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SNP Resources: Finding SNPs, Databases and Data Extraction

SNP Resources: Finding SNPs, Databases and Data Extraction. Debbie Nickerson debnick@u.washington.edu SeattleSNPs. Complex inheritance/disease. Many Other Genes. Variant Gene. Environment. Disease. Diabetes Heart Disease Schizophrenia Obesity Multiple Sclerosis Celiac Disease

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SNP Resources: Finding SNPs, Databases and Data Extraction

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  1. SNP Resources: Finding SNPs, Databases and Data Extraction Debbie Nickerson debnick@u.washington.edu SeattleSNPs

  2. Complex inheritance/disease Many Other Genes Variant Gene Environment Disease Diabetes Heart Disease Schizophrenia Obesity Multiple Sclerosis Celiac Disease Cancer Asthma Autism Two hypotheses: 1- common disease/common variant? 2- common disease/many rare variants?

  3. duplications deletions Genomic Variation inversions insertions Human Genetic Variation Copy-Number Variants Single Nucleotide Polymorphisms Small indels structural variation Frequency • Gene-rich, eg immune response, drug metabolism • Abundant cytogenetic 1 bp 1 chr Size

  4. Total sequence variation in humans Population size: 6x109 (diploid) Mutation rate: 2x10–8 per bp per generation Expected “hits”: 240 for each bp Every variant compatible with life exists in the population BUT: Most are vanishingly rare Compare 2 haploid genomes: 1 SNP per 1331 bp* *The International SNP Map Working Group, Nature409:928 - 933 (2001)

  5. Building Maps of Single Nucleotide Polymorphisms(SNPs)ATTCGGCATGAAATTCGGGATGAA Developed in two overlapping phases: SNP Discovery SNP Genotyping

  6. mRNA cDNA Library BAC Library EST Overlap BAC Overlap Validated - 5.6 MILLON SNPS G C Finding SNPs: Sequence-based SNP Mining Genomic RRS Library Random Shotgun DNA SEQUENCING Shotgun Overlap Align to Reference RANDOM Sequence Overlap - SNP Discovery GTTACGCCAATACAGGATCCAGGAGATTACC GTTACGCCAATACAGCATCCAGGAGATTACC > 11 Million SNPs

  7. 1.0 Candidate Gene Sequencing 96 48 24 16 HapMap Based on ~ 6-8 Chromosomes random 8 8 0.5 2 0.0 0.0 0.1 0.2 0.3 0.4 0.5 Minor Allele Frequency (MAF) Increasing Sample Size Improves SNP Discovery { GTTACGCCAATACAGGATCCAGGAGATTACC GTTACGCCAATACAGCATCCAGGAGATTACC 2 chromosomes Fraction of SNPs Discovered New 1000 Genome Program

  8. Genotype - Phenotype Studies You have candidate gene/region/pathway of interest and samples ready to study: What SNPs are available? How do I find the common SNPs? What is the validation/quality of the SNPs? Are these SNPs informative in my population/samples? What can I download information? How do I pick the “best” SNPs? - Dana Crawford

  9. Minimal SNP information for genotyping/characterization • What is the SNP? Flanking sequence and alleles. • FASTA format • >snp_name • ACCGAGTAGCCAG • [A/G] • ACTGGGATAGAAC • dbSNP reference SNP # (rs #) • Where is the SNP mapped? Exon, promoter, UTR, etc • How was it discovered? Method • What assurances do you have that it is real? Validated how? • What population – African, European, etc? • What is the allele frequency of each SNP? Common (>5%), rare • Are other SNPs associated - redundant? • Is genotyping data for control populations available?

