genetic testing and the prevention of type 1 diabetes janice s. dorman, ph.d. september 4, 2001

Genetic Testing and the Genetic Testing and the Prevention of Prevention of

Type 1 DiabetesType 1 Diabetes

Janice S. Dorman, Ph.D.Janice S. Dorman, Ph.D.

September 4, 2001September 4, 2001

Type 1 DiabetesType 1 Diabetes

One of most frequent chronic One of most frequent chronic diseases of children diseases of children - - Prevalence ~ 2 / 1000 in Allegheny Prevalence ~ 2 / 1000 in Allegheny

County, PACounty, PA

Epidemiology of type 1 diabetes has Epidemiology of type 1 diabetes has been studied at the University of been studied at the University of Pittsburgh since 1979 Pittsburgh since 1979 - Dr. Allan Drash and Dr. Lewis Kuller- Dr. Allan Drash and Dr. Lewis Kuller

Type 1 Diabetes IncidenceType 1 Diabetes IncidenceAllegheny County, PAAllegheny County, PA

0

5

10

15

20

25

30

5 10 15 20

Age in Years

per

100

,000

/yr

WM

NWM

WF

NWF

Type 1 Diabetes Incidence Type 1 Diabetes Incidence Allegheny County, PAAllegheny County, PA

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Season

/100

,000

/yr

Type 1 Diabetes Incidence Type 1 Diabetes Incidence Allegheny County, PAAllegheny County, PA

0

5

10

15

20

25

1975-79 1980-84 1985-89

WM

NWM

WF

NWF

Type 1 Diabetes Incidence Type 1 Diabetes Incidence WorldwideWorldwide

05

10152025303540

Rat

e/10

0,0

00/y

r

Specific Environmental Specific Environmental Risk FactorsRisk Factors

Case-control studies - conflictingCase-control studies - conflicting Possible risk factorsPossible risk factors

- Infant diet or lack of breast feeding- Infant diet or lack of breast feeding- Childhood diet- Childhood diet- Viruses (exposure as early as in utero)- Viruses (exposure as early as in utero)- Hormones- Hormones- Stress- Stress

May act as initiators or precipitatorsMay act as initiators or precipitators

Evidence for GeneticEvidence for Genetic Risk Factors Risk Factors

Increased risk for 1st degree relatives Increased risk for 1st degree relatives of affected individualsof affected individuals

Concordance in MZ twins 20 - 50%Concordance in MZ twins 20 - 50% Recent genome wide screens have Recent genome wide screens have

revealed 15+ possible susceptibility revealed 15+ possible susceptibility genesgenes

Associations with HLA class II alleles Associations with HLA class II alleles in all populationsin all populations

Genome Screens for Genome Screens for Type 1 DiabetesType 1 Diabetes

IDDM1IDDM1 6p21.36p21.3

IDDM2IDDM2 11p15.511p15.5

IDDM3IDDM3 15q2615q26

IDDM4IDDM4 11q13.311q13.3

IDDM5IDDM5 6q156q15

IDDM6IDDM6 18q12-q2118q12-q21

IDDM7IDDM7 2q31-332q31-33



IDDM10IDDM10 10p11-10p11-q11q11

IDDM11IDDM11 14q24-14q24-q31q31



IDDM14IDDM14 NDND


* Candidate Gene * Candidate Gene *Possible Candidate *Possible Candidate *No Candidate Gene*No Candidate Gene

Interpreting Linkage Interpreting Linkage Analysis for Type 1DiabetesAnalysis for Type 1Diabetes

Need to control for effect of HLANeed to control for effect of HLA Some genes confer susceptibility in Some genes confer susceptibility in

absence of high risk HLA haplotypesabsence of high risk HLA haplotypes Need model- free statistical methodsNeed model- free statistical methods Account for gender, parent-of-origin Account for gender, parent-of-origin

effects and environmental risk effects and environmental risk factorsfactors

May not be appropriate phenotypeMay not be appropriate phenotype

Genome Screens forGenome Screens for Type 1 Diabetes Type 1 Diabetes

Chromosome 6Chromosome 6

IDDM8 IDDM8 6q25-276q25-27

IDDM15 IDDM15 6q216q21

Chromosome 2Chromosome 2

IDDM7 IDDM7 2q31-33 2q31-33 HOX8, IL-1HOX8, IL-1family IDDM12 family IDDM12 2q332q33 CTLA4, CTLA4, CD28CD28 IDDM13 IDDM13 2q342q34IGFBP2, IGFBP5IGFBP2, IGFBP5

