1 / 43

What we have learned from GWAS

Statistical Methods to Prioritize GWAS Results by Integrating Pleiotropy and Annotation Hongyu Zhao Yale School of Public Health June 25, 2014 Joint work with Min Chen, Lin Hou , Tianzhou Ma, Can Yang, Dong-Jun Chung, Cong Li, Judy Cho, Joel Gelernter. What we have learned from GWAS.

econner
Download Presentation

What we have learned from GWAS

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. Statistical Methods to Prioritize GWAS Results by Integrating Pleiotropyand AnnotationHongyu ZhaoYale School of Public HealthJune 25, 2014Joint work with Min Chen, Lin Hou, Tianzhou Ma, Can Yang, Dong-Jun Chung, Cong Li,Judy Cho, Joel Gelernter

  2. What we have learned from GWAS • Genes/Variants associated with phenotypes • Genetic risk prediction • Genetic architecture

  3. What we have learned from GWAS • Genes/Variants associated with phenotypes • Prediction • Genetic architecture

  4. Crohn’s Disease

  5. Network-Based Analysis • Start from a known interaction/co-expression network [N: assumed to be known] • Each gene is either associated or not associated with a phenotype [D: unknown] • Each gene has an observed statistical evidence for association [Z: observed] • Goal: Infer D conditional on N and Z Chen, Cho, Zhao (2011) PLoS Genetics

  6. Chen, Cho, Zhao (2011) PLoS Genetics

  7. Application to CD GWAS Chen, Cho, Zhao (2011) PLoS Genetics

  8. Co-Expression Networks Zhou et al. (2002) PNAS

  9. Guilt by Rewiring: Motivation • Gene networks are different between healthy controls and diseased individuals. • The differences are as important or even more important than their commonalities. A A A B B B C C C D D D Control Disease Rewiring network Hou et al. (2014) Human Molecular Genetics

  10. MRF model leads to better replication rates between independent studies • Negative control: • Non-specific microarray dataset (brown line, left figure) Hou et al. (2014) Human Molecular Genetics

  11. Signal enrichments in DHS sites Hou, Ma, Zhao (2014)

  12. Better replication rates at DHS sites Hou, Ma, Zhao (2014)

  13. Weighted scheme to integrate DHS site information to prioritize SNPs

  14. http://dongjunchung.github.io/GPA/

  15. GPA formulation

  16. GPA formulation

  17. GPA formulation

  18. GPA formulation

  19. GPA formulation

  20. GPA formulation

  21. GPA formulation

  22. GPA: Single GWAS Chung et al. (2014) PLoS Genetics, under revision

  23. GPA: Modeling Pleiotropy

  24. GPA: Modeling Annotation Data

  25. Modeling Pleiotropy and Annotation

  26. Key Assumptions for GPA

  27. Simulations

  28. Comparisons with conditional FDR approach

  29. GPA: Enrichment Testing • Pleiotropy & enrichment for annotation can be checked conveniently using the hypothesis testing procedure incorporated into the GPA framework. • Null hypothesis for pleiotropy: H0: ( π10 + π11 ) ( π01 + π11 ) = π11 • Hypothesis testing for annotation enrichment: H0: q0 = q1

  30. GPA: Hypothesis Testing

  31. Comparisons with GSEA

  32. Five Psychiatric Disorders • Five psychiatric disorders: • ADHD. • Autism spectrum disorder. • Bipolar disorder. • Major depression disorder. • Schizophrenia. • Strong pleiotropy exists for BIP-SCZ, MDD-SCZ, ASD-SCZ, & BIP-MDD.

  33. Five Psychiatric Disorders BIP: separate analysis BIP: joint analysis

  34. Five Psychiatric Disorders SCZ: separate analysis SCZ: joint analysis

  35. Comparisons with Linear Mixed Models • Integration of bladder cancer GWAS data with ENCODE DNase-seq data from 125 cell lines. • Annotation from 11 cell lines are significantly enriched, under α = 0.01, after Bonferroni correction.

  36. Acknowledgements Medicine: Judy Cho (Mount Sinai) Psychiatry: Joel Gelernter Yale Center for Statistical Genomics and Proteomics: Min Chen (UT Dallas), Lin Hou, Tianzhou Ma (U. Pittsburgh), Can Yang (HKBU), Dong-Jun Chung (MUSC), Cong Li Various NIH and NSF grants

More Related