¿Qué sigue después de la identificación de los genes candidatos para las

enfermedades autoinmunes?Christopher J. Lessard, B.Sc., PhD

Investigador Asociado, Departamento de Artritis e Inmunología Clínica, Fundación para la Investigación Médica de Oklahoma, Oklahoma City, OK, E.U.

What's Next After Candidate Gene and Genome-wide Association

Studies in Autoimmunity

Christopher J. Lessard, Ph.D., B.Sc.Associate Research Scientist

Oklahoma Medical Research Foundation

2nd Colombian Autoimmune Symposium3 March 2011

Outline

• Identification of susceptibility loci– systemic lupus erythematosus (SLE)

• Question of missing heritability• Identification of causal variants• Functional studies• Epistasis• Future of genetics

Candidate Gene Studies in SLE

• History of SLE genetics began in 1970s

• C2, C4, C1q very rare but potent risk loci

• Human genome sequence 99.9% complete

• IRF5 most replicated SLE locus

• First genome-wide association study 2006

PAD14

PsorSLE1996

2006

2007

2010

2008

2009

2005

2004

2003

2001

MS CD RAT1DINS

IBD5SH2D2

ACARD1

5

CTLA4

PTPN22 PTPN22

IRF5IL2Ra IL7R

IL23RIFIH1 ICAM-1 FCRL3PHOX2

BATG16L

1

1st GWAS

Impact of Genome Wide Association Studies on Gene Discovery

Very few confirmed associations prior to 2006

PTPN22

PAD14

PsorSLE1996

2006

2007

2010

2008

2009

2005

2004

2003

2001

MS CD RAT1DINS

IBD5SH2D2

ACARD1

5

CTLA4

PTPN22 PTPN22

IRF5IL2Ra IL7R

IL23RIFIH1 ICAM-1 FCRL3PHOX2

BATG16L

1

C12orf30

KIAA350

ERBB3 CD226

Tenr-IL2 PTPN2

IL7R NKX2-3 10q21ATG16L

1PTPN2

IL12B 3p21IRGM 5p13

TRAF1STAT4

ITGAVTNFAIP

3CARD8

FOXJ1 IL2Ra CD58

IL7RADAM3

3IL12B

IL23R

PTPN21CLEC16

AC12orf

30BACH2

PRKCQ

IFIH1

PTPN11

ERBB3 K1F1B

PTPN1

JAK2

CTSH CTLA4

ORMDL3

IL23R

IRGMPTGER

4IL12B

PTPN2

NOD2ATG16L

1

STAT3

MST1

CDKAL1

ZNF365

TNFSF15

CCR6ITLN1

MUC19ICOSLG

C11orf30

PTPN22

OLIG3

NKX2-3

TRAF1

CD40

STAT4

TNFAIP3

ITGAM

PHRF1

SPP1

PXK

BLK

BANK1

DLK1 RIO3

MEG3 TYK2

C10orf59

IL2RA

SHBB3 IL27

LRRC18PTPN2

IL10

RTL1 INS

HIC2

C6orf173

GLIS3 UBASH3A BLK

CTLA4

IL2 CD69

IRF8

TNFRSF1A

TNFSF4

REL

STAT4CD6

ETS1CYP27B

1

JAZF1

IKZF1

UBE2L3

RASGRP3

ORMDL3

ATG5

PRDM1SLC15A

4

WDFY4

TNIP1

IL13

TNIP1TNFAIP

3

IL23A

STAT2

CCR6 AFF3

RBPJ IL2RA

ANKRD55 IRF5

IL6ST CCL21 STAT3 CBLB

SPRED2

METTL1 ETS1DQA1

CD40

6q21

5q33.3

ICA1

C8orf12

XKR6

SCUBE1

NMNAT2

8p23.1LYN 1q25.1

UHRF1BP1

IL10

7q11.23

CD44

IRF8

1st GWAS

Impact of Genome Wide Association Studies on Gene Discovery

PTPN22

2008: New SLE Genes Revealed by GWAS

Association between a SNP and disease status

DD 10/20 = .50 Dd 6/20 = .30 dd 4/20 = .20

DD 2/20 = .10 Dd 4/20 = .20 dd 14/20 = .70

Affecteds Controls

Test distributions for

a statistically significant difference

For “D” = disease risk allele:

MH

C

IRF5

TN

FAIP

3

MN GWAS Results Summary

431 SLE cases; 2155 controls

Graham et al. Nat Genet 2008

P=5x10-8 (genome-wide significant)

P=9x10-7 (suggestive)

Sig

nifi

canc

e le

vel

SNP Location (by chromosome then base pair)