* Candidate Gene * Candidate Gene *Possible Candidate *Possible Candidate *No Candidate Gene*No Candidate Gene

Genome Screens for Genome Screens for Autoimmune DiseasesAutoimmune Diseases

Candidate Genes - Type 1 DiabetesCandidate Genes - Type 1 DiabetesIDDM1IDDM1 6p21.36p21.3 DR-DQ, 2nd loci - TNF?DR-DQ, 2nd loci - TNF?

IDDM2IDDM2 11p15.511p15.5 INS-VNTRINS-VNTR

IDDM12IDDM12 2q332q33 CTLA4, CD28CTLA4, CD28

Candidate Genes - Other DisordersCandidate Genes - Other DisordersIDDM1IDDM1 ATD, CD, RA, MS, SLEATD, CD, RA, MS, SLE

IDDM2IDDM2 SLE, ankylosing spondylitisSLE, ankylosing spondylitis

IDDM12IDDM12 ATDATD

WHO DiaMond Molecular WHO DiaMond Molecular Epidemiology StudyEpidemiology Study

Have evaluated HLA DQ Have evaluated HLA DQ Best single genetic markerBest single genetic marker

Evaluate other candidate genesEvaluate other candidate genesIDDM1IDDM1 HLA DR, DPHLA DR, DPIDDM2 IDDM2 INS-VNTRINS-VNTRIDDM12IDDM12 CTLA4CTLA4OthersOthers VDR, HLA class IVDR, HLA class I

WHO Multinational Project WHO Multinational Project for Childhood Diabetes for Childhood Diabetes

(DiaMond)(DiaMond)

What is Causing the Tremendous What is Causing the Tremendous Geographic Variation in Incidence Geographic Variation in Incidence

of Type 1 Diabetes?of Type 1 Diabetes?

Monitored Incidence WorldwideMonitored Incidence Worldwide

1990 - 20001990 - 2000

WHO Collaborating Center WHO Collaborating Center for Diabetes Registries, for Diabetes Registries, Research and TrainingResearch and Training

Ron LaPorte, Ph.D.Ron LaPorte, Ph.D. Disease Monitoring &Disease Monitoring &TelecommunicationsTelecommunications

Jan Dorman,Ph.DJan Dorman,Ph.D.. Molecular EpidemiologyMolecular Epidemiology

University of PittsburghUniversity of Pittsburgh

WHO DiaMond Molecular WHO DiaMond Molecular Epidemiology StudyEpidemiology Study

HypothesisHypothesis

Geographic differences in type 1 diabetes Geographic differences in type 1 diabetes incidence reflect population variation in incidence reflect population variation in the frequencies of disease susceptibility the frequencies of disease susceptibility genesgenes

20+ countries participating20+ countries participating Focus on 2, 1, or 0 high risk HLA-DQ Focus on 2, 1, or 0 high risk HLA-DQ

haplotypes (SS, SP, PP)haplotypes (SS, SP, PP)

Relative Increase In RiskRelative Increase In Risk

Population Population SS SS SPSP PPPP

CaucasianCaucasian†† 15.915.9 4.04.0 1.01.0**

Af AmericansAf Americans†† 44.844.8 7.37.3 1.01.0**

AsianAsian‡‡ 10.710.7 3.63.6 1.01.0**

* p < 0.05, test for trend* p < 0.05, test for trend††Allegheny Co, PA and Jefferson Co, ALAllegheny Co, PA and Jefferson Co, AL‡‡Hokkaido, Japan and Seoul, KoreaHokkaido, Japan and Seoul, Korea

