Genomics

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Genomics
The study of the functions and interactions of
all the genes in the genome, including their
interactions with environmental factors
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Genomics to biology

• Identify all the functional elements in the human
  genome (ENCODE)
• Elucidate the organization of genetic networks
  and protein pathways and establish how they
  contribute to cellular and organismal phenotypes
• Understand evolutionary variation across species
  and the mechanisms underlying it

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Genomics to health

• Identify genes and pathways with a role in health
  and disease, and determine how they interact with
  environmental factors
• Apply genomebased diagnostic methods for the
  prediction of susceptibility to disease, the
  prediction of drug response and the accurate
  molecular classification of disease
• Translate genomic information into therapeutic
  advances

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Genomics to society

• Promote the use of genomics to maximize benefits
  and minimize harms
• Define policy options, and their potential
  consequences for the use of genomic information
• Define ethical boundaries of applying genomics to
  reproductive testing, genetic enhancement and
  germline gene transfer

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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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Genetics of type 2 diabetes Major prediabetic
quantitative traits

• Prospective studies among Caucasians have shown
  that
• obesity
• low insulin secretion
• insulin resistance
• low birth weight
• are quantitative traits predicting increased risk
  of type 2 diabetes

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Familiality of IVGTT-derived traits
Trait Adjustment h2
Si BMI, age, gender 0.31
Sg BMI, age, gender 0.49
Acute insulin BMI, age, gender
0.83 Fasting s-insulin BMI, age,
gender 0.37
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Familiality of body composition, birth weight and
length, and VO2 -max
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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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Alternative splicing A single gene can produce
multiple related proteins or isoforms, by means
of alternative splicing
N Engl J Med 347 1512-1520 2002
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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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Maturity onset diabetesof the young (MODY)

• Monogenic form of diabetes
• Autosomal dominant mode of inheritance
• Diabetes appears at a low age
• Impaired insulin secretion is a major phenotypic
  trait
• Different subtypes due to mutations in different
  genes

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Clinical characteristics of different forms of
Maturity-Onset Diabetes of the Young (MODY)
MODY1 MODY2 MODY3 MODY4 MODY5 MODY6 HNF-4
a Glucokinase HNF-1a IPF-1 HNF-1b NEUROD Locus
20q 7p 12q 13q 17q 2q Fasting
hyperglycemia 0- 0- 0- ?
0- ? Postprandial hyperglycemia - 0-
- ? - ? Minimum age at diagnosis 5
years 0 year 5 years 1 family renal cysts 4
families Need for insulin therapy 30 2 30 ? ?
50 Late diabetic complications common rare com
mon ? ? ? Patophysiology beta-cell beta-cell bet
a-cell beta-cell beta-cell beta-cell Prevalence
of MODY 5 10-15 60-75 rare rare rare Non-diabe
tes related ? s-trig reduced ? renal pancreatic re
nal cysts, Features birth weight threshold, agene
si proteinurea sulf.urea in renal
failure sensitivity homozygotes
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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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Scepticism
We finished the DM genome map - now we cant
figure out how to fold it !
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Genome scan to gene
0.3 genome 10,000,000 bases 100 genes 30,000
common variants gt5,000 perigenic variants
10cM
lod
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Triangulation
Positional candidates
Large-scale genetic epidemiology
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The candidate gene approach
Gitte Andersen Steno Diabetes Center
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Progress 2004 Genetics of type 2 diabetes

• Monogenic (2)
• Syndromic (1)
• Overlap (5)
• Polygenic T2D (90)

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Progress 2004 Genetics of type 2 diabetes

• Monogenic (2)Maturity onset diabetes of the
  young (MODY) HNFs IPF1 GCK NeuroD1
• Syndromic (1)
• Maternally inherited diabetes and deafness
  (MIDD) - tRNA(Leu)UUR
• Severe Insulin Resistance Insulin Receptor
• Overlap (5) Latent autoimmune diabetes of the
  adult (LADA) HLA
• Polygenic T2D (90)

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Genetics of T2D Why is it so difficult?

