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MESA Candidate Gene Project

MESA Candidate Gene Project. MESA Steering Committee Meeting September 2005. Specific Aim. Conduct an association study of 2,880 MESA participants (parent study) 720 randomly selected from each ethnic group of Caucasian-, African-, Mexican-, and Chinese-Americans

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MESA Candidate Gene Project

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  1. MESA Candidate Gene Project MESA Steering Committee Meeting September 2005

  2. Specific Aim • Conduct an association study of 2,880 MESA participants (parent study) • 720 randomly selected from each ethnic group of Caucasian-, African-, Mexican-, and Chinese-Americans • Include the well-phenotyped “MESA 1000” • Genotype 1536 SNPs in candidate genes proposed by MESA Investigators

  3. Candidate Gene Selection • MESA investigators were asked to propose candidate genes at Fall 2004 meeting. • List collated by DCC and resent to MESA investigators for ranking (267 genes) • Ranking conducted by DCC. Semi-final list ready by Spring 2005 meeting. • Further refinements • More genes added & some priorities altered • More discussion with MESA Eye • More coordination with the laboratory tests

  4. (Candidate Gene Selection, con’t) • List grew from 267 to 294 genes • Initial analysis of number of “tagSNPs” per gene in order to give idea of the possible number of genes that could be studied • HapMap data analyzed using “Tagger” • PGRN data (Seattle SNPs) / LD Select output • Check of what would be missing and what was included (keep current with known gene associations)

  5. Selection of tagSNPs • List of 294 genes sent to Illumina, list of 60,293 SNPs returned with info • Illumina validated, 95% predicted success • Double-hit and designable, 80-85% • Non-validated and designable, 30-50% • Non-designable • tagSNPs selected with goal of 80-85% or greater

  6. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded • Minor allele frequency set at 0.05 or greater • Merge Illumina designability info with tagSNPs • Use “Tagger” to select tagSNPs AND Illumina predicted high success rate, reiterate if necessary • Dump final tagSNP list to SNPLIST database • Repeat for other ethnic groups & combine in SNPLIST database • Add any cSNPs with Illumina predicted high success rate, add to SNPLIST • Go to next gene on gene list

  7. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded

  8. Selection of tagSNPs

  9. Selection of tagSNPs • Obtain HapMap info for gene & ethnic group Example: ABCC1

  10. Selection of tagSNPs

  11. Selection of tagSNPs Add Margin of 10kb on promoter end and 3 kb to 3’-utr end

  12. Selection of tagSNPs Add Margin of 10kb on promoter end and 3 kb to 3’-utr end Isoforms must be checked for cSNPs

  13. Selection of tagSNPs Dump SNP data

  14. Selection of tagSNPs Dump SNP data

  15. Selection of tagSNPs Import into Haploview Program

  16. Selection of tagSNPs Import into Haploview Program

  17. Selection of tagSNPs List of HapMap SNPs for Caucasian population in HapMap database

  18. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded • Minor allele frequency set at 0.05 or greater

  19. Selection of tagSNPs Change MAF to 0.05 Dump SNP list

  20. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded • Minor allele frequency set at 0.05 or greater • Merge Illumina designability info with tagSNPs (uses relational database technology).

  21. Selection of tagSNPs List of SNPs from HapMap now merged with Illumina designability info

  22. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded • Minor allele frequency set at 0.05 or greater • Merge Illumina designability info with tagSNPs • Use “Tagger” to select tagSNPs AND SNPs with an Illumina-predicted high success rate, reiterate if necessary

  23. Note, for example, that SNP #11 has low designability. This SNP is excluded from Tagger Use Tagger List of SNPs from HapMap now merged with Illumina designability info

  24. Use aggressive tagging, including 2 and 3 marker haplotypes Use Tagger

  25. Use aggressive tagging, including 2 and 3 marker haplotypes r2 ≥ 0.8 Use Tagger

  26. Thus far, I have 32 SNP’s to genotype Use Tagger

  27. Use Tagger But a few SNPs are “untaggable”

  28. Remember that I excluded the “Non-validated but designable” class. Go back and add some of these back in. Use Tagger

  29. Final list gives 41 SNPs required to tag 51 alleles of caucasian SNPs in the 200kb span of the ABCC1 gene Dump to SNPLIST Use Tagger

  30. Selection of tagSNPs • HapMap genotyping information for one ethnic group was downloaded • Minor allele frequency set at 0.05 or greater • Merge Illumina designability info with tagSNPs • Use “Tagger” to select tagSNPs AND Illumina predicted high success rate, reiterate if necessary • Dump final tagSNP list to SNPLIST database • REPEAT for other ethnic groups & combine in SNPLIST database (combined for ABCC1 is 50 SNPs) • REPEAT for all genes

  31. AND: Add cSNPs • Add any cSNP from dbSNP with frequency information and high designability • (none for ABCC1 example) Add Investigator-proposed SNPs • Some investigators submitted cSNPs or tagSNPs, these were checked for designability in the Illumina system and also added to SNPLIST

  32. Add ethnic-specific SNPs • ~110 ethnic-specific SNPs were added • (SNPs showing a MAF > 0.3 in one population and no MAF in the other populations) • Will be available if analysis of population stratification becomes important in the subsequent analysis

  33. Result:

  34. Result • List of 1536 SNPs has been sent to Illumina and has been input into the pipeline for oligo synthesis • Mike Tsai’s group has sent the DNAs to Illumina for the genotyping pipeline

  35. caveats • To reduce the total number of SNPs for some genes, the MAF was increased to > 0.20, and/or the SNPS were limited to Caucasians • So not all “eggs in one basket” • Some genes are represented by a few SNPs that were proposed by investigators only • cSNPs tended to fail Illumina designability • This program will not look at APOE very well because the most important SNPs failed designability

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