Cumulative Risk Through Cumulative Risk Through Age 30 YearsAge 30 Years

Population Population SS SS SPSP PPPP

CaucasianCaucasian†† 2.6%2.6% 0.7%0.7% 0.2%0.2%

Af AmericansAf Americans†† 3.1%3.1% 0.5%0.5% 0.1%0.1%

AsianAsian‡‡ 0.2%0.2% 0.1%0.1% 0.02%0.02%

††Allegheny Co, PA and Jefferson Co, ALAllegheny Co, PA and Jefferson Co, AL‡‡Hokkaido, Japan and Seoul, KoreaHokkaido, Japan and Seoul, Korea

Population Attributable Population Attributable FractionFraction

Population Population SS SS SS or SPSS or SP

CaucasianCaucasian†† 36.2%36.2% 66.6%66.6%

Af AmericansAf Americans†† 43.5%43.5% 74.9%74.9%

AsianAsian‡‡ 18.8%18.8% 53.3%53.3%

††Allegheny Co, PA and Jefferson Co, ALAllegheny Co, PA and Jefferson Co, AL‡‡Hokkaido, Japan and Seoul, KoreaHokkaido, Japan and Seoul, Korea

What do these data tell us?What do these data tell us?

Increased risk for individuals with SS Increased risk for individuals with SS and SP genotypes, relative to PP, and SP genotypes, relative to PP, with a significant dose responsewith a significant dose response

Cumulative risk for SS individuals in Cumulative risk for SS individuals in high-moderate incidence countries high-moderate incidence countries approaches rates for first degree approaches rates for first degree relatives; relatives; 3 - 6%3 - 6%

What do these data tell us?What do these data tell us?

Contribution of the highest risk HLA-Contribution of the highest risk HLA-DQ genotypes to type 1 diabetes DQ genotypes to type 1 diabetes incidence varied from 19% - 43% incidence varied from 19% - 43% across populationsacross populations

More than 50% of the incidence of More than 50% of the incidence of type 1 diabetes is type 1 diabetes is NOTNOT explained by explained by the highest risk HLA-DQ genotypesthe highest risk HLA-DQ genotypes

Gene - Environment Gene - Environment InteractionsInteractions

Exposure Exposure increased risk by increased risk by 1/100,000 / year 1/100,000 / year among among susceptiblessusceptibles

Overall population Overall population risk would risk would increase by increase by 0.8%0.8% 0

5

10

15

20

25

30

35

Baseline AfterExposure

FinlandFinland

0

0.1

0.2

0.3

0.4

0.5

0.6

Baseline AfterExposure

ChinaChina

Gene - Environment Gene - Environment InteractionsInteractions

Exposure Exposure increased risk by increased risk by 1/100,000 / year 1/100,000 / year among among susceptiblessusceptibles

Overall population Overall population risk would risk would increase by increase by 10%10%

Molecular Epidemiology of Molecular Epidemiology of Type 1 Diabetes in ChinaType 1 Diabetes in China

What is contributing to the low overall What is contributing to the low overall incidence and large variation in risk within incidence and large variation in risk within China?China?

- Etiological heterogeneity- Etiological heterogeneity

- Susceptibility genes- Susceptibility genes

- Environmental risk factors- Environmental risk factors Project based on DiaMond registry Project based on DiaMond registry

networknetwork Model study for molecular epidemiologyModel study for molecular epidemiology

Rate (per 100,000)0 1.8

** ******

********

******

** **

**

****

**

**

Molecular Epidemiology of Molecular Epidemiology of Type 1 Diabetes in ChinaType 1 Diabetes in China

Data collection completed in 1999 Data collection completed in 1999 - Dr. Yang Ze- Dr. Yang Ze

296 cases, 528 controls; 18 centers296 cases, 528 controls; 18 centers Molecular analyses - BeijingMolecular analyses - Beijing

- HLA DRB1, DQB1 typing- HLA DRB1, DQB1 typing Serological analyses - PittsburghSerological analyses - Pittsburgh

- GAD, IA-2, TPOAb, TGAb, C-pep- GAD, IA-2, TPOAb, TGAb, C-pep Environmental data - PittsburghEnvironmental data - Pittsburgh

- Nutrition, infections, pollution- Nutrition, infections, pollution Dissertation for Dr. Elsa StrotmeyerDissertation for Dr. Elsa Strotmeyer

Jan Alice Lew Yang Ze

genetic testing and the prevention of type 1 diabetes janice s. dorman, ph.d. september 4, 2001

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