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Applications of GWAS and Next-Gen Sequencing Technology to the Study of Viral and Drug-Induced Liver Disease

Thomas J. Urban, PharmD, PhD

Center for Human Genome Variation

Duke University Medical Center

3.22.2011

Outline

• Value of common and rare genetic variants in predicting disease and drug response (GWAS)

– Hepatitis C treatment outcomes

– Drug-induced liver injury

• Potential value of rare variation in prediction of serious adverse drug events

Challenges of PegIFN + ribavirin treatment for HCV infection

• Only 40-50% of patients with HCV

genotype 1 show sustained virological response (SVR)

• Serious adverse events, so many drop out prior to completion of 48 week course of therapy

• Treatment is 2 fold more effective in groups of European vs. African ancestry with HCV genotype 1

Study design

N = 871 Caucasians

N =75 Hispanics

N = 191 African Americans

N = 1671

DNA genotyping Nature, 2009

N = 1137

Exclusions: Undetectable HCV RNA at baseline Other/unknown ancestry Compliance < 80% at 12wk QC failure

N = 3070

IDEAL Study NEJM, 2009

Quality control 2 Gender, relatedness, HWE…

Genotypic calls

Correction for population stratification, correcting for

local genomic characteristics

Signal intensity

RAWCNV format

PennCNV

Standard PED/MAP formats; PLINK binary BED/BIM/FAM

formats

*EIGENSOFTplus

*PipeCMD

PLINK/*PipeCMD

Quality control 3

Enrichment of SNP functions and

gene sets

Association with common CNVs

Quality control 4 - Lab re-check Export to WGAViewer for annotation

Rare/large CNV/Pathway

analysis

Association analysis

Logistic regression Linear regression Survival analysis

Genomic Analysis Facility Quality control 1

PLINK/GWSurv

Computer software used:

PipeCMD

PLINK EIGENSOFTplus

WGAViewer

rs12979860 on chromosome 19 is strongly associated with SVR

Ge D. et al, Nature 2009

CC genotype at rs12979860 associates with a 2-3 fold

greater rate of SVR than CT/TT genotype

Ge D. et al, Nature 2009

Ge D. et al, Nature 2009

Marcello T et al., Gastroenterology 2006

Possible mechanisms for cis-acting IL28B genetic variants

IL28B variants: effects on IL28B expression in blood or PBMCs

Tanaka et al., Nat Genet 2009

Suppiah et al., Nat Genet 2009

Urban TJ et al., Hepatology 2010

Differential hepatic gene expression by IL28B genotype:

N = 61 patients with

Chronic HCV infection

Liver mRNA expression

Profiling on Illumina HumanHT-12

Expression microarray

-37G>C

(rs28416813)

Lys70Arg

(rs8103142)

1,340 bp

IVS2+134C>T

(rs11881222) 3’UTR+52T>G

(rs4803217) -312A>G

(rs4803219)

Fine mapping of IL28B gene region

IFN-λ3-Arg70Lys: Potential role in receptor binding affinity/specificity?

Gad HH et al., J Biol Chem 2009

Antiviral potencies of E. coli-expressed IFN-λ3 variants

Drug-Induced Liver Injury (DILI)

• Primary cause of drug attrition from market

• Idiosyncratic, suspected genetic predisposition

• Most represented drugs among DILI cases:

– Amoxicillin/clavulanate

– Valproic Acid (VPA)

– Isoniazid (INH)

– Nitrofurantoin

– Minocycline

• Common variation implicated in risk, but positive predictive value (PPV) fairly low

• Rare variation may contribute significantly to this rare adverse drug event

The Drug-Induced Liver Injury Network (DILIN)

https://dilin.dcri.duke.edu/

Potential to capture 20 Million lives

Coordinated by DCRI

Proposed Mechanisms for DILI

www.liv.ac.uk/drug-safety/Research/dili.htm

Augmentin liver injury study

145 Cases

iSAEC/DILIN Collaboration

201 Genotyped Cases

56 Cases

Augmentin GWAS results 201 cases, 532 POPRES controls

MHC

Lucena MI et al., 2010 (under review)

Augmentin MHC association Top SNP Chr:Mbp P-value OR (95% CI) P-value conditioned on

rs3135388 (DR2)

P-value OR (95% CI)

rs9274407 6:32.74 4.8e-14 3.1 (2.2-4.2) 0.00011 3.2 (1.8-5.8)

