–43. %) 6.70 2.60 5. though one of two fragments ampliWed by this marker behaves as a dominant marker. interspeciWc derivatives. In Rainy 2007 environment (ER-IV).80– 36.51 2.70–55. gLR-II ER-IV EI.132 ¡0.70–55.259 TC11A04-IPAHM524b TC1B02-TC9F04bcdefjkl TC4E09-IPAHM121i pPGSseq13E6-PM3j pPGSseq19G7-TC2C07 TC2G05-TC9H09i GM624-TC4G10dei PM434-TC4F02j TC9H09-GM624 k i 8 9 9 8 9 10 PM377-TC1A01l Superscripts on group of markers associated with QTLs represent environment and stages as follows: LR-III ER-I.80% PVE. LR-I ER-II. QTLrust03.10–5. In case of Rainy 2008 (ER-V) season.145 ¡0.94–3.47 3.10 Additive eVect ¡0.70 to 48. LR-IV ER-III.20% PVE. %) 1.86–4.143–0.355 ¡0.35–44. cultivars from South American landraces and advanced 123 .09 2. One QTL (QTLrust01) was co-mapped with LR-II and LR-III of EI accounted for 52.176 ¡0.90–55.20 1. QTLrust12) were identiWed with PVE ranging from 2.372 0. In this direction.155 QTLLLSQE01 QTLLLSQE02 QTLLLSQE03 1 9 13 1 5 9 13 gi-1107-pPGSseq7G2 TC2G05-TC9H09 TC5A07-IPAHM395 gi1107-pPGSseq7G2 PM179-GM633 TC2G05-TC9H09 TC5A07-IPAHM395 ¡ve sign indicates that favorable allele has come from resistant parent GPBD 4 +ve sign indicates that favorable allele has come from susceptible parent TAG 24 LLS-II QTLLLSQE01 QTLLLSQE04 QTLLLSQE02 QTLLLSQE03 Table 4 Features of QTLs for rust identiWed in the TAG 24 £ GPBD 4 population QTL Marker intervalA IPAHM103-pPGSseq19D6abcdefghijklmn PM436-Lec-1 bc LG Position (cM) 0–12 46 16 0–14 24 20 76 2 14 4 12 0 LOD Phenotypic 2 variation (RP .20 2.208 to ¡0.247 0.80 2.91 2. a set of 46 genotypes included released varieties. The resistant allele is contributed by the resistant parent GPBD 4 in Wve QTLs (QTLrust01.90 3. mLP ER-V. mutant lines. details of these abbreviations are given in “Materials and methods”as well as Table 1 B ¡ve sign indicates that the favorable allele has come from resistant parent GPBD 4 except for IP and LP. two QTLs (QTLrust01. QTLrust04. +ve sign indicates that the favorable allele has come from the susceptible parent TAG 24 LR-V and LR-VI with 2.51 2.70 2.32 3. QTLrust09 and QTLrust11) and TAG 24 for seven QTLs (QTLrust02. kLR-III ER-IV EII.80 6.20% PVE and all three stages of EII contributed 35.50% PVE.80–36.15 3.199 0. hLR-III ER-IV EI. QTLrust07.145 ¡0.24 3.148 0.30 2.80–7.323 0.
