Submit or Track your Manuscript LOG-IN

Identification and Genotyping of SNPs in RKM1 and RKM4 Genes of Sordaria fimicola

PJZ_54_2_529-535

Identification and Genotyping of SNPs in RKM1 and RKM4 Genes of Sordaria fimicola

Iqra Mobeen1*, Rabia Arif1*, Maimoona Ilyas1, Siu Fai Lee2 and Muhammad Saleem1

1Molecular Genetics Research Laboratory, Department of Botany, University of the Punjab, Lahore 54590, Pakistan

2Department of Biological Sciences, Macquarie University and CSIRO Black Mountain Laboratories, Building 101, Clunies Ross Street, ACT 2601, Australia

ABSTRACT

Single nucleotide polymorphisms (SNPs) are one of the most common and abundant class of molecular markers present in the genome of many organisms. The current study represents the first attempt to investigate the natural variations in the RK-MTases genes; Ribosomal N-lysine methyltransferase1 (RKM1) and Ribosomal N-lysine methyltransferase4 (RKM4) in Sordaria fimicola using SNP markers. A total seven SNPs in the RKM1 gene and nine in RKM4 gene were identified. A subset of SNPs were unique in SFS strains and others were fixed in the NFS strains of S. fimicola. These polymorphisms might be adaptive in stressful environmental conditions. Genotyping of eight SNPs of RK-MTases genes of S. fimicola was accomplished by designing allele specific primers via amplification refractory mutation system–PCR (ARMS-PCR) yielding amplicons of different sizes. This study concluded that SNP markers are an efficient and informative marker system in S. fimicola. Most of the studied SNPs are non-synonymous substitutions, which might underpin functional differences in their protein products.


Article Information

Received 02 September 2019

Revised 22 December 2019

Accepted 20 February 2020

Available online 09 February 2021

(early access)

Published 03 January 2022

Authors’ Contribution

IM did experimental work and wrote the manuscript. RA and MI Helped in experimental work. SFL reviewed the paper. MS supervised the research.

Key words

Single nucleotide polymorphisms, RK-MTases, ARMS-PCR, Sordaria fimicola, NFS strains, SFS strains

DOI: https://dx.doi.org/10.17582/journal.pjz/20190902090906

* Corresponding author: [email protected]; [email protected]

0030-9923/2022/0002-0529 $ 9.00/0

Copyright 2022 Zoological Society of Pakistan



INTRODUCTION

From last two decades, single nucleotide polymorphisms (SNPs) have become the most popular molecular marker system to study polymorphisms in natural populations of numerous organisms (Väli et al., 2008; Coates et al., 2009; Ljungqvist et al., 2010; Guichoux et al., 2011; Fischer et al., 2017). SNPs are the most abundant type of molecular marker and can be identified in animals, plants and as well as fungi. Their abundance make them ideal for the study of inheritance of genomic regions including exonic and intronic regions (Berger et al., 2001; Wicks et al., 2001; Stickney et al., 2002).

SNP is one of the simplest and most common forms of polymorphism which arises due to the substitution of one nucleotide with the other nucleotide (Shastry, 2002). Due to the environmental stress, gene conversion, and deficiency in the DNA repair mechanisms. These variations are driving force of species evolution and adaptation (Lamb et al., 1998; Hoffmann and Hercus, 2000; Saleem et al., 2001).

Owing to their abundance, SNPs are present in the frequency of approximately one in every kilobase in the human genome (Brookes, 1999). These simplest forms of genetic variation are more in the non-coding regions, with less deleterious effects. Those SNPs, which do not change the encoded amino acids are known as synonymous substitutions and are usually not involved in natural selection (Kimura, 1983). In contrast, SNPs that alter the encoded amino acids are recognized as non-synonymous substitutions and are more likely to be under natural selection. SNPs can be observed between individuals in a population, may change the promotor activity, influence the DNA and pre mRNA conformation as well as change the phenotypic expression (Lamb et al., 1998; Hoffmann and Hercus, 2000; Saleem et al., 2001).

