SNp mining in crops
- 1. SNP Mining in Cereal Crops Presented By: SAURABH PANDEY PALB-3252 Sr. M.Sc.(Ag.)
- 2. Seminar flow • Introduction • What is SNPs -Detection methods, -Techniques used for detection • Different software used • Crop lists where SNPs were detected • Improvement works done • Case Study 09-05-2015 Department of Plant Biotechnology 2
- 3. Introduction 09-05-2015 Department of Plant Biotechnology 3
- 4. Scenario of Molecular Markers 09-05-2015 Department of Plant Biotechnology 4
- 5. Molecular markers First Generation Second Generation Third Generation •RFLP- Hybridization based •RAPD-PCR based •AFLPs •SSRs •ESTs-used in functional genomics •SNPs 09-05-2015 Department of Plant Biotechnology 5
- 6. Single Nucleotide Polymorphisms DNA sequence variations that occur when a single nucleotide (A, T, C or G) in the genome sequence is altered. SNP: Single DNA base variation found >1% Mutation: Single DNA base variation found <1% SNPs 09-05-2015 Department of Plant Biotechnology 6
- 7. Coding SNPs cSNP Positions that fall within the coding regions of genes Regulatory SNPs rSNP Positions that fall in regulatory regions of genes Synonymous SNPs sSNP Positions in exons that do not change the codon to substitute an amino acid Non-synonymous SNPs nsSNP Positions that incur an amino acid substitution Intronic SNPs iSNP Positions that fall within introns SNP functional classes Mooney et al, 2005 09-05-2015 Department of Plant Biotechnology 7
- 8. SNPs Importance Abundant in the genome and frequency of SNPs is higher i.e. Maize has 1SNP/31bp in non-coding, 1SNP/124bp in coding region (Ching et al, 2002) In rice whole genome shotgun sequences for japonica and indica 3-27 SNP/kb In Soybean 280SNPs/76.3kb of genomic DNA Automation is easy. Due to biallelic nature of these molecular markers and these are directly based on DNA sequence. • Millions of sequences are available in public database for several crops i.e. wheat has 132,000 ESTs, • Till now no. of SNPs has been discovered i.e. Barley have 29447 SNPs , Rice 64837 SNPs (Yu et.al, 2005) (Zhu et al,2003) 09-05-2015 Department of Plant Biotechnology 8
- 9. SNPs Application • Comparative mapping is performed using datbases. • Linkage Disequilibrium based Association Studies- eg. Arabidopsis thaliana • Marker Assisted Selection(MAS)- used in soybean by identification of SNP markers with GmNARK gene. Gene indicates hypernodulating mutation (Kim et al, 2005) • Genetic diversity studies- In maize using SNPs at 21 loci in chromosome1 (Tenalillon et al, 2005) • High resolution genetic mapping -5 SNPs are genetically mapped in melon (Morales et al, 2004) • Mapping of EST sequences can be done with SNPs(Davis G et al, 2001) • Identification of cloned genes with SNPs- Secondary application of SNPs (Jehan and Lakhanpaul, 2006) 09-05-2015 Department of Plant Biotechnology 9
- 10. Crop Gene function Trait associated with SNP Utility of SNP Barley (Hordeum vulgare) B-Amylase gene Degradation of starch Enzyme thermostability To select barley seedling carying superior allele of B-Amylase Wild Barley (H. spontaneum) Dhn1 & Dhn5 (Dehydrin) Adaptive response of plant to enviromental stress Resistance to water stress For water stress adaptation Rice (Oryza sativa) 1) Wx (waxy) gene 2) Sd-1(semi- dwarfing )gene 1) Control amylose synthesis by coding starch synthase enyme 2) Dwarfism 1) Amylose content 2) Dwarfism 1) For development of new cultivar 2) Selection of sd-1 in breeding programme Wheat (Triticum aestivum ) 1) Pin b (Puroindolin b) 2) Rht 1 & Rht 2 gene 1) Thicken the coat 2) Dwarfism 1) Grain hardiness 2) Dwarfism Breeding program Soybean (Glycine max) Rhg 1& Rhg 4 Soyabean Cyst nematode resistance allele SCN resistance Breeding programme Onion (Allium cepa) SNP allele in Plastosome Responsible for CMS Cytoplasmic male sterility and fertility For development of CMS lines Musturd (Brassica juncea ) FAE 1 gene Fatty acid elongase Erucic acid content Breeding programme 09-05-2015 Department of Plant Biotechnology 10