Genome-wide - P=5x10-8

Suggestive - P=9x10-7

Many Genes, Fewer Genetic Pathways in SLE

Association of ~35 genes robustly confirmed, more on the way b((Modified from Moser et al, Gene & Immunity SLE Genetics Special Issue, 2009)

Innate Immune Response

Lymphocyte Activation/Functi

on

TLR/IFN signaling

STAT4

IRAK1

Pathways GenesDendritic cells

Macrophages

IRF5

Autoreactive T cells

Autoreactive B cells

Immune Complex

Clearance

TNF/NFκB signaling TNFAIP3

T Cell signaling

B Cell signaling

HLA-DR PTPN22

BLK BANK1

Macrophages

Neutrophils

Phagocytosis FcγR3A

FcγR2B

C4A C2

FcγR3B

C4B

IRAK1 TNFSF4

Other

PXK XKR6ICA1

ATG5

NMNAT2

MECP2

ITGAM

PDCD1

SCUBE1

UBE2L3

KIAA1542

CRP

Complement

STAT4

C1q

Apoptosis

Ubiquitination

DNA methylation

Unknown

Cellular adhesion

TNFAIP3

SPP1

HLA-DR

ITGAM

Cells

STAT4

LYN

TREX1

CD44

More Loci Identified in 2009-present

• Continued replication in European cohorts

• GWAS replication limited to top few hits– CD44 ranked ~2000– Ranking of SNPs by

p-value not optimal– Common or rare

variants hard to detect in small sample sizes

Asian GWAS completed in 2009

• First SLE GWAS in non-European population

• Evidence for susceptibility loci unique to Asians

• Some regions not evaluated in Europeans

Question of Missing Heritability

• Many risk loci, but marginal risk – odds ratios (OR) typically < 2.0– HLA and TNFAIP3 OR ≈ 2.5

• HLA and IRF5 account for ~1% of heritable risk for Europeans

• With all loci identified (~35), it is estimated that only 8-12% of heritable risk for Europeans has been identified

• Why?

GWAS to Date Limited

• Many of the causal variants yet to be identified

• Functional consequences remain elusive

• No subphenotype GWAS• Limited resequencing to find rare

variants• Methylation not comprehensively

studied • No whole transcriptome sequencing • No Amerindian and African-American

GWAS

Causal Variants Bottleneck

• Linkage Disequilibrium (LD)– Correlation between

variants– Aids identification of

association during GWAS (lowers cost)

– Makes causal variant localization difficult

CD44 Replication Dataset and Association Analysis

Analysis: Logistic regression (PLINK)

Compare distribution (frequencies) of SNP genotypes (DD, Dd, dd) between

cases and controls to identify a statistically significant difference

P-value thresholds for two levels of significance:

“Genome-wide” = p < 5 x 10-8

“Suggestive” = p-value < 1 x 10-4

GWAS identified 2 SNPs p=3x10-3

Cases Controls

Total

Europeans (EU) 3562 3491 7053

African Americans (AA)

1527 1811 3338

Asians (AS) 1265 1260 2525

Hispanics (H) 961 336 1297

Gullah (G) 152 123 275

7998 7492 15490

Replication of Association at 11p13 near CD44

meta-analysis

Results After Imputation

Association with Other Ethnic Groups

• Both AA and Asians associated with rs2732552:

- AA: P=5.0x10-3

- Asian: P=4.3x10-4

- Combined all Europeans, Asians and African-Americans: Pmeta=3.0x10-13

• rs387619 was associated with Asians, but not AA

• No evidence of association with Hispanics, Gullah or Amerindians

EuropeanAsian

African-American

Regulatory Region

• Haplotype contains several

regulatory elements:

- ~74 Kb telomeric of

CD44 and ~61 Kb

centromeric of PDHX

- Several regulatory sites

identified by mining

ENCODE ChIP-Seq data

CD44• Widely expressed in immunological cells

- Numerous splice variants

- Tissue specificity

- Variable exonic region

• Cell-surface glyoprotein involved in cell-cell

interactions, cell adhesion and migration

• Receptor for:

- Hyaluronic acid (HA), osteopontin,

collagens, matrix metalloproteinases

(MMPs)

• Accessory molecule in the synapse between

APC-T cell complex

~93Kb

CD44 and SLE

•Expression of protein widely studied

TNFAIP3 Putative Causal Variant

NF-B Signaling

TNFAIP3 encodes the protein A20

Vigo Heissmeyer & Anjana Rao, Nature Immunology 9, 227 - 229 (2008)

