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. 2021 Jan 23;11(2):91. doi: 10.1007/s13205-020-02638-y

Mapping QTLs for Alternaria blight in Linseed (Linum usitatissimum L.)

Neha Singh 1, Rajendra Kumar 2, Sujit Kumar 3, P K Singh 4, Hemant Kumar Yadav 1,5,✉
PMCID: PMC7826323  PMID: 33520577

Abstract

A SSR-based linkage map of linseed constructed based on 154 individual lines of F2 mapping population derived from JRF-4 (disease-resistant) and Chambal (disease susceptible) genotypes. QTLs for Alternaria blight and other yield related traits identified. Out of 1720 SSRs, 216 SSRs were found polymorphic among the parents but due to segregation distortion 18 SSRs could not be used for linkage map construction. Total 191 SSRs were used to construct the linkage map and distributed in 15 linkage groups covering genome length of 1802.4 cM. A total of 10 QTLs were identified for 4 phenotypic traits including 4 QTLs for capsules/plant, 2 for capsule weight/plant, 2 for seed weight/plant and 2 for Alternaria blight resistance. This study laid a foundation for further validation and fine mapping with more advance and large set of marker for different QTL identification and marker-assisted selection in linseed.

Electronic supplementary material

The online version of this article (10.1007/s13205-020-02638-y) contains supplementary material, which is available to authorized users.

Keywords: Alternaria blight, RILs, SSRs, QTLs

Introduction

Oilseed crops are highly valuable agricultural trade materials due to their economical and nutritional values. Presently, the major oilseed crops at global level are Soybean (Glycine max), oilseed rape/canola (Brassica napus) and sunflower (Helianthus annus). However, the continuously increasing world population, decrasing biodiversity and accelerated agricultural land degradation require a sustainable crop production including oilseeds. With respect to this growing interest, an enhancement in production of alternative oilseed crop is required in addition to the conventional crops. Linseed/Flax is considered to be a good example for an alternative oilseed crop. The linseed (Linum usitatissimum L., 2n = 2x = 30) is a dual-purpose crop utilized for both its oil and fibre. Linseed oil embraces lots of health beneficial nutritional components. A high level of omega-3 fatty acids, i.e. ALA (alpha-linolenic acid) is present in its oil having considerable benefit to humans and animals (Singh et al. 2019). The omega-3 fatty interferes with the effect of estrogen and helps in the cure of heart disease, strokes, and certain types of cancer (Flax Council of Canada; https://flaxcouncil.ca/). There are several biotic and abitioc stresses which severaly affects productivity of linseed. Among these, Alternaria blight is one of the major biotic stresses in linseed limiting crop yield in hot and humid environments (Singh and Singh 2004, 2005). Inspite of lack of truly resistant lines of linseed in genepool, there are few moderately resistant varieties have been reported (Singh et al. 2008). The conventional breeding approaches for disease-resistant varietal development involves laborious field trials and critical phenotypic observations. However, the new molecular tools, such as QTLs, linked to the target traits help in precise selection and pyramiding of favourable alleles to develop new varieties in shorter period of time. The marker-trait associations have been successfully deployed in various plant breeding programs using various types of molecular markers (Wu et al. 2008; Khan et al. 2012; Welter et al. 2007; Khedikar et al. 2010). Further, among all available molecular markers, simple sequence repeat marker (SSRs) is one of the most popular and versatile markers which is used for genetic studies due to several desirable features, such as high abundance, locus specificity, codominant inheritance, high polymorphism information content and reproducibility (Collard et al. 2005). Various molecular markers have been used for linkage map construction and QTL identification in linseed. Spielmeyer et al. (1998) constructed the first linkage map based on AFLP markers and identified two QTLs for Fusarium wilt Later, RAPD, RFLP (Oh et al. 2000), SSRs (Cloutier et al. 2011; Cloutier et al. 2012a, b) were utilized to construct a linkage map and QTL identification for important traits in linseed. Kumar et al. (2015) used SSR and SNP to identify QTLs for days to maturity and yield based on Canadian genotypes. Few SNP-based high-density linkage maps have also been constructed (Yi et al. 2017; Xie et al. 2018; Zhang et al. 2018). Besides, only one linkage map based on Indian genotypes of linseed has been reported (Chandrawati and Yadav 2017). It is also important to mention that none of the earlier developed genetic maps of linseed were used to identify QTLs associated with Alternaria blight. Therefore, a bi-parental F2 mapping population consisting 154 indiviuals was developed from the cross of genotype JRF-4 and Chambal to construct linkage map and identify QTLs. The parent JRF-4 is a fiber-type and Alternaria blight-resistant genotype while parent Chambal is oil-type and Alternaria blight-susceptible genotype (Singh et al. 2008). Besides response towards disease, these parental lines also differed in other traits, such as days to 50% flowering, capsules/plant and seed weight/plant. In present study, this F2 mapping population was used to develop a SSR-based linkage map and identification of QTLs for Alternaria blight and other agronomic traits in linseed.