- 11. CROP Citation No.of SNPs detected Alfalfa Li et al 2012 872.384 Cotton Bayers et al 2012 Zhu et al 2014 151,712 40,503 Peanut Khera et al 2013 Zhou et al 2014 8,486 1,765 Potato Uitdewilligen et al., 2013 42,625 Rapeseed Trick et al 2009 Hu et al 2012 Huang et al 2013 41,593 655 892,803 Wheat Allen, 2013 Cavanagh et al., 2013 10,251 25,454 White Clover Nagy et al.,2013 208,854 SNP identification in polyploids 09-05-2015 Department of Plant Biotechnology 11
- 12. Approximately 18.9 million single nucleotide polymorphisms (SNPs) in rice were discovered when aligned to the reference genome of the temperate japonica variety, Nipponbare. Phylogenetic analyses based on SNP data confirmed differentiation of the O. sativa gene pool into 5 varietal groups – indica, aus/boro, basmati/sadri, tropical japonica and temperate japonica. 09-05-2015 Department of Plant Biotechnology 12
- 13. The trends of crop sequencing toward breeding practice Yang et al., 2015 09-05-2015 Department of Plant Biotechnology 13
- 14. SNPs Discovery • Two approaches have been adopted for the discovery of Novel SNPs In- vitro Discovery (new sequence data is generated) In-silico methods (analysis of available sequence data) 09-05-2015 Department of Plant Biotechnology 14
- 15. In vitro approaches Non sequencing based methods Sequencing based methods Resequencing based methods 09-05-2015 Department of Plant Biotechnology 15
- 16. Non sequencing methods Restriction based techniques DNA conformation technique RFLP (Restriction Fragment Length Polymorphism) CAPS(Cleaved Amplified Polymorphic Sequence) dCAPS(Derived Cleaved Amplified Polymorphic Sequence Chip Based methods Use of probes for hybridization of whole genome SSCP (Single Strand Conformational Polymorphism) DGGE (Denaturing Gradient Gel Electrophoresis) TGGE (Temperature Gradient Gel Electrophoresis) Heteroduplex Analysis TILLING Target Induced Local Lesion IN Genome- Cel I Endonuclease 09-05-2015 Department of Plant Biotechnology 16
- 17. Based on Sequencing methods Locus specific PCR amplification Alignment of available genomic sequences Whole genome shotgun method Overlapping region BACs and PACs Reduced Representation Shotgun(RRS) 09-05-2015 Department of Plant Biotechnology 17
- 18. Finding SNPs: Sequence-based SNP Mining RANDOM Sequence Overlap - SNP Discovery GTTACGCCAATACAGGATCCAGGAGATTACC GTTACGCCAATACAGCATCCAGGAGATTACC Genomic RRS Library Shotgun Overlap BAC Library BAC Overlap DNA SEQUENCING mRNA cDNA Library EST Overlap Random Shotgun Align to Reference > 11 Million SNPs G C Validated - 5..6 MILLON SNPS
- 19. Resequencing methods Pyrosequencing Mass Array •Sequence by synthesis •Primer +dNTP Primer+1N+ PPi •APS + PPi ATP Adenosine Phospho Sulphate •Luciferin + ATP Oxyluciferien + light • Based on MALDI-TOF MS Matrix Assisted Laser Desorption/Ionization –Time of Flight Mass Spectrophotometery •Based on variation in molecular wt. of 4 nucleotides 09-05-2015 Department of Plant Biotechnology 19
- 20. In silico methods Use of software for discovery of SNP from available sequence database Manually is not possible, to find out SNP or single nucleotide difference large no. of sequences Hence various software i.e. Phred, PolyBayes, autoSNPdb, SNPserver, etc 09-05-2015 Department of Plant Biotechnology 20
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- 22. Seal et al., 2014 09-05-2015 Department of Plant Biotechnology 22
