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Monogenic diabetes. Multifactorial type 2 diabetes. Syndromic diabetes. Type 1 DM. LADA. Diabetes. environments. maternal genotype. childhood. prenatal. adult. postnatal. risk of T2D. individual genotype. paternal genotype. Genes and environment. familial clustering

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Diabetes

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Diabetes

Monogenic

diabetes

Multifactorial

type 2

diabetes

Syndromic

diabetes

Type

1

DM

LADA

Diabetes

HugeNET (Venezia)


Genes and environment

environments

maternal

genotype

childhood

prenatal

adult

postnatal

risk of

T2D

individual

genotype

paternal

genotype

Genes and environment

  • familial clustering

  • twin studies

  • adoption studies

  • migration studies

  • admixture studies

  • gene discovery

  • secular trends

  • migration studies

  • twin studies

  • transgenerational effects

  • intervention studies

Environment

Genes

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Activity in the field

Activity in the field

  • ~(3)000 association studies in T2D

  • Vast majority are small (n=hundreds) case-control studies vulnerable to the usual litany of errors, biases, problems

  • Growing number (~50) of large (>5000 samples) studies from limited number (4) of groups which have examined ~30 genes (NB many of these in more than one large study); all European eg UK 4000 cases & controls; France 3000; DK 2500; Broad/Lund 5000

  • Genome wide association studies underway in ~8000 case-control pairs (by several groups)

  • Most studies use case-control design….in a few centres these are nested from within larger cohorts (but generally small n)

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Three steps to failure

Three steps to failure….

P (no true association

given positive

finding)

Wacholder S et al, JNCI, 2004

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Some issues

Some issues

  • Cases: T2D = diagnosis of exclusion; no clear basis for subdividing cases into more homogeneous groups; some use population base, others selected for FH+ or early onset

  • Controls:some use population controls (modest classification bias), others “hypernormal” controls. Should not be a major issue for power or effect size estimation;

  • Other phenotypes: -- continuous: glucose, HbA1c-- intermediate: beta-cell function, insulin sensitivity-- related: BMI, WHR, lipid measures

  • Phenotypic accuracy: variable, mostly good

  • Genotyping accuracy: variable, (historically = poor)

  • Statistical prowess: variable

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Atlas of diabetes susceptibility

HNF1A

PNDM

otherMODY

TNDM

Other rare

syndromes

Atlas of diabetes susceptibility

Effect

size

few if any genes

up here

Large

Beyond the scope

of genetics!

Allele frequency

Small

Common

Rare

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Diabetes

PPARG Pro12Ala and T2D

Altshuler et al, NG, 2000

1.25 relative risk

85% susceptibility allele frequency

~25% attributable risk

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Kir6 2 kcnj11

Kir6.2 (KCNJ11)

E23K

1.16 relative risk

40% susceptibility allele frequency

~10% attributable risk

Gloyn et al, Diabetes 2003

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Tcf7l2

TCF7L2

Grant, Nat Genetics, March 2006

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Tcf7l2 associations

TCF7L2 associations

Grant, Nat Genetics, March 2006

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Case control data relative risks

Case control data – relative risks

* rs7903146: GRR for comparison of heterozygote and rare homozygote, 1.41 (95% CI, 1.15-1.72) P=8.6x10-4

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More tcf7l2

More TCF7L2

Florez et al, 2006

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Tier 1 definites

Tier 1 – definites

AUC=58%

2500 T2D, 3500 controls – UK

Weedon et al, PLOS Med 2006

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Reliability and heterogeneity

Reliability and heterogeneity

  • For “real” results, relatively little heterogeneity of effect size (I know this is a circular argument…)

  • Little evidence of GGI or GEI (but then again, how would we know?)

  • Based on “proven” examples, much harder to find associations with the intermediate traits than with end-phenotypes (presumably because we measure the former so badly)

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Tier 1 the possibles

Tier 1: the possibles

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Diabetes

LMNA

  • Lamin A/C

  • Nuclear membrane protein

  • Associated with diverse disease – laminopathies

  • Rare mutations familial partial lipodystrophy

  • ? Association with T2D

  • Maps to chr 1q: replicated linkage

  • H556H associations with metabolic syndrome

  • Monogenic multifactorial: another example?