• Multiple alleles are likely involved - each
  confering a subtle increase in risk (relative
  risk about 1.3) or a subtle protection
• Poor definition of cases and controls
• Far too small study samples, i.e. lack of power
• Poor or no knowledge about gene-gene and
  gene-environment interplays

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Current challenges

• Do large-scale gene to gene analyses
• Do large-scale gene to environment analyses
• Recruit mega-samples of diabetics and refine your
  difinitions of type 2 diabetes
• Complement your rational gene hunting strategy
  with genome-wide SNP association studies applying
  about 100.000 SNPs per individual

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Evidence for associations between 7 gene variants
and common polygenic forms of T2D among Caucasians
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The combined impact of the Kir6.2 Gly23Lys and
the PPAR-?2 Pro12Ala polymorphisms on the risk
of T2D The odds ratio for T2D increases with
1.14 per risk allele (test for trend P0.003)

• Odds ratios and corresponding 95 CI for T2D
  estimated from 1164 T2D patients and 4733
  glucose-tolerant subjects categorised into 5
  groups (0-4) according to the number of risk
  alleles
• The odds ratios for each group were estimated
  relatively to the group with two risk alleles by
  logistic regression with a model assuming equal
  additive effects of the risk alleles

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Gene-gene interaction analyses
Kir6.2
PPAR-?2
PGC-1?
LTA
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Current challenges

• Do large-scale gene to gene analyses
• Do large-scale gene to environment analyses
• Recruit mega-samples of diabetics and refine your
  difinitions of type 2 diabetes
• Complement your rational gene hunting strategy
  with genome-wide SNP association studies applying
  about 100.000 SNPs per individual

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Do you have statistical power in your
case-control study design?
500 cases vs. 500 controls
5000 cases vs. 5000 controls
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How do you qualify for..

• ..being a case?
• ..being a control person?

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Type 2 diabetes (T2D) do we really know what we
are talking about?

• T2D in centrally obese (waist/BMI)
• T2D in peripherally obese (BMI/waist)
• T2D in lean
• LADA
• Family-related T2D
• Early-onset T2D
• Screen detected T2D
• T2D recruited from a clinic

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T2D exclusion criteria

• diabetes secondary to known chronic pancreatitis
• haemochromatosis
• severe insulin resistance
• maturity-onset diabetes of the young (MODY)
• maternally inherited diabetes and deafness (MIDD)
• patients with a family history of first degree
  relatives with Type 1 diabetes
• patients with insulin requirement within the
  first year after diabetes diagnosis
• patients with a fasting serum C-peptide level
  150 pmol/liter at the time of recruitment

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Control subjects for case-control studies of the
genetics of T2D associated with centrally obesity
and the metabolic syndrome

• One or ideally more normal OGTTs
• No features of the metabolic syndrome
• Population-based recruitment from the same
  geographical region as cases
• Matched on gender and ethnicity
• Matched on age? Age above?

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• Collect gt 10.000 T2D cases (ideally many more -
  from the same ethnic group to have sufficient
  power
• Apply strict exclusion citeria
• Stratfify on familiality, type of obesity,
  presence of markers of autoimmunity, age of onset
  etc.
• Do the hard job to recruit matched
  population-based glucose tolerant control
  subjects

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Current challenges

• Do large-scale gene to gene analyses
• Do large-scale gene to environment analyses
• Recruit mega-samples of diabetics and refine your
  difinitions of type 2 diabetes
• Complement your rational gene hunting strategy
  with genome-wide SNP association studies applying
  about 100.000 SNPs per individual

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Genome-wide association studies in type 2 diabetes

• Public haplotype block maps are now available
• High-through-put and cheaper SNP genotyping has
  being developed
• It is now possible to do random genome
  association studies applying about 100.000
  haplotype block markers

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Calpain-10
MC4R
HNF-1a
IRS-1
IPF-1
PGC-1
InsVNTR
B2AR and B3AR
SUR1/Kir6.2
PPARg
GCK
InsVNTR
B3AR and PPARg
PTP-1B
Insulin action
Insulin secretion
Obesity
Birth weight
Gene-gene and gene-environment interactions
Environment
Subsets of polygenic T2D
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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