DR2 tag

Lucena MI et al., 2010

Augmentin MHC association Conditioned on top class II SNP

Top SNP Chr:Mbp P-value OR (95% CI)

rs2523822 6:29.94 1.3e-9 2.3 (1.7-2.9)

Lucena MI et al., 2010

Augmentin MHC association Conditioned on top class I and II SNPs

Lucena MI et al., 2010

Augmentin top SNPs and HLA alleles

HLA-DRB1 HLA-DRA1

rs3135388 HLA-DRB1*1501 (and DR2) rs9274407

r2 ~ 1

r2 ~ 0.77

HLA-DQB1 HLA-A

rs2523822

SNP Tagged

Alleles Notes

rs9274407 DR2 In LD with DR2

rs3135388 DR2 Reported to be associated by two previous

studies;

rs2523822 HLA-A*0201 Not reported in previous studies

HLA-A*0201

r2 ~ 0.9

DR2 = DRB1*1501–DQB1*0602

Augmentin conclusions

29

• DR2 association is confirmed (DRB1*1501)

• Additional association in region of HLA-A

• Significant interaction between HLA-A and HLA-DRB1

• Cannot determine likely causal variants

– Investigation in additional populations likely required to sort out

Genetic Influence on DILI Risk: Across drugs, across phenotypes…

Drug Reaction

Details Prev Risk Allele Freq.

Rel

Risk PPV 1 - NPV

Ximelagatran 0.08 HLA-DRB1*0701 0.08 4 0.22 0.055

Augmentin <0.001 HLA-DRB1*1501

A*0201/B*1801

0.15 4 5.7e-4 5.7e-5

Isoniazid 0.15 CYP2E1*1 & 2 0.13 7 0.59 0.084

Lapatinib 0.09 HLA-DQA1*0201

(HLA-DRB1*0701)

0.08 9 0.17 0.03

Lumiracoxib 0.013 HLA-DRB1*1501 0.15 13 0.039 0.0030

Ticlopidine <0.001 HLA-A*3303 0.07 36 1.2e-3 3.5e-5

Tranilast 0.12 UGT1A1*28 0.30 48 0.23 0.0048

Flucloxacillin <0.001 HLA-B*5701 0.04 81 0.0022 2.8e-5

Irinotecan Neutropenia 0.20 UGT1A1*28 0.30 28 0.36 0.013

Mercaptopurine Neutropenia 0.12 TPMT*2/3A/3B/3C 0.05 9.0 0.77 0.086

Abacavir Hypersensitivity 0.04 HLA-B*5701 0.04 >1000 0.50 5.0e-4

Carbamazepine SJS/TEN 0.003 HLA-B*1502 0.04 >1000 0.038 3.8e-5

Early Conclusions from GWAS

• Top-associated SNPs in for amoxicillin-clavulanate (and flucloxacillin, SAEC) revealed secure associations with common SNPs in the HLA region

– Evidence suggesting possibility of shared HLA risk alleles across different drugs, ADRs

• Other common genetic risk factors for DILI in general, or factors specific to certain clinical presentations (injury type, delay of onset, etc.) were not appreciable

• Thus, a combined analysis of all genotyped cases from DILIN and SAEC provides the best chance of discovering additional common DILI risk alleles (HLA or otherwise)

DILIN-SAEC collaborative GWAS

32

403 Cases

iSAEC/DILIN Collaboration

968 Genotyped Cases (783 European)

655 POPRES Controls

2,345 WTCCC2 Controls

565 Cases

DILIN-SAEC: Total Enrollment

• After genotype and ancestry pruning:

• N=783 DILI cases

– 401 DILIN, 382 SAEC

• N=3,000 controls

– 2,345 WTCCC2, 655 POPRES

• Genotyped on Illumina 1M or 1Mduo array

• 800,769 SNPs overlapping after QC

• Amoxicillin/clavulanate and flucloxacillin highly represented (n=296); stratified analyses performed with and without these cases

All DILI Cases (n = 783)

• Association signal(s) in the MHC region reflect previously-described Augmentin and flucloxacillin risk variants

• No genome-wide significant effects outside of MHC

All Non-Augmentin/Flucloxacillin DILI Cases (n = 487)