  10. Finding SNPs: Databases and Extraction How do I find and download SNP data for analysis/genotyping? • 1. SeattleSNPs - Candidate gene website • 2. Other web applications • GVS • HapMap Genome Browser • 3. Entrez Gene • - dbSNP • - Entrez SNP

  11. Finding SNPs: Databases and Extraction How do I find and download SNP data for analysis/genotyping? • 1. SeattleSNPs - Candidate gene website • 2. Other web applications • GVS • HapMap Genome Browser • 3. Entrez Gene • - dbSNP • - Entrez SNP

  12. Finding SNPs: Seattle SNPs Candidate Genes pga.gs.washington.edu

  13. Finding SNPs: SeattleSNPs Candidate Genes Example - PCSK9

  14. Finding SNPs: SeattleSNPs Candidate Genes

  15. Finding SNPs: SeattleSNPs Candidate Genes

  16. AD ED

  17. SNP_pos <tab> Ind_ID <tab> allele1 <tab> allele2 Repeat for all individuals Repeat for next SNP

  18. PolyPhen - Polymorphism Phenotyping Structural protein characteristics and evolutionary comparison SIFT = Sorting Intolerant From Tolerant Evolutionary comparison of non-synonymous SNPs

  19. Finding SNPs: SeattleSNPs Candidate Genes pga.gs.washington.edu

  20. Finding SNPs: Databases and Extraction How do I find and download SNP data for analysis/genotyping? • 1. SeattleSNPs - Candidate gene website • 2. Other web applications • GVS • HapMap Genome Browser • 3. Entrez Gene • - dbSNP • - Entrez SNP

  21. GVS: Genome Variation Server http://gvs.gs.washington.edu/GVS/ • Provides rapid analysis of 4.5 million genotyped SNPs from dbSNP and the HapMap • Mapped to human genome build 36 (hg18) • Displays genotype data in text and image formats • Displays tagSNPs or clusters of informative SNPs in text and image formats • Displays linkage disequilibrium (LD) in text and image formats • Online tutorial provided at OpenHelix.com

  22. GVS: Genome Variation Server LDLR http://gvs.gs.washington.edu/GVS/

  23. GVS: Genome Variation Server

  24. GVS: Genome Variation Server • Table of genotypes • Image of visual genotypes

  25. GVS: Genome Variation Server Genotypes displayed in prettybase table and visual genotype graphic

  26. GVS: Genome Variation Server

  27. High Density Genic Coverage(SeattleSNPs) Low Density Genome Coverage (HapMap) = Seattle \SNP discovery (1/200 bp) =HapMap SNPs (~1/1000 bp) GVS: Genome Variation Server Dense genotypes around a candidate gene can be integrated with broader HapMap genotypes

  28. GVS: Genome Variation Server Dense genotypes around a candidate gene can be integrated with lower-density HapMap genotypes

  29. GVS: Genome Variation Server Common samples-combined variations B. Combined samples- common variations Combined samples- combined variations Common Combined

  30. GVS: Genome Variation Server Common samples- combined variations -Common samples- Combined variations

  31. GVS: Genome Variation Server B. Combined samples- common variations SeattleSNPs -Combined samples- HapMap

  32. GVS: Genome Variation Server C. Combined samples- combined variations Combined variations -Combined samples-

  33. Finding SNPs: Databases and Extraction How do I find and download SNP data for analysis/genotyping? • 1. SeattleSNPs - Candidate gene website • 2. Other web applications • GVS • HapMap Genome Browser • 3. Entrez Gene • - dbSNP • - Entrez SNP

  34. www.hapmap.org

  35. Finding SNPs: HapMap Browser

  36. Finding SNPs: HapMap Browser • HapMap data sets are useful because individual genotype data in deeply sampled populations can be used to determine optimal genotyping strategies (tagSNPs) or perform population genetic analyses (linkage disequilbrium) • Data are specific to the HapMap project (not all dbSNP) • HapMap data is available in dbSNP • Visualization of data and direct access to SNP data, individual genotypes, and LD analysis possible in the browser and formats can be saved for Haploview

  37. Finding SNPs: Databases and Extraction How do I find and download SNP data for analysis/genotyping? • 1. SeattleSNPs - Candidate gene website • 2. Other web applications • GVS • HapMap Genome Browser • 3. Entrez Gene • - dbSNP • - Entrez SNP

  38. NCBI - Database Resource PCSK9 www.ncbi.nlm.nih.gov

  39. Finding SNPs using NCBI databases http://www.ncbi.nlm.nih.gov/

  40. Default View cSNPs

  41. Finding SNPs using NCBI databases http://www.ncbi.nlm.nih.gov/

  42. PCSK9

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