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- 26. Method for SNP Identification (Ganal et al.,2009) Prerequisite Current false discovery rate(%) Specifics, Limitations EST Sequence data Large number of available EST- sequences 15-50 Dependent on the expression level or need for normalized libraries, difficulties in the discrimination of orthologous from paralogous sequences, low sequence quality Array analysis Unigene sets based on EST-sequences, array technology >20 Not all SNPs identified, large genomes require complexity reduction Amplicon Resequencing Unigene sets based on EST-sequences, amplification primers for many individual genes <5 High reliability but costly, detailed haplotype analysis possible, many lines can be compared, allele frequency data with pools of DNA No Genomic Sequence and Next Generation Sequencing Technologies Novel sequencing technologies, complexity reduction methods, bioinformatic tools 15-25 Generates large amounts of data, costly bioinformatics, false discovery rate for genomes without full sequence is relatively high Genomic Sequence is available either through conventional sequencing or next generation sequencing Reference genome, bioinformatic tools <5-10 Small genomes can be fully sequenced and compared for SNPs, for large genomes targeted approaches will be necessary (e.g. exon capture and multiplex amplification) 09-05-2015 Department of Plant Biotechnology 26
- 27. SNP Genotyping For SNPs detection there are more than 30 techniques available. i.e. Molecular beacons, Padlock probe, Invader assay etc All are based on basic principles i.e. Hybridization Direct Sequencing Allele specific primer extension Single base extension Endonuclease Cleavage / Ligation 09-05-2015 Department of Plant Biotechnology 27
- 28. • Illumina GoldenGate Assay • Sequenom • Affymetrix/GeneChip • SNaPshot • SNPlex • Taqman • Dye Terminator Sequencing • Pyrosequencing (454) • Reverse-nucleotide sequencing(Solexa) • Sequencing by ligation (ABI) SNPs Genotyping platforms 09-05-2015 Department of Plant Biotechnology 28
- 29. Crop Specific SNP marker system development Non Sequencing method Resequencing methods Sequencing methods In silico method Crop specific SNP genotyping platform development Application of SNP genotyping Oligonucleotide ligation assay Invader assay Molecular Beacons Padlock probes Marker Assisted Selection Gene tagging Fine mapping Association studies 09-05-2015 Department of Plant Biotechnology 29
- 30. Case study 09-05-2015 Department of Plant Biotechnology 30
- 31. Introduction • Genome analysis in bread wheat poses substantial challenges • Single nucleotide polymorphisms (SNPs) represent the most frequent type of genetic polymorphism and can therefore allow the development of the highest density of molecular markers. • Whole-genome Illumina paired read sequence data were generated from 16 Australian bread wheat varieties. • After filtering to remove poor quality and clonal reads, a total of 13 642 million read pairs remained. • Alignment of these read pairs to the wheat group 7 and 4AL chromosome assemblies using strict parameters resulted in 3.05%, 3.76% and 3.43% of read pairs mapping uniquely to chromosomes 7A, 7B and 7D, respectively. • SNP calling using the SGSautoSNP pipeline predicted a total of 4 018 311 intervarietal SNPs. 09-05-2015 Department of Plant Biotechnology 31
- 32. Material and Methods • SNP prediction SNP predication was performed using SGSautoSNP (Lorenc et al., 2012), with output in snp format for subsequent analysis and gff format for presentation on a GBrowse genome viewer at www.wheatgenome.info. • SNP matrix production and transition/transversion ratio analysis The snp files generated by SGSautoSNP were parsed using a custom Python script to generate the SNP matrix file The transition/transversion ratio for each chromosome was calculated based on bins of 500 SNPs using VCFtools. • SNP density and gene analysis The SNP density plots for each chromosome were generated using a custom Python script that calculates relative density based on a window size of 50 000 bp. 09-05-2015 Department of Plant Biotechnology 32