• No deviation from expected p-value distribution

• No SNPs in MHC approaching genome-wide significance

Power to detect common genetic

risk factors for DILI

Searching for Rare DILI Risk Variants by Whole-Genome/Exome Sequencing

• Genome-wide association studies have revealed no common genetic risk factors for DILI overall, or to DILI due to individual drugs (sans Augmentin, flucloxacillin), perhaps owing to limited sample sizes

• Common variation cannot be highly predictive of DILI, by definition

• Whole-genome/exome sequencing allows essentially complete survey of both common and rare functional variation across the known functional regions of the genome

• Goal: Identify genetic risk factors causal for DILI, with the aim of developing a diagnostic test with clinically useful predictive power

Study Design Overview

Whole Genome Sequencing

CHGV Genomic Analysis Facility: -12 Illumina Genome Analyzers

-4 Illumina HiSeq 2000s

“Extreme Phenotype” Sequencing

1. Identify subjects (carefully!)

2. Sequence to 30x coverage

3. Alignment of reads to reference genome

4. Identify SNPs, indels and CNVs

– Indels=small insertions/ deletions <50bp

> 3 million SNPs and

> ½ million indels

What to do with it all???

SequenceVariantAnalyzer, software to manage,

annotate, and analyze the large number of

unique variants detected in whole genome

sequencing projects.

http://humangenome.duke.edu/software

Quality filtering

Analysis module:

Analysis module output: • Gene prioritization

• Fisher’s exact test

• Search for deviations from Hardy-

Weinberg Equilibrium

• Search for compound heterozygotes

• Simple listings

• … Whole genome, whole exome, genomic region, or gene specific:

• Variant tables (all variants)

• Variant listings (cases vs. controls)

• Coverage summarizations

Quality Analysis: Coverage

Coverage (Captured

Regions)

% of Captured Region

with >5X Coverage % Pairs Aligned

Average 73.1 0.956 0.953

Standard Deviation 9.62 0.013 0.018

0

1

2

3

4

5

6

7

8

9

60 62.5 65 67.5 70 72.5 75 77.5 80 82.5 85 More

Read Depth

0

2

4

6

8

10

12

14

16

% Bases with RD>5

QA: Concordance with GWAS Data

Based on 20,347 SNVs overlapping exome and

1Mduo data after SNV quality filtering

0

5

10

15

20

25

30

35

Example of a Trait Mapped by Whole Exome/Genome Sequencing: Metachondromatosis

Sobreira NLM et al.,

PLoS Biology 2010

Sobreira NLM et al.,

PLoS Biology 2010

Example of a Trait Mapped by Whole Exome/Genome Sequencing: Metachondromatosis

PTPN11: Protein tyrosine phosphatase SHP-2

• Gain-of-function mutations in multiple Mendelian disorders

• Loss-of-function mutations in metachondromatosis

Example of a Trait Mapped by Whole Exome/Genome Sequencing: Metachondromatosis

Sampling of current sequencing

projects

Neuropsychiatric/Cognition – 100 Schizophrenics (enriched in families)

– 50 individuals with epilepsy (family based)

– Focus on extremes in treatment response/resistance

Drug induced adverse events – Severe Adverse Event Consortium (SAEC) partnership

– Drug-Induced Liver Injury (part of DILIN)

– Clozapine-Related Agranulocytosis

– Drug-Induced Prolonged QT Syndrome, TdP

Other – Children with peanut and other food allergies

– Multiple Mendelian diseases

Conclusions • GWAS have revealed several

common variants of strong effect on some drug response phenotypes

– IL28B and HCV treatment response

– ITPA and RBV-induced anemia

• GWAS have revealed common variants of moderate effect on DILI risk

– Tendency for drug-specific effects

• Comprehensive evaluation of rare functional variation may soon allow for identification of high-impact genetic determinants of DILI and other drug response phenotypes

Acknowledgements • David Goldstein • Paul Watkins • DILIN Clinical

Investigators • Duke CHGV

• Kevin Shianna • Yuki Hitomi • Dongliang Ge • Kristen Linney

• DCRI • John

McHutchison • James Rochon • Thomas Phillips • Katherine Galan

• iSAEC

• Arthur Holden

• Matt Nelson

• Yufeng Shen

• Ann Daly

• NIDDK

• Jose Serrano

• Jay Hoofnagle

• Schering Plough Research Institute (Merck)