Materials and methods

Development of mapping population and phenotypic evaluation

The F2 mapping population comprising 154 plants along with parental lines were grown in field at C.S.A. University of Agriculture and Technology, Kanpur during the crop year 2014–2015. Data on days to 50% flowering, plant height, branches/plant, capsules/plant, capsule weight/plant, seed weight/plant, number of seeds/capsule and test weight were recorded. The morphological screening of F2 plants was carried out for Alternaria blight response along with their parents. This disease attacks on all the aerial parts of the plant. On the leaves, the disease appears as small, dark brown spots which results in their twisting and drying. In severe infection, plant may die. In present experiment, Alternaria blight screening was done at flowering (Stage 1) and harvesting (Stage 2) stage using 0 to 5 scale (Tiwari et al. 2012) (Supplementary Table S1) based on percent damage area of leaf.

SSR genotyping

The genomic DNA was extracted using the DNeasy plant mini kit (Qiagen, Valencia) as per manufacturer’s protocol. The quality of DNA was assessed by electrophoresis in 0.8% agarose gel and the concentration was determined with flurometer (Qubit, Invitrogen). Finally, the DNA was adjusted to 10 ng/µl for PCR amplification. A total of 1720 SSR primer pairs, consisting 1323 newly synthesised SSRs and 397 previously reported SSRs (Cloutier et al. 2012b), were used for polymorphism screening between parental genotypes. The newly synthesised 1323 SSRs were extracted from whole-genome sequence of flax consisting of 88,429 scaffolds (www.phytozome.net). These downloaded sequneces were then subjected to SSR searching tool, i.e. MISA (http://pgrc.ipk-gatersleben.de/misa) with minimum 8 repeats units for dinucleotide, 5 for trinucelotide and 4 each for tetranucleotide, penta and hexanucleotide. The identified SSR-contating sequences were then subjected for primer designing through PRIMER3 (http://bioinfo.ut.ee/primer3-0.4.0/) with major criteria as: length of 20–26 bp; melting temperature 55–65 °C and length of PCR product 100–400 bp. Following the genotyping method of Schuelke (2000), all the forward primers were synthesized with additional 18 base tag (5′ TGTAAAACGACGGCCAGT 3′) as M13 tail. The PCR amplification was carried out in 10 µl reaction volume consisting of 10 ng of genomic DNA, 1X PCR master mix (AmpliTaq Gold®, Applied Biosystems, USA), 0.1 µl (5 pmol/µl) of forward primer (tailed with M13 tag), 0.3 µl (5 pmol/µl) each of both, reverse primer and M13 tag (labelled with either 6-FAM, VIC, NED and PET). The PCR amplification was performed using following condition: initial denaturation at 95 °C for 5 min followed by 36 cycle of 94 °C for 30 s, 50–55 °C (primer specific) for 45 s and 72 °C for 1 min. Subsequently, 10 cycles of denaturation for 30 s at 94 °C annealing for 45 s at 53 °C, extension for 45 s at 72 °C followed by final extension for 15 min at 72 °C were performed. The amplified PCR products were first checked on 1.5% agarose gel for amplification, then post-PCR pool prepared based on fluorescence labeled primers. For post-PCR pooling, 1 µl each of 6-FAM, VIC, NED and PET-labeled PCR product with different SSRs were combined with 10 µl of water. Then, 10 µl Hi-Di formamide containing 0.25 µl GeneScanTM 600 LIZ® as internal size standard was added to above mentioned pooled PCR product. Furthermore, denaturation of this mixture was done at 95 °C for 5 min, quick-chilled on ice for 5 min and loaded on ABI 3730xl DNA Analyzer for capillary electrophoresis. To obtain allele size, the raw data were analyzed by GeneMapper v4.0 software (AppliedBiosystems, Foster City, CA, USA).