is very important because resistance is quantitative in nature PM183 0 10 20 30 40 50 60 70 80 90 123 . ICGV 87921. marker density. the type of mapping population.98–51. LR-IV phenotype scored at 105 DAS. Discussion QTL detection mainly depends on the biometrical methods to analyze.10– 48. Girnar 1. DiVerent environments and stages of phenotyping in the Wgure have been abbreviated as follows. LR-II phenotype scored at 80 DAS. IdentiWcation of resistant breeding material to major foliar diseases is one of the challenging objectives of groundnut breeders. In these analyses. X-axis shows the linkage group 6 (LG 6) with relative position of diVerent markers and Y-axis shows the LOD value for which QTL for rust has been detected for the above-mentioned environment/stage breeding lines were tested. and the dominating and defoliating nature of LLS make the visual selection for rust resistance very diYcult. therefore. Allelic data for the marker IPAHM 103 were obtained on these genotypes in this study. recent eVorts at the international level have facilitated development of a large number of SSR markers. 2009). a few groups have developed some good mapping populations. LR-VI phenotype scored at 120 DAS. LP latent period. ER-II Rainy 2005. ER-III Post-rainy 2007. ER-IV Rainy 2007.Theor Appl Genet (2010) 121:971–984 50 LR-I ER-II 979 45 40 35 LR-I ER-IV EII LR-II ER-IV EI LR-II ER-IV EII LR-III ER-I LR-III ER-IV EI LR-III ER-II LOD value 30 25 20 15 10 5 0 LR-III ER-IV EII LR-IV ER-III LR-V ER-III LR-VI ER-III LP ER-V IP ER-V IT ER-V pPGSseq19D6 IPAHM103 IPAHM272 PM50 Map Distance (in cM) Fig. LR-III phenotype scored at 90 DAS. 3 A snapshot showing the occurrence of a major QTL (QTLrust01) on LG 6 for rust based on phenotyping data obtained for diVerent stages of infection under Wve environments. Environments: ER-I Rainy 2004. Phenotyping data for all these 146 RILs were already available. A new mapping population (TG 26 £ GPBD 4) that segregates for rust was also used for validation of the candidate marker. Genotyping data and phenotyping data were subjected to CIM and SMA analyses. LR-V phenotype scored at 113 DAS. Similarly. population size and heritability of traits (Melchinger 1998). As a result. ICGV 86156) (ESM 1). IT infection type. Allelic data were obtained for 53 markers including IPAHM 103 for all 146 RILs of this mapping population. linkage mapping and QTL detection have now become possible in cultivated groundnut (Varshney et al. Stages: LR-I phenotype scored at 70 days after sowing (DAS). Phenotyping data for rust resistance were already available for these lines. Single marker analysis and CIM yielded similar results in both TAG 24 £ GPBD 4 and TG 26 £ GPBD 4 populations (Table 5). distinct marker proWles for these candidate markers were observed for resistant and susceptible lines for rust except in the case of six genotypes (VR5. ICGV 86590. The simultaneous occurrence of LLS and rust.96% PV in SMA. the marker IPAHM 103 was found as the nearest marker for the QTL that contributes 24. ER-V Rainy 2008. Based on the phenotypic and genotypic data. QTL analysis in groundnut was mostly thwarted in past because of the less number of SSR markers available in the public domain and lack of suitable mapping population with suYcient molecular and trait diversity. MN 1-28. Mapping of resistance genes to these diseases.90% PV in CIM analysis and 27. IP incubation period. However.