SNPs are present twice as frequent in non-coding and intergenic regions than in coding regions of the genome (Zhao et al., 2003). Moreover, genome wide studies depict that SNPs of non-coding regions are physically associated with functional regions of genome (Kim et al., 2007). These days, automated next generation sequencing make the SNPs detection and genotyping straightforward (Kaiser et al., 2016). In addition, current SNP based studies generally need to bear high upfront costs in SNP discovery (Chen et al., 2008; Lai et al., 2007) and then genotype them in target organisms (Van Orsouw et al., 2007; Van Tassell et al., 2008).

For low and medium throughputs SNPs genotyping PCR is most commonly used (Chuang et al., 2008). There are many PCR methods available for this purpose but selecting the suitable one is critical key factors that taken into account include the nature of polymorphism, number and type of samples, and availability of appropriate instrument for the desired sensitivity and throughput levels (Hamajima et al., 2002).

A very simple and cost-effective method for SNP genotyping is tetra-primer amplification refractory mutation system–PCR (ARMS-PCR), which requires only a PCR reaction followed by gel electrophoresis (Ye et al., 2001). We applied ARMS-PCR approach to study genetic variation in the fungus Sordaria fimicola, targeting SNPs in RKM1 and RKM4 genes.

MATERIALS AND METHODS

Sub-culturing of fungi

S. fimicola strains (S1, S2, S3, N5, N6 and N7) were sub-cultured on potato dextrose agar (PDA) media under sterile conditions, which were provided by Molecular Genetics Laboratory, University of the Punjab, Lahore. These strains were originally collected from “Evolution Canyon” in Israel (S1, S2 and S3 strains were collected from the South Facing Slope (SFS) and the N5, N6, N7 strains from the North Facing Slope (NFS) of the “Evolution Canyon”. The fungal cultures were incubated at 20 in refrigerated incubator for 7-9 days and were harvested for DNA extraction.

DNA extraction and PCR amplification of RKM1 and RKM4 genes

DNA extraction from all studied strains of S. fimicola was carried out by using modified DNA extraction protocol of Pietro et al. (1995). Four primer pairs (two for each gene) were used to amplify both RK-MTases genes. The primer pairs used for amplification of RKM1 gene were; RKM1F1 (5’-GTTAAAAGCACTACTTCAGT-3’), RKM1R1 (5’-ACAAATCCATATCCAGAGAG-3’) and RKM1F2 (5’-TAAATTGCCATTAGATGTGG-3’), RKM1R2 (5’-TAAAATAGTCTCTTCGGTTG-3’).

For RKM4 gene, primer pairs were; RKM4F1 (5’-AGAGATACCGAAAACTTTGT-3’), RKM4R1 (5’-CAGTTAGAGTCGTAAGTTAA-3’) and RKM4F2 (5’-GAATGAACAAGTGTACAACA-3’), RKM4R2 (5’-GGACGTTTGACAGAGCTTTT-3’). The PCR reaction volume was 15µl, which contained; 10µl 2X Amp Master Mix (GeneAll), 1µl forward primer, 1µl reverse primer (100µM each), 2µl DNA sample (1 in 10 dilution of the g-DNA stock) and 1µl dd H2O. Touch down PCR conditions were used to amplify the RK-MTases genes. The stage 1 included the 15 cycles with initial denaturation at 95 for 3 min, second denaturation for 30 sec, annealing at Tm+10 for 45 sec and elongation at 72 for 60 sec. The stage 2 contained 25 cycles with denaturation at 95 for 30 sec, annealing at Tm-5 for 45 sec and elongation at 72 for 60 sec. The termination stage contained elongation at 72 for 5 min, stop reaction at 4 for 15 min and final hold at 23 until removed from thermal cycler. 1.0% agarose gel electrophoresis was carried out to resolve the PCR products, stained with ethidium bromide and visualized under UV light in Gel Documentation System (Syngene).

Sequencing of genes and sequence analysis for SNPs

Amplicons were sequenced at Macrogen Korea and sequences were edited using the BioEdit program. Multiple sequence alignment was carried out for both methyltransferase genes (RKM1 and RKM4) separately using the Clustal Omega online tool (https://www.ebi.ac.uk/Tools/msa/clustalo/) to identify SNPs using S. cerevisiae sequence as reference.

