Genetics of diabetes Torben Hansen, MD, PhD Steno Diabetes Center STAR Research Course Epidemiology

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Genetics of diabetesTorben Hansen, MD, PhDSteno
Diabetes CenterSTAR Research Course
Epidemiology
Linkage studies
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Phenotypic variationThe clinical presentation or
expression of a specific gene or genes,
environmental factors, or both
agaatttcat atT/Cgtg gaagaggaca
3.2 billion letters of human DNA
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Why spend time on finding genes causing diabetes?

• Molecular understanding

Rational nosological classification and
pharmacogenomics
New targets
Improved dissection of environmental factors
Individual genetic prediction
Novel drugs Gene therapy
Increased motivation for prevention
Personalized treatment and prevention
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Empirical risks for Type 1 diabetes

• Background population 0,4
• Average risk for siblings 6
• One parent with Type 1 diabetes 2-5
• Both parents affected 5-20
• HLA-identical siblings 12
• Monozygotic twins 35-70

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Empirical risks for Type 2 diabetes

• Background population 10
• Average risk for siblings 30-40
• One parent with Type 1 diabetes 30-40
• Both parents affected 50-80
• Monozygotic twins 50-90

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Familial Clustering
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risk to siblings
l
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population prevalence
0.4
T1D
30-40
risk to siblings
l

3-4

population prevalence
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T2D
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The mode of inheritance of IDDM is complex
(read unknown)
A pure multiplicatory model is most often assumed
when evaluating data from genome screenings
l S a x b x c x d x e
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Possible Models for theGenetic Basis of Type 2
Diabetes
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A model for the natural history of T2D
Normal glucose tolerance
Impaired glucose tolerance
Non-diagnosed type 2 diabetes
Type 2 diabetes
0
30
45
60
Age (yr)

• Environmental factors
• Acquired obesity
• Sedentary life style
• Smoking
• Exogenous toxins

• Genes predisposing to
• Insulin resistance
• Insulin deficiency
• Obesity
• Low birth weigth

30-50 of all cases have late diabetic complicati
ons at the time of diagnosis
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(No Transcript)
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The Human Chromosomes

• 46 chromosomes
• - 22 pairs of autosomes
• - 1 pair of sex chromosomes
• Total length of the humane genome
• - 3.3 x 109 basepairs in
• the haploide genome (physical)
• - 3.000 cM (genetic)
• - 1 cM 1 Mb

Two loci which show 1 recombination are
defined as being 1 centimorgan (cM) apart on a
genetic map
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Genetic dissection of diabetes

• Linkage approach
• Large families
• Affected sib-pairs
• Quantitative traits
• Candidate gene approach
• Differential RNA/protein expression
• Animal models
• Bioinformatics

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Genome Scan Concept

• Scan the entire genome with a dense collection
  of genetic
• markers
• Calculate appropriate linkage statistics at
  each posi-
• tion along the genome
• Identify regions which show a significant
  deviation from what
• would be expected under independent
  assortment
• Clone the disease-contributing gene from the
  linked region

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The inheritance of diabetes

• Mendelian

• Complex

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2
1
2
3
1
2
3
4
5
6
7
8
1
2
3
4
1
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Statistics
Replication study MLS gt 1.2 nominal p-value
lt 0.01
All regions with a nominal p-value of p 0.05
encountered in a complete genome scan are worth
reporting - without any claims of linkage
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Comparisons
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Genetic predisposition to IDDM
Locus
Chromosome location
6p2111p1515q2611q136q2518q212q316q25-q273q
21-q2510p11.2-q11.214q24.3-q312q332q346q21 Xq
7p
IDDM1IDDM2IDDM3IDDM4IDDM5IDDM6IDDM7IDDM8ID
DM9IDDM10IDDM11IDDM12IDDM13IDDM15 DXS1068GCK
Davies 1994 and Hashimoto 1994
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• A combined analysis of the UK/US and Scand
• genome-wide scans for linkage to T1D by T1DGC
• Total of 1295 ASP
• Cox et al (2001) AJHG 69820 and ECIGS (2001)
  AJHG 691301
• Conclusions
• Nine candidate regions with Lod scores gt 2
• Five candidate regions in conditional analyses
• 64 of the genome excluded for disease loci
• with ?S gt 1.3.

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Scepticism
We finished the DM genome map - now we cant
figure out how to fold it !
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Steno QTL Study (SQS) of key quantitative traits
related to the Metabolic Syndrome

• Objectives
• To estimate the heritability of quantitative
  traits related to the Metabolic Syndrome
• To identify chromosomal loci linked to key
  quantitative traits in the Metabolic Syndrome

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SQS Recruitment of glucose tolerant offspring of
probands with the Metabolic Syndrome including
familial T2D

• Danish Caucasian probands with four or more
  glucose tolerant offspring. A total of 246
  subjects
• Diabetic probands were diagnosed after age 45
  years
• Typical family structure

Proband
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SQS Extensive characterization of quantitative
traits in glucose tolerant offspring of probands
with the Metabolic Syndrome

• Glucose tolerance
• 4 h post-OGTT glucose
• AUC glucose
• Fasting plasma glucose
• Kg (IVGTT)

• Insulin sensitivity
• Si (Bergman)
• HOMA-IR
• OGTT-derived Si

• Insulin secretion
• First phase during IVGTT
• Responses during 4 h OGTT
• Tolbutamide induced
• secretion

• Glucose effectiveness
• Sg

• Lipid metabolism
• s-triglycerides
• s-FFA

• Body- fat and composition
• Fat mass
• Waist circumference
• BMI

• Physical fitness
• VO2-max

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SQS Random genome mapping

• ABIs Linkage Mapping Set
• 340 microsatellite markers
• 10 cM spacing
• Average heterozygosity 0.8
• Variance component model

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SQS Familialty of plasma GIP during a 4 h OGTT
in glucose tolerant offspring of probands with
the Metabolic Syndrome
Plasma GIP (pM)
Heritability of plasma GIP
Time (minutes)
Time (minutes)
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SQS Some other heritability estimates
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SQS Obesity-related QTLs
Trait Chr Pos (cM)
LOD BMI 8 44 3.3 BMI 12
76 3.1 body fat 6 66 2.9
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Chromosome 8
Fasting plasma GIP
log BMI
Maximum LOD score
Maximum LOD score
3
3
2
2
1
1
0
0
0
50
0
50
0
100
150
100
150
Map position (cM)
Map position (cM)
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Genome-wide significance of QTLs
Simulated p-values are based on 10,000 resamples
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UK Warren 2 T2D genetics
LOD 2.55 _at_ 42cM Entropy (37) 9 of genome scan
replicates
Other linkage reports within the same region LOD
2.78 BMI (Artword et. al. 2002) p 0.023
fat (Saar et. al. 2003) LOD 1.60 T2D (Elbein
et. al. 1999) LOD 1.35 T2D (Busfield et. al.
2002) LOD 1.77 T2D (Lee et. al. 2000)
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Summary Linkage Studies

• Collect families
• large families
• affected sib-pairs (if possible collect additonal
  siblings and parents)
• Genome scan with informative markers
• microsattelite markers
• SNPs (chips)
• Linkage statistics
• Identification of disease gene(s)