00 32. To achieve higher number of marker polymorphism between parental genotypes for developing good genetic maps. only 6. 2005. SSR markers.15% markers showed polymorphism between the parents.86 38.20 6.64 24. lesion size and lesion on the main stem for LLS. However. Precise phenotyping of traits is one of the paramount factors in QTL detection strategies.10 48. Polymorphism assessment and segregation distortion Although a variety of marker systems.46 39. Nimmakayala et al.70 36.80 33.95 54.01 37. 2004. IP incubation period. In general. Varshney et al. Out of 67 markers for which genotyping data were obtained..10 35. TAG 24 and GPBD 4.70 48. 2007.g.86 49. 2005.70 31. 2001 (68%). Motagi 2001. e.42 36.80 7. IT infection type and governed by recessive genes.91 32. EII experiment II. 1991) and RAPDs (Kochert et al. Therefore.90 resistance.980 Table 5 Estimates of phenotypic variation for IPAHM 103 using single marker analysis (SMA) and composite interval mapping (CIM) analysis in TAG 24 £ GPBD 4 and TG 26 £ GPBD 4 mapping populations Environmenta Stages of Scoring SMA 2 (RP .20 39.50 36. 1991.88 48. cultivated £ wild/synthetic genotypes. The low level of polymorphisms obtained in this set of genotypes was not unexpected as other diversity and mapping studies showed similar kind of results (Moretzsohn et al. number of pustule and pustule diameter for rust. The near normal to normal distribution revealed the quantitative nature of 123 . Ferguson et al. Hong et al. a very low level of polymorphism was observed that was mainly ascribed to the origin of cultivated groundnut by a single event of polyploidization and further isolation from wild relatives (Halward et al.10 43.60 36. it would be desirable to use either more appropriate marker systems such as SNPs or genetically diverse genotypes for developing mapping population (see Paterson et al. the present study was conducted to determine the location and eVects of QTLs for LLS and rust and identify the diagnostic marker(s) for deployment in breeding. %) CIM 2 (RP . were tested for DNA polymorphism in cultivated groundnut lines.50 55. 1991). e. 1987. relatively less SD can be attributed to low diversity nature of the parental genotypes (TAG 24 and GPBD 4) or to the use of a large size of the mapping population (268 RILs) employed in the present study. the power of QTL detection could be increased by phenotyping the mapping population for the components of resistance such as: incubation period.11 28.80 35.80 52. latent period.04 53. 2008. Since resistance to LLS and rust is complex with several components of resistance (Nevill 1982.58 33. Green and Wynne 1986. LR-I LR-III Abbreviation for diVerent environments (ER) are given in “Materials and methods” as well as in Table 1. 2009 (35%).40 30. However.8%) showed SD that was relatively less as compared to earlier mapping studies such as Burow et al. 2003.91 34.79 27.089) of SSR markers.70 36. higher SD is obtained in the mapping populations developed from highly diverse genotypes with less genome similarities.96 18.49 32. 20 (29. it is important to note that the use of distorted markers may aVect the estimation of map distances and the order of markers.90 34. Mace et al.98 33. 2006. Young et al.39 51.50 28. Varshney et al. Phenotypic evaluation The mapping population consisting of 268 RILs exhibited signiWcant variation in the resistance to LLS and rust.07 28.g.10 46. RFLP (Halward et al. 1996). 2004). 2005. Varma et al. even by using a large number (1.30 28. Dwivedi et al. EI experiment I.45 34. %) Theor Appl Genet (2010) 121:971–984 Mapping population-TAG 24 £ GPBD 4 ER-I ER-II ER-III LR-III LR-I LR-III LR-IV LR-V LR-VI ER-IV EI ER-IV EII LR-II LR-III LR-I LR-II LR-III ER-V IP LP IT Mapping population-TG 26 £ GPBD 4 ER-II ER-III LR-I LR-III LR-I LR-III LR-IV ER-IV EI LR-I LR-III LR-IV ER-IV EII a 19. LP latent period. 2009).59 40. Moretzsohn et al. Therefore. due to their multi-allelic nature on the other hand showed a relatively better polymorphism in cultivated groundnut (He et al.35 39.50 35. 2004. The magnitude of variation was moderate to high as revealed by phenotypic coeYcient of variation and with high to very high heritable variation. 2009).90 32.. High positive correlation between disease scores at diVerent stages and across environments revealed consistency in disease development in diVerent lines. 2005 (51%) and Varshney et al. 2002).10 24.