Designing of primers for SNP sites

For SNP genotyping, a total of eight specific primers based on SNP sites for both RKM1 and RKM4 genes were designed; rkm1F1 (specific for SFS strains), rkm1F2 (specific for all strains of S. fimicola), rkm1F3 (specific for SFS strains), rkm1F4 (specific for all strains of S. fimicola) and rkm4F1, rkm4F2, rkm4F4 (specific for SFS strains), rkm4F3 (specific for NFS strains). The reverse primer RKM1R1 (5’-ACAAATCCATATCCAGAGAG-3’), (specific for RKM1 gene) was used in combination with the forward primers specific to the SNPs of RKM1 gene. Likewise, reverse primer RKM4R1 (5’-CAGTTAGAGTCGTAAGTTAA-3’), (specific for RKM4 gene) was used in combination with the forward primers of SNPs of RKM4 gene. An additional mismatch at third base towards the 3’ end was deliberately introduced in each SNP primer, a G was substituted with a T and a C substituted with an A and vice versa (Table I).

ARMS PCR conditions for amplification of SNP sites

To differentiate between the target SNPs in the RKM1 and RKM4 genes ARMS-PCR conditions were used. For amplification of four SNPs of RKM1gene of S. fimicola, four forward primers (rkm1F1, rkm1F2, rkm1F3, rkm1F4) along with reverse primer rkm1R1 (specific to RKM1 gene) were used. Likewise, for four SNPs of RKM4 gene of S. fimicola, four forward primers (rkm4F1, rkm4F2, rkm4F3, rkm4F4) along with reverse primer rkm4R1 (specific to RKM4 gene) were used. The 20µl PCR reaction mixture for SNPs of RKM1 gene contained 2µl DNA (1 in 10 dilution of the g-DNA stock), 1µl rkm1F1 primer, 1µl rkm1F2 primer, 1µl rkm1F3 primer, 1µl rkm1F3 primer, 1µl rkm1R1 primer (100µM each), 10µl 2X Amp Master Mix (GeneAll) and 3µl ddH2O. The reaction mixture for SNPs of RKM4 gene was prepared in the same way as for SNPs of RKM1. The PCR conditions for SNPs of both genes consisted of 5 min at 94, followed by 35 cycles of 30 s at 94, 30 s at 62.5, 40 s at 72, and a final 7 min extension at 72 (Yang et al., 2017).

 

Table I. Oligonucleotide primers used in the current study.

Name of primer

Oligonucleotide sequence

Position in gene

RKM1F1

5’-GTTAAAAGCACTACTTCAGT-3’

15-34

RKM1R1

5’-ACAAATCCATATCCAGAGAG-3’

785-804

RKM1F2

5’-TAAATTGCCATTAGATGTGG-3’

844-864

RKM1R2

5’-TAAAATAGTCTCTTCGGTTG-3’

1662-1681

RKM4F1

5’-AGAGATACCGAAAACTTTGT-3’

16-35

RKM4R1

5’-CAGTTAGAGTCGTAAGTTAA-3’

656-675

RKM4F2

5’-GAATGAACAAGTGTACAACA-3’

702-727

RKM4R2

5’-GGACGTTTGACAGAGCTTTT-3’

1391-1410

rkm1F1

5’-GTGAATCCACTAAGACTC-3’ A

20-37

rkm1F2

5’-AAAGAGTGGTTTGAAATT-3’ C

256-273

rkm1F3

5’-TTTTATGGTCGCACCGTG-3’ T

428-445

rkm1F4

5’-GAAACTGTCCTGACACCC-3’ A

485-502

rkm4F1

5’-TAACTGTATACTTTAGGG-3’ T

33-50

rkm4F2

5’-TAGCGACGTCCTTCGCGA-3’ A

266-243

rkm4F3

5’-GATTCCACTTGCTGATCTA-3’ A

424-441

rkm4F4

5’-ATGGTTGCTTTGAGGTAC-3’ G

502-519

 

Note: Bold underlined nucleotides are showing additional mismatches, where G substituted with T and C substituted with A and vice versa. Highlighted nucleotides are showing SNPs.

RESULTS AND DISCUSSION

Molecular markers have become a popular tool for observing polymorphism in plants, animals and fungi. Among all marker systems, SNPs are the most prevalent molecular marker for describing genetic variation in natural populations. These are useful for observing genetic variation, population genetic structure and reconstructing the evolutionary history of species (Banke and McDonald, 2005Coates et al., 2009Fischer et al., 2017).