Linkage map construction and QTL mapping

The alleic data of SSR genotying of mapping population was subjected to segregation analysis by Chi-square test for goodness of fit. For linkage mapping, the genotypic data showing Mendelian segregation were selected. The linkage map was constructed with MAPMAKER v3.0 (Lander et al. 1987) using genotypic data of 154 F2 plants. The linkage groups were established at maximum of 50% recombination and a LOD of 3.0. The marker position within a linkage group was determined with the ‘RIPPLE’ command and the ‘GROUP’command was used for the best marker order of the linkage group. Map distance was calculated using Kosambi mapping function (Kosambi 1994) and centimorgan unit was used. Graphic representation of linkage groups was drawn using software MapChart version 2.2 (Voorrips 2006). The QTL mapping was conducted through Cartographer version 2.5 (Wang et al. 2007) using single QTL model in combination with composite interval mapping (CIM). A stringent LOD threshold > 2.5 for the detection of QTL was calculated based on 1000 permutation at P < 0.05. The relative contribution of a genetic component (R2) was calculated as proportion of the phenotypic variance explained (PVE). The QTL explaining more than 10% phenotypic variation (PV) was considered as major QTL.

Candidate gene prediction

Based on the gene annotation, available at Phytozome v12.1 (https://phytozome.jgi.doe.gov/jbrowse) database, predictions were made for possible candidate genes within the major QTL region related to Alternaria blight disease resistance.

Results

Phenotypic variability

The phenotypic data of 7 different traits were collected in the segregating F2 population including the parental lines. The minimum, maximum and mean value of phenotypic traits are presented in Table 1. The parental lines, i.e. JRF-4 and Chambal differed significantly from each other for Alternaria blight response along with other agronomic traits, namely days of 50% flowering, plant height, branches/plant, capsules/plant, capsule weight/plant and seed weight/plant. On the scale of 0–5 disease score, the parent JRF-4 scored as 1 indicating its resistance towards disease while the parent Chambal had a score of 4 which shows its high susceptibility towards Alternaria blight. The F2 mapping population revealed presence of high variability and normal distribution of population for different phenotypic traits except day to 50% flowering and seed weight/plant (Fig. 1).

Table 1.

Phenotypic trait variability of parental lines and mapping population

Trait JRF-4 Chambal F2 population
Min Max Mean ± SD
Days to 50% flowering 105.0 75.0 55.0 131.0 97.1 ± 18.5
Plant height 110.2 65.6 43.0 130.0 87.1 ± 16.0
Branches/plant 3.1 6.5 3.0 13.0 7.5 ± 2.2
Capsules/plant 55.3 110.7 62.0 225.0 150.8 ± 32.5
Capsule weight/plant 8.0 12.5 5.0 18.8 11.4 ± 4.1
Seed weight/plant 5.2 9.0 3.0 15.6 8.2 ± 3.6
Alternaria blight response 1.0 4.0 0.0 5.0 2.5 ± 1.1
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Fig. 1.

Fig. 1

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Frequency distribution of 6 quantitative traits in F2 mapping population of linseed. (DOF- Days of 50% flowering. PH Plant height, BP Branches/plant, CP Capsule/plant, SW Seed weight/plant, AB Alternaria blight resistance). Red arrow showing trait value for parent JRF and green arrow for parent Chambal

SSR polymorphism and segregation analysis

In the present study, 1720 SSR markers were used for polymorphism screening between parental lines. Out of 1720 SSRs screened, 216 (12.5%) SSRs produced clear and consistant polymorphism between the parental lines and were used for genotyping of 154 individual F2 lines. Furthermore, the genotypic data of 216 SSRs with 154 F2 plants were checked for segregation ratio using chi-square test. The 18 SSRs showed segregation distortion from Mendelian segregation ratio due to which they were not included for further analysis. Therefore, the genotyping data of 198 SSRs was used for the construction of linkage map and QTL identification.