The present study presents a genetic map with 56 SSR marker loci and 14 linkage groups (LGs) that span 462. except for QTLrust01 that contributes 1. QTLLLS02.00% PVE.Theor Appl Genet (2010) 121:971–984 981 Genetic map and its comparison with the reference map Due to low level of polymorphism. low density of marker loci. In addition to the environmental conditions that vary according to seasons.230. which report identiWcation of small and medium eVect QTLs.20%).8 cM). However. six QTLs were co-mapped for LLS-I and LLS-II in one or the other environments (QTLLLS01. 20 LGs) and Varshney et al. which explained as large as 55. QTLLLS03 and QTLrust08. For instance.70–6. are of direct signiWcance to genetic improvement of cultivated groundnut. QTL analysis revealed four common QTLs (QTLLLS01 and QTLrust10. In general.89 cM) and Gobbi et al. For instance.50%) identiWed for LLS indicate governance of LLS resistance 123 .50% PVE) and one QTL (QTLLLS03. 2. The presence of major QTL accompanied by minor QTLs appears to be a common phenomenon in disease resistance studies (George et al. Hong et al. QTLLLS06. but the tetraploid maps. LG_AhIX (TAG 24 £ ICGV 86031) with LG 1 (TAG 24 £ GPBD 4). The results of the present study reconWrms that the genetics of resistance to rust and rust components are complex and controlled by both major and minor QTLs (Motagi 2001. The developed map in this study based on the RIL population (TAG 24 £ GPBD 4) was compared in detail with the SSR-based genetic map developed based on the RIL population (TAG 24 £ ICGV 86031) of Varshney et al. QTLLLS08 and QTLrust06. as mentioned earlier. QTLLLS03.70–7. Nair et al. ESM 2).70–7. Dwivedi et al. In the present investigation. the majority of QTLs identiWed were minor QTLs that were prone to inconsistency. Welter et al. indicated multiple recessive genes governing resistance in LLS (Sharief et al. 1978. QTLLLS07. the present map was found to be superior to the AFLP map of Herselman et al. QTLLLS10 and QTLrust01) conferring resistance to both diseases.20% of PVE and/or detected at high LOD scores (up to 44. 2003.210 cM. QTLrust04) were consistently present in all the three diVerent stages and in the majority of the environments. it was intriguing to Wnd a major QTL (QTLrust01). 2006 (754. However. 2001). Such inconsistencies have been found in other studies. in terms of marker density. in case of rust. 1990). 2007. but less dense than the RFLP map of Burow et al. Shuancang et al. such QTLs can be validated and used in markerassisted breeding. a total of 12 QTLs were detected and the majority of them were minor QTLs (1. QTLs for LLS and leaf rust As the expression of quantitative traits is inXuenced by environment. this map was compared with other tetraploid maps and.80% PVE) was observed across the environments. 2008 (679 cM.70–6. Classical genetic analyses. 2001 (2. 22 LGs). It is also important to mention that although some genetic maps were developed in past. As mentioned above. 3. These analyses indicate the possibility of preparing a combined (consensus) genetic linkage map of groundnut with higher marker density. Further.25 cM. but two QTLs (QTLrust01. as compared to diploid maps. 2002). The map coverage is much lower than diploid maps developed by Moretzsohn et al. near normal to normal phenotypic distribution of RILs and many small eVects QTLs (1. incomplete marker genotyping data and genotyping/phenotyping errors may be attributed for the appearance of small eVect QTLs. Paramasivam et al. 2005.80–4. 2005 (1. QTLLLS05. all these QTLs were minor as they contributed 1. It is quite possible that saturation of map can yield common QTL with large eVect that may be of interest to breeders.00% PVE). Therefore. speciWc to the screening environment (Ender and Kelly 2005). if both the maps are saturated with a suYcient number of common markers. 2004 (139.5 cM. conducted in the past on LLS and rust. In some cases. size of the population (Miklas et al. 2009). These consistent QTLs can be a future target for MAS and candidate gene approach.32) and was also consistent over environments. LG_AhIV with LG 2 and LG_AhIII with LG 8 showed good congruence (Fig. Comparison of these maps revealed only 28 common markers on these two maps. but map saturation can enhance the magnitude of eVects of these QTLs.50% of the phenotypic variation. Parallelism in the QTLs conferring resistance to LLS and rust Although no phenotypic correlation was found between LLS and rust phenotyping data.90–55. 1.270. The present study revealed 11 QTLs in the individual environment and four QTLs across environments associated with resistance to LLS. co-mapping of a few QTLs was observed. (2009). QTLs identiWed for the trait may vary for diVerent environments in which the trait is phenotyped. In the case of rust.4 cM. a good congruence was observed in marker orders on these maps.70–6. It is noteworthy here that these are mainly small eVects QTLs. 23 LGs). 5 LGs). Knauft 1987. there has been slow progress in developing genetic maps in groundnut and especially in cultivated groundnut. Similarly. 2009 (1. However. Nevill 1982) and as few recessive genes confer rust resistance (Kalekar et al. 1984. one major QTL (QTLrust01) contributed a considerable amount of variation toward the total PVE (6. these were not used to identify QTLs associated with biotic stresses. four QTLs were co-mapped at one or the other stages.24 cM with an average marker interval of 8.