To the best of our knowledge, it is the first time SNPs identified and genotyped in the RK-MTases genes in S. fimicola. A lot of work has been carried out on SNPs of plants and humans but a very few or negligible studies are done on fungal SNPs. In the present study, SNPs genotyping was carried out by performing allele specific PCR conditions to observe polymorphisms in RKM1 and RKM4 methyltransferase genes of S. fimicola. The RKM1 and RKM4 regions of S. fimicola were amplified with target-specific primers by using touchdown PCR conditions and the product sizes were 1320bp and 900bp respectively.

The results of multiple sequence alignment showed that the RKM1 and RKM4 regions for six strains of S. fimicola and S. cerevisiae were identical except for polymorphic sites. These polymorphic sites are due to substitution of single nucleotide and hence termed as single nucleotide polymorphism (SNP). Total seven SNP sites for RKM1 region and nine SNP sites for RKM4 region of S. fimicola were identified (Supplementary Figs. 1 and 3) but genotyping of total eight SNPs were carried out for both RK-MTases in this study (Figs. 1 and 2). All observed SNPs of RKM1 region for all strains of S. fimicola were identical except for two SNPs, which are unique for SFS strains (Fig. 1). In RKM4 region, some SNPs are present in SFS strains but not present in NFS strains and vice versa. For example, SNP at 35th position in the RKM4 region is present only in SFS strains but not present in NFS strains (Fig. 2).


 

 

For RKM4 region, the SNP site at 50th nucleotide position is unique for S1 and S3 strains where A substituted with G. The SNP site at 284th position is unique for NFS strains (T substituted with A) and absent in SFS strains. The SNPs at 442 and 520 nucleotide position, in which G substituted with A and T with C are present only in SFS strains, but not in NFS strains (Fig. 2).

In RKM1 gene, at first SNP of SFS strains, substitution at first base of codon took place, where A replaced with C (ATC-CTC) which changed the encoded amino acid from isoleucine (I) to leucine (L). In 2nd SNP of SFS and NFS strains, substitution at third base of the codon did not change the encoded amino acid. At 3rd SNP of SFS strains, replacement of A with G, resulted in the change of codon from ATA to GTA and amino acid from isoleucine (I) to valine (V). At 4th SNP of SFS and NFS strains, substitution at first base of codon occurred and codon changed from TCT to CCT and encoded amino acid from serine (S) to proline (P) (Fig. 1 and Supplementary Fig. 2). First and third SNPs of SFS strains showed conservation among the groups of strongly similar properties. These are shown by symbol (:) in the amino acid sequence of RKM1 protein in Supplementary Figure 2.

In RKM4 gene, at first polymorphic site of SFS strains, T substituted with A at second base of codon, resulted in change of ATC codon into AAC, which changed the Isoluecine (I) into asparagine (N). At second polymorphic site in S2, S3 and N5 strains, A substituted with G, resulting into the change of codon from GAG to GGG, changed the encoding amino acid from glutamate (E) to glycine (G). At 3rd polymorphic site in NFS strains, T replaced with A at first base of the codon, where TTT converted into ATT and changed the amino acid from phenylalanine (F) to isoleucine (I). In SFS strains at 4th polymorphic site, G substituted with A at third base of the codon (ATG-ATA), resulted into the change of methionine (M) into isoleucine (I) (Fig. 2 and Supplementary Fig. 4).

A number of studies have been carried out on SNPs in different genes as well as whole genome studies by next generation sequencing to observe polymorphism in different plants and fungi. Sun et al. (2013) identified three SNP rich genomic regions and observed polymorphisms in rice false smut Ustilaginoidea virens. Whole genome scan for SNP identification was carried out in Soybean. Likewise, Trick et al. (2009) and Park et al. (2010) observed SNPs in the whole genome of Brassica and Li et al. (2009) reported SNPs in candidate genes controlling morphological traits of leaves and flowering time. Lopez et al. (2000) reported SNPs in candidate genes delta 12 fatty acid desaturase and in fatty acid desaturase 2A in Arachis hypogaea L.