Construction of Linkage map and QTL identification

All the 198 SSRs were used in the linakge analysis and of which 191 (Supplementary Table S2) were assisgned to 15 linkage groups (LG 1–LG 15) at LOD > 3.0 (Fig. 2) corresponding to haploid chromosome number of linseed while 7 markers remained unlinked. This linkage map with 191 SSRs covered a total genome length of 1802.4 cM. The numbering of linkage groups from LG 1 to LG 15 was based on descending order of their total map length in cM. The linkage group 1 encompasses the largest number of markers with 20 markers covering a map length of 290.0 cM while linkage group 15 was the shortest wih 7 markers covering a map length of 25.6 cM. The marker distance on the map also varied greatly across the different linkage groups. The minimum and maximum inter-marker distances observed were 0.2 cM and 45.0 cM, respectively, with an average marker distance of 10.2 cM. The markers’ distribution was random and uneven as revealed by the average marker density on each linkage group. The linkage map obtained in the present study was also compared with the earlier developed genetic map of linseed by Cloutier et al. (2012b) and Chandrawati and Yadav (2017). The comparison of these maps showed 110 common markers while remaining 81 markers were novel. Furthermore, the marker distribution on linkage group revealed that out of 110 previously reported SSRs, 35 SSRs were mapped on same linkage group while remaining 75 SSRs were present in different gropus.

Fig. 2.

Fig. 2

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Linkage map of linseed derived from 154 F2 lines of JRF-4 X Chambal mapping population comprising 191 SSRs. Different colored blocks represent the QTLs on their respective linkage group. Qcp: QTL for capsules/plant, Qcwp: QTL for capsule weight/plant, Qsw: QTL for seed weight/plant, Qabr: QTL for Alternaria blight resistance, nbri: National Botanical Research Institute)

The QTL mapping analysis revealed 10 putative QTLs for four agronomic traits, namely capsules/plant, capsule weight/plant, seed weight/plant and Alternaria blight (Table 2). No QTLs could be identified for days to 50% flowering, plant height and branches/plant. Out of 10 QTLs, 4 QTLs were for capsules/plant (Qcp.nbri.2.1, Qcp.nbri.6.1, Qcp.nbri.7.1 and Qcp.nbri.14.1) at LOD of 4.2, 3.1, 3.0 and 5.1, respectively. The Qcp.nbri.2.1 was closest to marker LuSc_270_01 and present at position of 120.6 cM on LG 2. The Qcp.nbri.6.1 was positioned at LG 6 at a position of 15.2 cM closest to marker LuSc_629_4. The Qcp.nbri.7.1 was positioned at 87.6 cM on LG 7 nearer to the marker LuSc_257_01. The Qcp.nbri.14.1 was present at 1.0 cM on LG 14 closest to marker LU_304. The proportion of PVE (Phenotypic Variance Explained) by individual QTLs of capsules/plant ranged from 5.6% to 8.6%. Four QTLs, i.e. Qcwp.nbri.7.1, Qcwp.nbri.9.1 for capsule weight/plant Qsw.nbri.7.1 and Qsw.nbri.9.1 for seed weight/plant were identified at LOD 3.8, 3.8, 3.9 and 4.3, respectively. These two QTLs were found to be common for both the traits. Therefore, the QTLs Qcwp.nbri.7.1 and Qsw.nbri.7.1 were same and marked at 42.2 cM on LG 7 with closest marker LU_a58. Also, the QTLs Qcwp.nbri.9.1 and Qsw.nbri.9.1 were same and marked at 12.5 cM on LG 9 with closest marker LU_934. The PVE for Qcwp.nbri.7.1 and Qcwp.nbri.9.1 was 9.2 and 10.5, respectively. While, the PVE of Qsw.nbri.7.1 and Qsw.nbri.9.1 was 9.5 and 1.0, respectively. Two QTLs for Alternaria blight resistance were detected, i.e. Qabr.nbri.14.1 at LOD 3.0 and Qabr.nbri.14.2 at LOD 4.0. The Qabr.nbri.14.1 was detected at 3.0 cM on LG 14 having closest marker Lu_3043. The Qabr.nbri.14.2 was also marked on LG 14 at 10.5 cM wih closest marker Lu_207F198. The PVE for Qabr.nbri.14.1 was 9.2% and for Qabr.nbri.14.2 was 4.2%.

Table 2.

