Statistical Methods to Prioritize GWAS Results by Integrating

Pleiotropy and Annotation

Hongyu ZhaoYale 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

• Genes/Variants associated with phenotypes

• Genetic risk prediction

• Genetic architecture

What we have learned from GWAS

• Genes/Variants associated with phenotypes

• Prediction

• Genetic architecture

Crohn’s Disease

pathway_name gene_symbol pvalueIL22 Soluble Receptor Signaling Pathway pathway IL23R 0.002297IL22 Soluble Receptor Signaling Pathway pathway SOCS1 0.010415IL22 Soluble Receptor Signaling Pathway pathway IL2RA 0.017337IL22 Soluble Receptor Signaling Pathway pathway PRLR 0.019376IL22 Soluble Receptor Signaling Pathway pathway STAT2 0.033827IL22 Soluble Receptor Signaling Pathway pathway TYK2 0.052902IL22 Soluble Receptor Signaling Pathway pathway IL10RB 0.060543IL22 Soluble Receptor Signaling Pathway pathway CNTFR 0.068332IL22 Soluble Receptor Signaling Pathway pathway IL12RB2 0.072698IL22 Soluble Receptor Signaling Pathway pathway IL20RA 0.077203IL22 Soluble Receptor Signaling Pathway pathway IFNAR2 0.085782IL22 Soluble Receptor Signaling Pathway pathway IL22 0.10299IL22 Soluble Receptor Signaling Pathway pathway IL22RA2 0.113906IL22 Soluble Receptor Signaling Pathway pathway IL6ST 0.124483IL22 Soluble Receptor Signaling Pathway pathway IL21R 0.125142IL22 Soluble Receptor Signaling Pathway pathway IL6R 0.125529IL22 Soluble Receptor Signaling Pathway pathway SOCS2 0.131336IL22 Soluble Receptor Signaling Pathway pathway IL13RA2 0.142406IL22 Soluble Receptor Signaling Pathway pathway IL7R 0.146245IL22 Soluble Receptor Signaling Pathway pathway JAK2 0.166414IL22 Soluble Receptor Signaling Pathway pathway IL11RA 0.16868IL22 Soluble Receptor Signaling Pathway pathway GHR 0.191144IL22 Soluble Receptor Signaling Pathway pathway CSF3R 0.191723IL22 Soluble Receptor Signaling Pathway pathway IFNGR2 0.208994IL22 Soluble Receptor Signaling Pathway pathway IL12RB1 0.267659IL22 Soluble Receptor Signaling Pathway pathway IL28RA 0.294141IL22 Soluble Receptor Signaling Pathway pathway JAK1 0.317088IL22 Soluble Receptor Signaling Pathway pathway STAT6 0.349177IL22 Soluble Receptor Signaling Pathway pathway LEPR 0.391859IL22 Soluble Receptor Signaling Pathway pathway IFNAR1 0.392715IL22 Soluble Receptor Signaling Pathway pathway IL15RA 0.414013IL22 Soluble Receptor Signaling Pathway pathway SOCS6 0.442633IL22 Soluble Receptor Signaling Pathway pathway SOCS3 0.444405IL22 Soluble Receptor Signaling Pathway pathway IL22RA1 0.469906IL22 Soluble Receptor Signaling Pathway pathway STAT1 0.503734IL22 Soluble Receptor Signaling Pathway pathway STAT4 0.504923IL22 Soluble Receptor Signaling Pathway pathway EPOR 0.553102IL22 Soluble Receptor Signaling Pathway pathway SOCS4 0.556056IL22 Soluble Receptor Signaling Pathway pathway IL2RB 0.61677IL22 Soluble Receptor Signaling Pathway pathway STAT5A 0.661919IL22 Soluble Receptor Signaling Pathway pathway IL2RG 0.672769IL22 Soluble Receptor Signaling Pathway pathway IFNGR1 0.676117IL22 Soluble Receptor Signaling Pathway pathway JAK3 0.702464IL22 Soluble Receptor Signaling Pathway pathway IL4R 0.746998IL22 Soluble Receptor Signaling Pathway pathway STAT3 0.780401IL22 Soluble Receptor Signaling Pathway pathway IL5RA 0.78238IL22 Soluble Receptor Signaling Pathway pathway LIFR 0.803115IL22 Soluble Receptor Signaling Pathway pathway SOCS5 0.807055IL22 Soluble Receptor Signaling Pathway pathway CSF2RB 0.903223IL22 Soluble Receptor Signaling Pathway pathway STAT5B 0.906422IL22 Soluble Receptor Signaling Pathway pathway IL10RA 0.924236IL22 Soluble Receptor Signaling Pathway pathway OSMR 0.928906IL22 Soluble Receptor Signaling Pathway pathway IL13RA1 0.973552

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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

Chen, Cho, Zhao (2011) PLoS Genetics

Chen, Cho, Zhao (2011) PLoS Genetics

Application to CD GWAS

Zhou et al. (2002) PNAS

Co-Expression Networks

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.

AA

CCBB

DD

AA

CCBB

DD

AA

CCBB

DD

Control Disease Rewiring network

Hou et al. (2014) Human Molecular Genetics

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

Signal enrichments in DHS sites

Hou, Ma, Zhao (2014)

Better replication rates at DHS sites

Hou, Ma, Zhao (2014)

Weighted scheme to integrate DHS site information to prioritize SNPs

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

GPA formulation

GPA formulation

GPA formulation

GPA formulation

GPA formulation

GPA formulation

GPA formulation

GPA: Single GWAS

Chung et al. (2014) PLoS Genetics, under revision

GPA: Modeling Pleiotropy

GPA: Modeling Annotation Data

Modeling Pleiotropy and Annotation

Key Assumptions for GPA

Simulations

Comparisons with conditional FDR approach

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

G1/G2 Null Assoc.

Null π00 π01

Assoc. π10 π11

GPA: Hypothesis Testing

Comparisons with GSEA

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.

Five Psychiatric Disorders

BIP: separate analysis BIP: joint analysis

Five Psychiatric Disorders

SCZ: separate analysis SCZ: joint analysis

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.

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