ARMS has become a standard technique that was first described by Newton and colleagues in 1989. It allows the discrimination of alleles that differ by as little as 1bp. In order to genotype the SNPs, the critical part of ARMS-PCR is to design the primers. A single mismatch at 3’ end is not sufficient to avoid non-specific binding, so an extra mismatch was introduced at the 3rd base pair at 3’ end to allow specific binding (Wang et al., 2010; Medrano and de oleveiro, 2014).

In the current study, eight SNP sites for both RKM1 and RKM4 regions (4 for each) were amplified using SNPs site specific primers. Four forward primers; rkm1F1, rkm1F2, rkm1F3 and rkm1F4 were used in combination with reverse primer RKM1R1 to amplify the four SNP sites of the RKM1 region. Four SNP sites having 650bp, 520bp, 240bp and 200bp were amplified by ARMS-PCR respectively, shown in schematic diagram in Figure 3a. Likewise, four SNPs primers (rkm4F1, rkm4F2, rkm4F3 and rkm4F4) in combination with reverse primer RKM4R1 used to amplify the SNPs of RKM4 region and amplicons obtained as; 600bp, 450bp, 250bp and 200bp respectively, shown in schematic diagram in Figure 3b.

ARMS-PCR is allele specific PCR, which is much reliable and reproducible. It does not require restriction digestion and sequencing of PCR product. It only requires the separation of different DNA fragments by using agarose gel electrophoresis. Hence, it is cost effective technique for genotyping of SNPs in coding and non-coding regions of plants, animals and fungal genome.


 

CONCLUSION

SNP is a reliable, efficient and highly reproducible molecular marker to observe polymorphisms in coding regions as well as in non-coding regions. In this study, we successfully identified and genotyped SNPs in the fungus S. fimicola via ARMS-PCR. SNPs specific to the SFS strains and the NFS strains of S. fimicola were found, some of which were non-synonymous substitutions, which might have important role in evolution and adaptive values in their respective environmental conditions.

FUNDING ACKNOWLEDGEMENT

This research is not funded by any governmental or non-governmental organization, institution etc.

Supplementary material

There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20190902090906

Statement of conflict of interest

The authors have declared no conflict of interest.

References

Banke, S. and McDonald, B.A., 2005. Migration patterns among global populations of the pathogenic fungus Mycosphaerella graminicola. Mol. Ecol., 14: 1881-1896. https://doi.org/10.1111/j.1365-294X.2005.02536.x

Berger, J., Suzuki, T., Senti, K.A., Stubbs, J., Schaffner, G. and Dickson, B.J., 2001. Genetic mapping with SNP markers in Drosophila. Nat. Genet., 29: 475. https://doi.org/10.1038/ng773

Brookes, A.J., 1999. The essence of SNPs. Gene, 234: 177–186. https://doi.org/10.1016/S0378-1119(99)00219-X

Chen, D., Ahlford, A., Schnorrer, F., Kalchhauser, I., Fellner, M., Viragh, E., Kiss, I., Syvänen, A.C. and Dickson, B.J., 2008. High-resolution, high-throughput SNP mapping in Drosophila melanogaster. Nat. Methods, 5: 323. https://doi.org/10.1038/nmeth.1191

Chuang, L.Y., Yang, C.H., Tsui, K.H., Cheng, Y.H., Chang, P.L., Wen, C.H. and Chang, H.W., 2008. Restriction enzyme mining for SNPs in genomes. Anticancer Res., 28: 2001-2007.

Coates, B.S., Sumerford, D.V., Miller, N.J., Kim, K.S., Sappington, T.W., Siegfried, B.D. and Lewis, L.C., 2009. Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J. Hered., 100: 556-564. https://doi.org/10.1093/jhered/esp028

Fischer, M.C., Rellstab, C., Leuzinger, M., Roumet, M., Gugerli, F., Shimizu, K.K., Holderegger, R. and Widmer, A., 2017. Estimating genomic diversity and population differentiation–an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genom., 18: 69. https://doi.org/10.1186/s12864-016-3459-7

Guichoux, E., Lagache, L., Wagner, S., Chaumeil, P., Léger, P., Lepais, O., Lepoittevin, C., Malausa, T., Revardel, E., Salin, F. and Petit, R.J., 2011. Current trends in microsatellite genotyping. Mol. Ecol. Resour., 11: 591-611. https://doi.org/10.1111/j.1755-0998.2011.03014.x