List of QTLs identified JRF-4 X Chambal mapping population through single-locus analysis for 4 phenotypic traits

Trait QTL name LG Position Closest Marker LOD PVE
Capsules/plant Qcp.nbri.2.1 2 120.6 10 (LUSc_270_01) 4.5 8.2
Qcp.nbri.6.1 6 15.2 3 (LUSc_629_4) 3.1 6.0
Qcp.nbri.7.1 7 87.6 9 (LUSc_257_01) 3.0 8.6
Qcp.nbri.14.1 14 1.0 1 (LU_3043) 5.1 5.6
Capsule weight/plant Qcwp.nbri.7.1 7 42.2 4 (LU_a58) 3.8 9.2
Qcwp.nbri.9.1 9 12.5 2 (LU_934) 3.8 10.5
Seed weight/plant Qsw.nbri.7.1 7 42.2 4 (LU_a58) 3.9 9.5
Qsw.nbri.9.1 9 12.5 2 (LU_934) 4.3 1.0
Alternaria blight resistance Qabr.nbri.14.1 14 3.0 1 (LU_3043) 3.0 9.2
Qabr.nbri.14.2 14 10.5 3 (LU_207F198) 4.0 4.2
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The candidate gene prediction analysis was performed in the genomic region surrounding the QTLs identified for Alternaria blight resistance, i.e. Lu 3043 and Lu207F198. Among these two QTLs, only Lu3043 annotated to hsp_1 (heat shock proteins) present at physical distance of 272861–272908 bp on scaffold 38 of L. ussitatissimum genome available on Phytozome v12.1 (Supp Fig. 1). On the basis of LD decay at long physical distance, extension of selected genome region upto 250 kb upsteam and downstream of Lu3043 was made. A large number of heat shock proteins were found within this 500 kb genomic region.

Discussion

Various molecular markers, such as RFLP, RAPD, SSR and SNP, have been used for linkage map construction and QTL identification in linseed. The first linkage map was constructed based on AFLP markers covering approximately 1400 cM distance with 10 cM average spacing between markers across the 18 linkage groups. Later, RAPD and RFLP markers were used to construct a linkage map having 15 linkage groups with 1000 cM genetic distance. However, the application of these maps was limited due to labor-intensive and unreliable nature of used markers. Furthermore, Cloutier et al. (2011, 2012b) constructed an SSR-based linkage map in linseed which covered 833.8 cM and 1551 cM genetic distance, respectively. Chandrawati and Yadav (2017) also developed a linkage map through 141 SSRs with 2074.2 cM genome coverage. Zhang et al. (2018) utilized 4497 SNP markers to develop a linkage map of 1138 cM distance with an average marker density of 2.71 cM. In the present study, low level of polymorphism was noticed (out of 1720 SSRs, only 216 polymorphic SSRs) which might be due to SSRs with short number of repeats and less diversity between the parental lines used. Generally, the SSRs with higher number of repeats were found to be more polymorphic than those of shorter ones (Ellergren 2004). The segregation distortion observed between polymorphic SSRs was 8.3% which was found to be less than the earlier studies in linseed (Spielmeyer et al. 1998; Cloutier et al. 2011; Chandrawati and Yadav 2017). Hackett and Broadfoot (2003) suggested that the population size influences the segregation distortion when two markers are separated by more than 10 cM. However, this is not always true as Cloutier et al. (2011) observed about 27% of segregation distortion in previous linseed linkage map developed with RILs population. Thus, it may be inferred that population type is not the only factor responsible for distortion but other factors, such as chromosome loss during the process of crossing over (Kasha and Kao 1970), isolation mechanism (Zamir and Tadmor 1986) or the presence of other alien genes (Hendrick and Muona 1990). Here, in the present study, a genetic linkage map with 191 SSRs covering a total length of 1802.4 cM was constructed. The genetic distance of this map and average marker density (10.2 cM) were relatively higher than earlier reports (Spielmeyer et al. 1998; Oho et al. 2000; Cloutier et al. 2011) which might be due to less number of marker and F2 mapping population. It is also important to mention that only two of the earlier developed genetic maps in linseed were used to identify QTLs associated with biotic stress, i.e. for fusarium wilt (Spielmeyer et al. 1998) and powdery mildew (Asgarinia et al. 2013).