- 33. Genes identified as being in low-SNP-density regions were compared with the Swissprot database using BLASTX (BLASTALL 2.2.6) with an E value cut-off 1e-5. The genes with minimum E- value has been identified in low/high SNP density regions with UniProtKB entry ID and protein names. • Validation A total of 22 SNPs were selected from the three group 7 reference genomes for validation. PCR amplification of the 22 loci was performed using primers designed to bind to conserved sequence surrounding the SNPs The purified PCR products were Sanger-sequenced using Big-Dye 3.1 (PerkinElmer, Waltham, MA), using forward and reverse PCR primers, and analysed using an ABI3730xl. 09-05-2015 Department of Plant Biotechnology 33
- 34. Results Whole-genome Illumina paired read sequence data After filtering 13 642 million read pairs remained Alignment to the wheat group 7 and 4AL chromosome assemblies 3.05%, 3.76% and 3.43% of read pairs mapping uniquely to chromosomes 7A, 7B and 7D, respectively SNP calling using the SGSautoSNP pipeline total of 4 018 311 intervarietal SNPs 09-05-2015 Department of Plant Biotechnology 34
- 35. • The majority of SNPs were identified on contigs which do not form part of the syntenic builds and are predominantly within intergenic regions. 09-05-2015 Department of Plant Biotechnology 35
- 36. Figure 2: Ts/Tv ratio across the 7A, 7B and 7D syntenic builds 09-05-2015 Department of Plant Biotechnology 36
- 37. Phylogenetic relationships of 16 Australian wheat varieties based on SNP data obtained in this study. SNP variation is 146171 SNP variation is 968088 Avg. no. of SNPs between varieties 465278 09-05-2015 Department of Plant Biotechnology 37
- 38. Figure 3: SNP density across the 7A, 7B and 7D syntenic builds.09-05-2015 Department of Plant Biotechnology 38
- 39. • A total of 146 genes were predicted to be in low-SNP-density regions, representing 40, 27 and 79 genes on the A, B and D genomes, respectively. these genes include MADS box and Myb transcription factors, signal transduction pathway genes, a sodium transporter, an ironresponsive transcription factor, a potassium transporter, callose synthase, sucrose synthase and sugar transporters. • A total of 14 genes were predicted to be in high-SNP-density regions, representing 10, 3 and 1 gene(s) on the A, B and D genomes, respectively. These genes include cellulose synthase, argonaute and ethylene response factors. 09-05-2015 Department of Plant Biotechnology 39
- 40. The SNPs from the recently published wheat Infinium array (Wang et al. , 2014) were compared to those predicted by SGSautoSNP. A total of 850 SNPs were identified as having a match on the group 7 chromosomes at the same position as predicted in our study . Of these, 482 (57%) were classified as polymorphic single locus, 316 (37%) as being polymorphic multilocus, while only 52 (6%) were monomorphic.09-05-2015 Department of Plant Biotechnology 40
- 41. Conclusion • This study has revealed a vast number of polymorphisms occurring within the chromosome 7 homoeologues of hexaploid wheat among elite Australian varieties. • This resource is publically available to assist additional genetic analysis and breeding. • Furthermore, observed patterns of SNPs across the homoeologous group 7 chromosomes have provided insight into the molecular consequences of the evolution and selection that resulted in modern hexaploid wheat. 09-05-2015 Department of Plant Biotechnology 41
- 42. Summary • SNP are future markers, having high density in genome. • Although thousands of SNP markers are widely used in animal and human genome analysis, their use in plants is still in its infancy. • SNP mining can provide better understanding of crops at the gene level , for the detailed analysis of germplasm and ultimately for the efficient management of genetic diversity within plant breeding on a whole genome level. 09-05-2015 Department of Plant Biotechnology 42
- 43. 09-05-2015 Department of Plant Biotechnology 43 Thank you