Hamajima, N., Saito, T., Matsuo, K. and Tajima, K., 2002. Competitive amplification and unspecific amplification in polymerase chain reaction with confronting two-pair primers. J. mol. Diagn., 4: 103-107. https://doi.org/10.1016/S1525-1578(10)60688-5

Hoffmann, A.A. and Hercus, M.J., 2000. Environmental stress as an evolutionary force. Bioscience, 50: 217-226. https://doi.org/10.1641/0006-3568(2000)050[0217:ESAAEF]2.3.CO;2

Kaiser, S.A., Taylor, S.A., Chen, N., Sillett, T.S., Bondra, E.R. and Webster, M.S., 2017. A comparative assessment of SNP and microsatellite markers for assigning parentage in a socially monogamous bird. Mol. Ecol. Resour., 17: 183-193. https://doi.org/10.1111/1755-0998.12589

Kim, S.Y., Lohmueller, K.E., Albrechtsen, A., Li, Y., Korneliussen, T., Tian, G., Grarup, N., Jiang, T., Andersen, G., Witte, D. and Jorgensen, T., 2011. Estimation of allele frequency and association mapping using next-generation sequencing data. BMC Bioinform., 12: 231. https://doi.org/10.1186/1471-2105-12-231

Kimura, M., 1983. The neutral theory of molecular evolution. Cambridge University Press. https://doi.org/10.1017/CBO9780511623486

Lai, C.Q., Leips, J., Zou, W., Roberts, J.F., Wollenberg, K.R., Parnell, L.D., Zeng, Z.B., Ordovas, J.M. and Mackay, T.F., 2007. Speed-mapping quantitative trait loci using microarrays. Nat. Methods, 4: 839. https://doi.org/10.1038/nmeth1084

Lamb, B.C., Saleem, M., Scott, W., Thapa, N. and Nevo, E., 1998. Inherited and environmentally induced differences in mutation frequencies between wild strains of Sordaria fimicola from “Evolution Canyon”. Genetics, 149: 87-99.

Li, C.X., Liu, S.Y., Sivasithamparam, K. and Barbetti, M.J., 2009. New sources of resistance to Sclerotinia stem rot caused by Sclerotinia sclerotiorum in Chinese and Australian Brassica napus and B. juncea germplasm screened under Western Australian conditions. Australas. Pl. Pathol., 38: 149-152.

Ljungqvist, M., Åkesson, M. and Hansson, B., 2010. Do microsatellites reflect genome-wide genetic diversity in natural populations? A comment on Väli et al. (2008). Mol. Ecol., 19: 851-855. https://doi.org/10.1111/j.1365-294X.2010.04522.x

Lopez, Y., Nadaf, H.L., Smith, O.D., Connell, J.P., Reddy, A.S. and Fritz, A.K., 2000. Isolation and characterization of the Δ12-fatty acid desaturase in peanut (Arachis hypogaea L.) and search for polymorphisms for the high oleate trait in Spanish market-type lines. Theor. appl. Genet., 101: 1131-1138. https://doi.org/10.1007/s001220051589

Medrano, R.F.V. and de Oliveira, C.A., 2014. Guidelines for the tetra-primer ARMS–PCR technique development. Mol. Biotechnol., 56: 599-608. https://doi.org/10.1007/s12033-014-9734-4

Newton, C.R., Graham, A., Heptinstall, L.E., Powell, S.J., Summers, C., Kalsheker, N., Smith, J.C. and Markham, A.F., 1989. Analysis of any point mutation in DNA: The amplification refractory mutation system (ARMS). Nucl. Acids Res., 17: 2503-2516. https://doi.org/10.1093/nar/17.7.2503

Park, S., Yu, H.J., Mun, J.H. and Lee, S.C., 2010. Genome-wide discovery of DNA polymorphism in Brassica rapa. Mol. Genet. Genom., 283:135-145. https://doi.org/10.1007/s00438-009-0504-0

Pietro, S., Fulton, T.M., Chunwongesm, J. and Tanksley, S.D., 1995. Extraction of high quality DNA for Genome Sequencing. Mol. Biol. Rep., 13: 207. https://doi.org/10.1007/BF0267089

Saleem, M., Lamb, B.C. and Nevo, E., 2001. Inherited differences in crossing over and gene conversion frequencies between wild strains of Sordaria fimicola from “Evolution Canyon”. Genetics, 159: 1573-1593.