Department of Biotechnology (DBT) of the Government of India (MVCG. Therefore. three additional markers. controlling the resistance or environmental variation. Similarly. MVCG). 4 Comparative mapping of IPAHM 103. interspeciWc derivatives. ICGV 87921. the present report of a QTL is the Wrst of its kind. Similarly in the case of rust. M 28-2. but some more additional markers were added between IPAHM 103 and pPGSseq19D6 (Fig. Validation of the nearest marker IPAHM 103 in germplasm lines of diVerent genetic background and mapping population indicated that it could be directly used for marker-assisted breeding for rust resistance in groundnut. Thanks are also due to Dr T. Validation of IPAHM 103 marker allele with respect to resistance The present study was undertaken to identify the QTLs for LLS and rust resistance. IPAHM 103 was strongly associated with resistance in the majority of cases (hybrid derivatives from NcAcs.39 55. normal phenotypic distribution of RILs. MN 1-28. Rahuri and the two anonymous reviewers for their valuable suggestions. L.80 49. VL 1 and VL 2 (resistance to rust) are mutants derived from Dharwad Early Runner (DER.60 pPGSseq19D6 IPAHM272 IPAHM282 IPAHM103 PM36 TC1D12 56. diagnostic marker for rust QTL (QTLrust01) and other linked markers between TAG 24 £ GPBD 4 and TG 26 £ GPBD 4 mapping populations. 4). the results of the present study corroborate that the genetics of these traits is complex in nature and is controlled by few major and large number of minor genes. Thus. However. aVecting disease development. validation of the marker IAPHM 103 was undertaken in diVerent genetic background with an objective to test the reliability of the marker. the presence and absence of the candidate fragment ampliWed by IPAHM 103 marker were observed with respect to rust reaction in DER based mutants. RKV) and the Tropical Legume I of Generation Challenge Programme (RKV) for Wnancial support to undertake this study. and reproduction in any medium. a major QTL explaining the quantum of phenotypic variation as much as 55. ICGV 86590. Furthermore. further eVorts in Wne mapping and map-based cloning of this locus are necessary to study candidate resistance genes and cellular pathways involved in resistance. pPGSseq19D6 and IPAHM 272 show collinearity between two maps. Hence. Acknowledgments Authors are thankful to the National Fund of the Basic and Strategic Research in Agriculture (NFBSRA) of the Indian Council of Agriculture (ICAR-RKV. QTL analysis provides putative location of potential genomic regions.74 81. Although several QTLs were identiWed in this study. When VL 1 was mutated using ethyl methane sulfonate (EMS).29 PM50 PM183 TC9B8 pPGSseq19D6 84.10 IPAHM272 Fig. mutant lines) except in few (VR 5.50 34.982 Theor Appl Genet (2010) 121:971–984 TAG 24 × GPBD 4 LG 6 0. Kulwal of Mahatma Phule Krishi Vidyapeeth (MPKV). Dr P. The order and orientation of anchor markers were congruent. Girnar 1. namely PM36. which was further compared with the map derived from another mapping population TG 26 × GPBD 4 LG 3 PM183 20. Three markers namely IPAHM 103. distribution.00 35. so that molecular markers associated with such QTLs can be used in MAS to accelerate disease resistance breeding.00 by many and small eVect loci. For instance. 123 . ICGV 86156) germplasm lines. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use. only one major QTL (QTLrust01) was identiWed for resistance to rust that can be used in MAS.70 25. Nepolean of the Indian Agricultural Research Institute (IARI).20% for the QTL of rust has been reported here. TC1D12 and TC9B8 have been integrated in the QTL (QTLrust01) region in TG 26 £ GPBD 4 mapping population (TG 26 £ GPBD 4).00 IPAHM103 0. as well as additional minor genes. For this purpose. the appearance and disappearance of one of two fragments ampliWed by the marker IPAHM 103 with resistance and susceptibility indicate the possibility of association of the marker with the resistance gene itself. Though QTLs for LLS are minor (<10% PVE). a variety of highly diverse germplasm lines derived from multi-crosses and multi-parent were used to check the presence of resistance allele in resistant genotypes and vice versa. Intriguingly. susceptible to rust). New Delhi.20% PV indicate the possibility of oligogenes.30 75. Mutant 110 and Mutant 45 were obtained but with susceptible rust reaction (ESM 1). Conclusions The present study yielded partial linkage map of groundnut and QTLs for LLS and rust. VB 9. A major QTL (QTLrust01) for resistance to rust was mapped on LG6. provided the original author(s) and source are credited. Therefore. further studies are needed to narrow down the marker interval of the identiWed QTL by saturating the genetic map using more polymorphic markers. small eVect QTLs and one major QTL (QTLrust01) contributing as much as 55.