Attenuates TNF signaling

Association of TNFAIP3

• Identified a broad region of association

• No association in African-Americans

• Imputation and resequencing reveal putative causal variant

Europeans

Asians

Koreans

Imputation and Resequencing

• Imputation found a novel SNP in the 1000 Genomes dataset

• Resequencing identified a deletion/insertion polymorphism

• Together form TT>A putative causal polymorphism• Conditional analysis

Transcription Factors Sites Identified

• Chromatin immunopreciptitation followed by sequencing by ENCODE project

• Used this data to begin functional studies

Risk Variants Affect Biology

• Found difference in binding using electrophoretic mobility shift assays

• mRNA expression altered

• Protein expression altered

Epistasis

• Gene-gene interactions• Usually done statistically• No evidence for epistasis in regions found

in SLE work• TNFAIP3 and TNIP1 both associated with

SLE and do interact biologically

Future of Autoimmune Genetics

• Essential to recruit more subjects• Much larger GWAS:

– >50,000 subjects– More variants (>5 million)– Makes subphenotype studies possible

• Whole genome sequencing• Whole transcriptome sequencing• eQTL analysis with GWAS data• Epigenetic studies

Power to Detect Association

• Power influenced by:– Allele freqency– Odds ratio– Sample size

• Need large sample size for rare variants and recessive effects

USA LFRR Collaborators & Approved Users

Andrey ShawWashingtonU

Missouri

Joseph McCuneUniversity of Michigan

Michigan

Timothy BehrensUniversity of Minnesota

Minnesota

Michael SchneiderSouthern Illinois UTimothy NiewoldTammy UtsetUChicago

Illinois John B. HarleyCincinnati Children’s Hospital MCJane OlsonCase Western University

Ohio

J. Lee NelsonF. Hutchinson Cancer Res CtrGerald NopomVirginia Mason Research Ctr

Washington

Steve BindorBio-Rad LabsLisa BarcellosUC BerkeleyEvan HermelTouro UniversityLindsey CriswellUCSFBetty TsaoUCLAAnshu AgrawalUniv CaliforniaMariana IsraeliDan WallaceMichael WeismanCSMCChaim JacobUSC

California

Gary GilkesonDiane KamenMUSC

South Carolina

Bart HaynesDuke University

North Carolina

Andras PerlSUNYJane SalmonHospital for Special SurgeryNicholas ChiarazziCharles ChuPeter GregersonNorth Shore University Hospital

New York

Harold ChapmanHarvard Medical SchoolBrigette HuberTufts UniversityPatricia FraserBrigham & Women’s Hospital

Massachusetts

Mark ShriverPenn State UKathleen SullivanChildren’s Hospital of Philadelphia

Pennsylvania

Mark MamulaYale University

Connecticut

Jonathan ChairesJames Brown Cancer CenterBart HaynesDukes UniversityDama LaxminarayanaWake Forest University

Kentucky

OklahomaMorris FosterOUKathleen O’NeilOUHSC

OMRFMarta E. Alarcón-RiquelmeDarise FarrisBart FrankJudith JamesKen KaufmanBiji KurienJoan MerrillCourtney MontgomeryKathy MoserPatrick GaffneySwapan NathAmr SawalhaHal ScofieldEdward Wakeland

David KarpUT Southwestern MC

Texas

Robert KimberleyUAB

Alabama

International LFRR Collaborators & Approved Users

United Kingdom

Timothy VyseImperial College of Science, Technology & MedicineGrant GallagherUniversity of Glasgow

Puerto RicoAna QuinteroOMRF

SpainManual Ramos-CasalsBarcelona Hospital

South KoreaBao Sang-CheolUniversity of Seoul

DenmarkRuna NolsoeSteno Diabetes Center

JapanSachiko HirosaJuntonda U School of MedicineSumida TakayukiUniversity of Tsukuba

AustraliaCarola VinuesaAustralian National University

Juan-Manuel AnayaAdriana RojasUniversity del Rosario

Colombia

Ontario

Rose GoldsteinUniversity of OttawaAndrew PatersonUniversity of Toronto

Roland JonssonUniversity of Bergen

Marta AlarconUppsala University

Markus PerolaNational Public Health Institute

Norway

Sweden

Finland

SGENE: The Sjogren’s Genetics Network

International group interested in contributing DNA samples and clinical data for genetic studies

Currently:15 contributing sites (plus subsites) >2000 SS cases (AECG criteria), 2000 controlsDeveloping consistent clinical dataset

Goal:Continue to expand sitesOverall: 15,000+ cases

Primary cohort for Replication Studies

The LFRR always needs more participants

lupus-recruiters@lupus.omrf.orglupus.omrf.org

More Sjögren’s syndrome Patients Needed

lessardc@omrf.org

The Lupus Family Registry & Repository