1 / 18

Variation and Disease stage 1

Explore the relationship between phenotype-associated SNPs (GWAS) and function-associated DNA using ENCODE data. Identify functional SNPs associated with diseases through integrative analysis. Utilize multivariate segmentations to integrate various data tracks, including histone modifications, TF-occupancy clusters, and more. Analyze genetic interactions, eQTL data, and allele-specific information to offer mechanistic explanations and improve disease understanding. Workshop conducted on March 07, 2011.

ccaldwell
Download Presentation

Variation and Disease stage 1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Variation and Diseasestage 1 AWG workshop March 07, 2011

  2. Using function-associated DNA (ENCODE) to explain phenotype-associated SNPs (GWAS)

  3. Integrative ENCODE analysis of GWAS noncoding SNPs • The multivariate segmentations (Michael and Jason) integrate multiple tracks of data. • The GWAS SNPs are enriched in the segments associated with function. • Pipeline using all the ENCODE data • Summation • GWAS SNPs are enriched in DNase HSs (Stam), enhancers predicted by histone modifications (Brad, Manolis), segments with clusters of TF-occupancy, etc • How many SNPs fall into these individually? (refer to separate papers) Then do union and merge of these to get a composite answer • RNA contigs??

  4. GWAS SNPs are strongly enriched in function-associated segments

  5. Similar patterns of GWAS enrichment in segments from multivariate HMM

  6. Pipeline using all the ENCODE data • Begin with GWAS SNP(s) • Extend from mapped SNP to all SNPs in LD • Do SNPs fall in function-associated segment? • S. Wilder’s “cleaner view” of segmentations? • What features are in segment? • Histone mods, DHS, list of TFs, … • Is SNP in DNase footprint (UW, Duke)? • Is the DNA segments under constraint? Lineage-limited? • Go to Pouya’s motifs: is the SNP in there? • Is the binding allele specific? • Result: candidate functional SNP with testable hypothesis

  7. Published Genome-Wide Associations through 6/2010, 904 published GWA at p<5x10-8 for 165 traits NHGRI GWA Catalog www.genome.gov/GWAStudies 3417 GWAS SNPs, Nov 2010

  8. Use DNase HSs to find functional SNPs “…we find a total of 140 associations in 86 different phenotypes linked to at least one fSNP” Anshul Kundaje, Marc Schaub

  9. Type 1 diabetes locus: Anshul et al Diabetes associated SNP Complete LD fSNP Paint by segments

  10. GWAS uber-associations • Genetic interactions • Unlinked loci associated with same traits • Functional interactions • SOMs on segmentations? • Physical interactions • ChIA-PET, 5C • Connecting any type of interaction with another would be compelling

  11. Heterozygosity Ewan Bob Altschuler …

  12. GWAS SNPs occur in TF occupied segments twice as frequently as regular SNPs All TF ChIP-seq -> Anshul’s consistent peak calls (8M) -> Manoj’s consolidated set (200K) -> Bob Harris’ overlap analysis

  13. 140 TF peaks have GWAS SNPs

  14. eQTLs (Ewan) • To what extent can we find mechanistic explanations for observed eQTL data in lymphoblastoid lines? • Can we provide any additional information to help improve eQTL analysis (eg, proposed trans factors due to knowing chip-seq peaks?)

  15. eQTL wrt allele specific data

  16. Integrate features using multivariate segmentations Ernst and Kellis Multivariate HMM (image is old version) M. Hoffman Segway round 5b Dynamic Bayesian network

More Related