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Uk study

UK study

+ 2817 1q samplesOR 1.07 (0.99-1.15), p=0.08

+ other published dataOR 1.08 (1.01-1.16), p=0.04

+ unpublished dataOR 1.10 (1.04-1.16), p=0.001

Owen KR et al, in press

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Tier 1 the unlikely ones

Tier 1: the unlikely ones

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Diabetes

  • AXOLOTL consortium

  • UK

  • Denmark

  • Sweden

  • Cambridge, Mass

  • KCNJ11, PPARG, CAPN10

  • 15k samples

  • Is there more to PPARG than P12A?

Going beyond the association

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Atlas of diabetes susceptibility1

mt3243

LMNA

Atlas of diabetes susceptibility

Effect

size

HNF1A

PNDM

otherMODY

few if any genes

up here

Large

TNDM

Other rare

syndromes

TCF7L2

PPARG

LARS2

HNF1A

KCNJ11

CAPN10

ENPP1

ACDC

HNF4A

Beyond the scope

of genetics!

LMNA

INS

Allele frequency

Small

Common

Rare

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Wellcome trust case control consortium

www.wtccc.com

www.wtcc.org.uk

Wellcome Trust Case Control Consortium

WTCCC:

www.wtccc.org.uk

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Wtccc

To be finalised

Affymetrix 500k

2000 malaria

2000

T1D

2000

T2D

2000 Gambiancontrols

2000

RhA

2000 TB

3000

UK

common

controls

2000

CHD

“African” diseases

15k Infinium nsSNP

2000Crohns

2000

HT

1000

MS

1000

BrCa

2700 bipolar

1500

UK

common

controls

Main study with national cases/controls

2000 cohort

2000 obese

1000

thyroid

1000Ank S

Obesity case-cohort (Norfolk EPIC)

nsSNP experiment 15k Infinium

WTCCC

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Wtccc controls

WTCCC: controls

Nationally representative

Unselected for T2D

Other forms of diabetes “excluded”

Enriched for early onset, FH+

2000

T1D

2000

T2D

2000

T2D

2000

RhA

3000

UK

common

controls

2000

CHD

3000

UK

common

controls

2000Crohns

2000

HT

2700 bipolar

1500

UK

blood donor

controls

1500Birth

Cohort

1958

Main study with national cases/controls

Nationally representative

Unselected for disease phenotype

Common controls

Middle-aged – minimise survivor bias

Phenotypes++ for 58BC

DATA RELEASE….

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Even a billion genotypes is not enough

1000ccp

WTCCC

6200ccp

PPARG P12A

2000

~10

KCNJ11 E23K

10-100

3000

TCF7L2

10-100

Gene X

MAF 0.15; GRR 1.2; PAR 10%

40000

1400

Even a billion genotypes is not enough

Number

of positives

null

alternate:perfect data

alternate:real data

Uncorrected P

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Combining data

Combining data

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Diabetes

“IGWANA” consortium

  • Integrated Genome Wide Association ANAlysis in Type 2 Diabetes

  • Collaborative effort to share data from these (and other) scans toallow joint analyses…

  • to provide rapid replication of “big” signals

  • to allow more informed selection of SNP subsets for stage 2 genotyping

  • to allow formal meta-analysis of genome wide data across >6000 case-control pairs

  • to exchange experience, ensure high quality research

  • type 2 diabetes case-control studies (initially, at least)

  • genome wide association scan data (100k chip entry level)

  • both summary and individual patient data

  • contact [email protected]; [email protected]; [email protected]

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Follow up

Direct

typing

(selected

signals)

epidemiology

DGDG

Sequencing

genetics

DGI

physiology

SNP

selection

(p<0.001)

combinedanalysis

Finemapping

2nd stage

genotyping

Repli

cation

WTCCC

cell biology

FUSION

clinical diagnostics

CNVs

pharmacogenetics

Phase III

Phase IV

Phase 0

Phase I

Phase II

Follow up

joint

P<10-2

P<10-6

P<0.001

4000 cases

4000 controls

5000 cases

5000 controls

5500 cases

6500 controls

2000 cases

3000 controls

>100,000 samples

~30 signals

0% FPRP

~30 signals

<10% FPRP

~1500 SNPs

>95% FPRP

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Summary

Summary

  • Usual mess where small studies are concerned: heterogeneity and untraceable error and unfathomable biases suggest that little to be learned from such studies

  • More consistent picture when large studies are concerned (as consistent as sample size and effect size will allow) and much better “accountability”

  • GWA studies offer huge opportunities, but the scale of these studies means that replication, validation, extension is more vital than ever

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