The identification of QTLs or genomic regions associated with different phenotypic traits makes conventional breeding strategies more precise and time-saving for varietal development. In linseed, the QTL identification has progressed with the deployement of different type of molecular markers, from RFLP and RAPD (Speilmeyer et al. 1998; Oh et al. 2000) to SSRs (Cloutier et al. 2012a, b; Chandrawati and Yadav 2017) and SNPs (Wu et al. 2018). Here, we report first SSR linkage map-based QTL identification for Alternaria blight in linseed. The linkage map developed in this study also identified QTLs for capsule/plant, capsule weight/plant, seed weight/plant. Futhermore, the QTLs identified for capsule weight/plant and seed weight/plant were co-localized on LG 7 and LG 9 at 42.2 cM and 12.5 cM, repectively. One QTL for capsules/plant and Alternaria blight resistance was also clustered on same linkage group, i.e. LG 14 at 1.0 and 3.0 cM, respectively. The QTL clusters having more than one trait in common, such as capsule weight/plant and seed weight/plant, in the present study may have multiple effects on each other as they belong to the same genomic region (Verma et al. 2015). The clustering of QTLs can arise due to pleiotropic effect of a single regulatory gene (Aastveit and Aastveit 1993). Kumar et al. (2015) also reported that QTLs for cell wall, straw weight, seeds/boll, yield and days to maturity in linseed were co-located on LG 4. All the QTLs identified in present study showed minor effect on their respective traits with PVE of less than 9% except Qcwp.nbri.9.1 (PVE ~ 10.5%). However, one QTL each for capsule weight/plant (Qcwp.nbri.7.1), seed weight/plant (Qsw.nbri.7.1) and Alternaria blight resistance (Qabr.nbri.14.1) explained considerable phenotypic variation and thus could have potential to affect their respective traits. On the contrary, Cloutier et al. (2011) identified QTLs with comparatively high degree of phenotypic variations (13–42%) for linolenic acid, linoleic acid, iodine value and palimitic acid. The low percentage of variability explained by QTLs associated with Alternaria blight might be due to complex nature of disease resistance controlled by both major and minor QTLs simultaneously. The presence of major QTL accompanied by minor QTLs appears to be a common phenomenon in disease-resistance studies (Welter et al. 2007; Shuancang et al. 2009). Also, the low PVE of QTLs identified for remaining traits could be due to the environmental conditions that vary according to seasons, size of the population (Miklas et al. 2001), low density of marker loci, incomplete marker genotyping data and genotyping/phenotyping errors (Khedikar et al. 2010).The annotation of Lu3043 with heat shock proteins revealed that HSPs might be regulating Alternaria blight disease resistance in linseed. Park and Seo (2015) also reported that HSPs play an important role in innate immunity response in plant cells. Further studies, for example, linkage map construction by integrating more number of markers on large, multiple and advance mapping population, such as recombinant inbred lines, would assist in precise and efficient QTLs identification for different traits. Also, as suggested by Asgirinia et al. (2013), utilization of SNP markers will help in more accurate identification of QTLs in the chromosomal regions.

Conclusion

The genetic linkage map constructed with 191 SSRs provides a framework structure to further enrich and refine for better understanding of marker-trait association. The QTLs identified for different traits could be validated later and some of the validated QTLs would be helpful for transferring into desired genotypes for further improvement in faster and precise way. Also, the QTLs identified for Alternaria blight should be further validated under multiple environments due to its polygenic and complex nature of inheritance.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pptx 854 kb) (853.3KB, pptx)
Supplementary material 2 (DOCX 51 kb) (50.9KB, docx)

Acknowledgements

Authors thank Director, CSIR-NBRI, Lucknow for facilities and encouragement. This work was financially supported by the U.P. Council of Agricultural Research (India).

Author contributions

NS performed the experiments and prepared the manuscript, HKY and NS analyzed the data. RK, SK, PKS and HKY designed, coordinated the experiments and improved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest in the publication.

Footnotes

Any molecular data accession numbers or culture collection numbers for new taxa not reported here.

Contributor Information

Neha Singh, Email: neha.bhu07@gmail.com.

Rajendra Kumar, Email: rajendra64@yahoo.co.in.

Sujit Kumar, Email: sujit3773@gmail.com.

P. K. Singh, Email: pk_singh65@yahoo.com

Hemant Kumar Yadav, Email: h.yadav@nbri.res.in.

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