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If you as a customer contact us via WhatsApp in order to ask business-related questions, e.g. the status of an order, the availability of goods or the receipt of a payment any data processing initiated on the part of the company, such as storing the telephone number and answering the request, regularly by Art. 6 para. 1 lit. b DSGVO, without requiring the prior consent of the enquirer. The data processing operations of the communication to be initiated serve directly to initiate and/or execute a contractual relationship. The same applies to the provision of further data (e.g. e-mail address, customer number, etc.) for the purpose of allocation to an order or identification.
If you, as a customer or interested party, use the WhatsApp number of Auktionshaus Felzmann in order to contact the Auktionshaus Felzmann with regard to general enquiries not relating to a specific business, the storage of the telephone number and its other processing for answering the enquiry is regularly based on our authorized interested party in accordance with Art. 6 Para. 1 lit. f DSGVO and does not require any special consent. In this case, the predominance of the entrepreneurial interest over the data subject's interest in the integrity of his data is safeguarded because you initiated the contact to our company yourself via WhatsApp and therefore implicitly initiated and agreed to the processing of your data to answer the inquiry.
PayPal is an online payment service provider. Payments are processed via PayPal accounts, which are virtual private or business accounts. PayPal also offers the option of processing virtual payments via credit cards if a user does not have a PayPal account. A PayPal account is managed via an e-mail address, which is why there is no classic account number. PayPal makes it possible to trigger online payments to third parties or to receive payments. PayPal also acts as a trustee and offers buyer protection services.
The European branch of PayPal is PayPal (Europe) S.à.r.l. & Cie. S.C.A., 22-24 Boulevard Royal, 2449 Luxembourg, Luxembourg.
Auktionshaus Felzmann GmbH & Co KG has integrated PayPal components as an additional payment option on its website. If you as a customer select "PayPal" as payment option during the ordering process in our online shop, personal data is automatically transmitted to PayPal. By selecting this payment option, you consent to the transfer of personal data required for payment processing.
The personal data transmitted to PayPal is usually first name, surname, address, email address, IP address, telephone number and/or other data necessary for payment processing. For the completion of the sales contract such personal data is necessary which stands in connection with the respective order.
The transmission of the data is for the purpose of payment processing and fraud prevention. Auktionshaus Felzmann will transmit personal data to PayPal in particular if a justified interest for the transmission is given. The personal data exchanged between PayPal and Auktionshaus Felzmann may be transmitted by PayPal to credit agencies under certain circumstances. The purpose of this transfer is to check identity and creditworthiness.
PayPal may transfer the personal data to affiliated companies and service providers or subcontractors if this is necessary to fulfill the contractual obligations or if the data is to be processed on behalf.
You have the option to revoke your consent to the handling of personal data at PayPal at any time. A revocation has no effect on personal data that must be processed, used or transmitted for the (contractual) processing of payments.
We reserve the right to occasionally adapt this data protection declaration so that it always meets the current requirements or to implement changes to our services in the data protection declaration. The new data protection declaration will then apply for your next visit.

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