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SNP Picking: Application to Candidate Gene Studies

SNP Picking: Application to Candidate Gene Studies. Iona Cheng January 23, 2006. Candidate Gene Studies. “I am interested in a candidate gene and have samples ready to study. What SNPs do I genotype?”. T. T. G. G. T. G. T. T. G. G. T. G. A. A. A. A. A. A. C. C. C. G. C.

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SNP Picking: Application to Candidate Gene Studies

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  1. SNP Picking:Application to Candidate Gene Studies Iona Cheng January 23, 2006

  2. Candidate Gene Studies “I am interested in a candidate gene and have samples ready to study. What SNPs do I genotype?”

  3. T T G G T G T T G G T G A A A A A A C C C G C C Single Nucleotide Polymorphism (SNPs):Most DNA variants change a single DNA letter • Most frequent genetic variant • 1/1300 base pairs • ~ 10 million common SNPs (> 1- 5% MAF) - 1/300 bp

  4. Why Are SNPs Significant? Person 1 Person 2 Gene A Gene A SNP may cause Gene A to make altered protein = SNP variations in DNA National Cancer Institute

  5. Noncoding • Coding Synonymous = no change in amino acid Nonsynonymous/nonsense = change to stop codon Nonsynonymous/missense = change amino acid Normal allele • Gene sequence …..GCG GGA GCC GAT……………… • Protein Sequence ……Ala Gly Ala Asp……………… • C677T allele • Gene Sequence …..GCG GGA GTC GAT………………. • Protein Sequence ……Ala Gly ValAsp ..…………… Types of SNPs

  6. Candidate Gene: Where do I start? • Location: What chromosome? What position on the chr? • Exons/UTR: How many exons? UTR regions? • Size: How large is the gene?

  7. Candidate Gene: example MTHFR • UCSC Genome Browser http://genome.ucsc.edu/cgi-bin/hgGateway

  8. Candidate Gene: example MTHFR 3’ 5’

  9. SNP Picking: Things to consider • Validation: What is the quality of the SNPs? • Informative: Are these SNPs informative in my population? How common are they? Location? • Potentially Functional: Do these SNPs have a potential biological impact? Missense variants? • Previously Associated: Have previous studies found SNPs in the candidate gene associated with the outcome?

  10. SNP Picking: Database Resources • Validation: dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/ • Informative: dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/ • Potentially Functional: dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/ • Previously Associated: PubMed/OMIM http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed http://0-www.ncbi.nlm.nih.gov.library.vu.edu.au/entrez/query.fcgi?db=OMIM

  11. SNP Picking: Other Resources • UCSC Genome Browser http://genome.ucsc.edu/ • SNPper http://snpper.chip.org/ • Seattle SNPs http://pga.gs.washington.edu/ • HapMap http://www.hapmap.org/

  12. SNP Picking: Validation

  13. SNP Picking: Validation

  14. SNP Picking: Validation

  15. SNP Picking: Informative

  16. SNP Picking: Potentially Functional C677T

  17. SNP Picking: Previously Associated

  18. MTFHR Summary • Chromosome 1: 11,780,053-11,800,381 • Size: 20,329 bp • Exons: 12 • Potentially Functional: 5 missense of which 3 MAF >5% • Previously Associated: 3 (C677T, A1298C, A2756G)

  19. 102 SNPs across MTHFR Too Many SNPs to Genotype! MTFHR SNPs http://genome.ucsc.edu/cgi-bin/hgGateway

  20. G/C 3 G/A 2 T/C 4 G/C 5 A/T 1 A/C 6 G G A A G T G A C C C C C C C C T T A A G G C C high r2 high r2 high r2 • SNPs are correlated (aka Linkage Disequilibrium) Solution: Tag SNP Selection Pairwise Tagging: SNP 1 SNP 3 SNP 6 3 tags in total Test for association: SNP 1 SNP 3 SNP 6 Carlson et al. (2004) AJHG 74:106

  21. Tag SNPs Database Resources http://www.hapmap.org http://gvs.gs.washington.edu/GVS/index.jsp

  22. Tag SNPs: HapMap Samples • 90 Yoruba individuals (30 parent-parent-offspring trios) from Ibadan, Nigeria (YRI) • 90 individuals (30 trios) of European descent from Utah (CEU) • 45 Han Chinese individuals from Beijing (CHB) • 45 Japanese individuals from Tokyo (JPT)

  23. Tag SNPs: HapMap

  24. Tag SNPs: HapMap

  25. Tag SNPs: HapMap & Haploview http://www.broad.mit.edu/mpg/haploview/

  26. Tag SNPs: HapMap & Haploview

  27. Tag SNPs: HapMap & Haploview

  28. Tag SNPs: HapMap & Haploview

  29. Tag SNPs: HapMap & Haploview

  30. Tag SNPs: HapMap Summary • We identified 33 common MTHR SNPs (MAF > 5%) among Caucasians • We “forced” in 3 potentially functional/previously associated SNPs • We identified tag based on pairwise tagging • 15 tags SNPs could capture all 33 MTHR SNPs (mean r2 = 97%)

  31. A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T 15 MTHR tag SNPs DNA Samples Genotyping Clinical Observations

  32. Amino Acids 20 Different Amino Acids Amino group Carboxyl group Lysine Lysine side chain Graphic Representation of an Amino Acid Basic Structure of an Amino Acid National Cancer Institute

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