Shastry, B.S., 2002. SNP alleles in human disease and evolution. J. Hum. Genet., 47: 561. https://doi.org/10.1007/s100380200086

Stickney, H.L., Schmutz, J., Woods, I.G., Holtzer, C.C., Dickson, M.C., Kelly, P.D., Myers, R.M. and Talbot, W.S., 2002. Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res., 12: 1929-1934. https://doi.org/10.1101/gr.777302

Sun, X., Kang, S., Zhang, Y., Tan, X., Yu, Y., He, H., Zhang, X., Liu, Y., Wang, S., Sun, W. and Cai, L., 2013. Genetic diversity and population structure of rice pathogen Ustilaginoidea virens in China. PLoS One, 8: e76879. https://doi.org/10.1371/journal.pone.0076879

Trick, M., Kwon, S.J., Choi, S.R., Fraser, F., Soumpourou, E., Drou, N., Wang, Z., Lee, S.Y., Yang, T.J., Mun, J.H. and Paterson, A.H., 2009. Complexity of genome evolution by segmental rearrangement in Brassica rapa revealed by sequence-level analysis. BMC Genom., 10: 539. https://doi.org/10.1186/1471-2164-10-539

Väli, Ü., Einarsson, A., Waits, L. and Ellegren, H., 2008. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations? Mol. Ecol., 17: 3808-3817. https://doi.org/10.1111/j.1365-294X.2008.03876.x

Van Orsouw, N.J., Hogers, R.C., Janssen, A., Yalcin, F., Snoeijers, S., Verstege, E., Schneiders, H., van der Poel, H., Van Oeveren, J., Verstegen, H. and Van Eijk, M.J., 2007. Complexity reduction of polymorphic sequences (CRoPS™): A novel approach for large-scale polymorphism discovery in complex genomes. PLoS One, 2: 1172. https://doi.org/10.1371/journal.pone.0001172

Van Tassell, C.P., Smith, T.P., Matukumalli, L.K., Taylor, J.F., Schnabel, R.D., Lawley, C.T., Haudenschild, C.D., Moore, S.S., Warren, W.C. and Sonstegard, T.S., 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Meth., 5: 247. https://doi.org/10.1038/nmeth.1185

Wang, H., Sun, H., Kwon, W.S., Jin, H. and Yang, D.C., 2010. A PCR-based SNP marker for specific authentication of Korean ginseng (Panax ginseng) cultivar “Chunpoong”. Mol. Biol. Rep., 37: 1053. https://doi.org/10.1007/s11033-009-9827-5

Wicks, S.R., Yeh, R.T., Gish, W.R., Waterston, R.H. and Plasterk, R.H., 2001. Rapid gene mapping in Caenorhabditis elegans using a high density polymorphism map. Nat. Genet., 28: 160. https://doi.org/10.1038/88878

Yang, D.U., Kim, M.K., Mohanan, P., Mathiyalagan, R., Seo, K.H., Kwon, W.S. and Yang, D.C., 2017. Development of a single-nucleotide-polymorphism marker for specific authentication of Korean ginseng (Panax ginseng Meyer) new cultivar “G-1”. J. Ginseng Res., 41: 31-35. https://doi.org/10.1016/j.jgr.2015.12.007

Ye, S., Dhillon, S., Ke, X., Collins, A.R. and Day, I.N., 2001. An efficient procedure for genotyping single nucleotide polymorphisms. Nucl. Acids Res., 29: e88-e88. https://doi.org/10.1093/nar/29.17.e88

Zhao, Z., Fu, Y.X., Hewett-Emmett, D. and Boerwinkle, E., 2003. Investigating single nucleotide polymorphism (SNP) density in the human genome and its implications for molecular evolution. Gene, 312: 207-213. https://doi.org/10.1016/S0378-1119(03)00670-X

To share on other social networks, click on any share button. What are these?

Pakistan Journal of Zoology

December

Pakistan J. Zool., Vol. 56, Iss. 6, pp. 2501-3000

Featuring

Click here for more

Subscribe Today

Receive free updates on new articles, opportunities and benefits


Subscribe Unsubscribe