Transcript Bernard Keavney

Recent developments in genetic
epidemiology relevant to PURE
Bernard Keavney
Institute of Human Genetics
University of Newcastle, UK.
Objectives
• Brief revision of some genetic “basics”
• Developments 2003-2005 in genetic
markers and genotyping technology
• Ethnicity, genetic variation and disease
• The potential impact of rare variants on
common diseases: epidemiological and
technological challenges.
Genetic contribution to
cardiovascular diseases
genes
Monogenic
(disease genes)
oligogenic
(large-effect
susceptibility
genes)
polygenic
(small-effect
susceptibility
genes)
environment
HCM, LQTS
Non-genetic
Congenital HD
Hypertension
T II DM
Atherosclerosis
Common variants which affect
human diseases
•
•
•
•
•
•
•
•
•
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HLA:
APOE4:
FV Leiden:
PPARG:
KCJN11:
PTPN22:
Insulin:
NOD2:
CF-H:
RET:
Autoimmunity and infection
Alzheimer’s, CHD, lipids
Venous thrombosis
Type II Diabetes
Type II Diabetes
RhA, Type 1 Diabetes
Type I Diabetes
Crohn’s disease
Age-related MD
Hirschprung disease
Candidate gene association studies: a
uniquely non-replicable area of science
• Six of 166 replicated in >75% of
studies (4%)*
• Study sizes too small
• Statistical significance levels not
stringent enough
• Meta-analyses: problem of
publication bias
• Most conducted in urban Western
Caucasian populations
• Minimal environmental heterogeneity
within individual studies
• Minimal amount of “gene space”
tested
*Hirschhorn et al. Genet. Med. 2002
Genome figures
• The human genome: 3,200,000,000 base pairs
• 5% gene coding regions (1% expressed sequence)
• Noncoding regulatory elements are situated near genes
• 20,000 genes
• Any two genomes: 99.9% identical
• 3.2M differences between any two individuals
• 11,000,000 sites vary in at least 1% of the
world’s population (Polymorphisms)
• Every site compatible with life has been mutated
several times in this generation alone
Single nucleotide polymorphisms (SNPs):
the mapping tool for association studies
CAACTGTGTAGGTTGAG
Coding (amino acid change)
Minority
CAACTGTGTTGGTTGAG
Noncoding
Some regulatory
Between 2000 and 2005 10 million SNPs have been identified.
For mapping, focus hitherto on common SNPs (MAF > 0.05):
ancient
power to detect given effect greater
90% of human variation is due to common alleles
Most common variants are found in all world populations
Technology to find rare variants has not been available thus far
Expect one common SNP every ~600 bp
Total of 7M genomewide……Which ones to type? And how many?
SNPs in dbSNP 2000-2005
The degree of association between a
disease allele and a marker allele
determines power
Disease
Testing two associations in one.
Causal SNP
Locus 1
D
H
D
H
Locus 2
A
B
B
A
Marker SNP
The arrangement of two or more alleles on a chromosome is
called a haplotype
The degree of association between a
disease allele and a marker allele
determines power
Disease
Testing two associations in one.
Causal SNP
Locus 1
D
H
D
H
Locus 2
A
B
B
A
Marker SNP
The arrangement of two or more alleles on a chromosome is
called a haplotype
Chromosomes are mosaics
reflecting ancestral haplotypes
MD
after n generations
D
MD
D
MD
M
MD
MD
D
ACE gene diagram
Position of 10 polymorphisms typed at the ACE locus
210 haplotypes could be generated from these genotypes
.
T A T A T C G I A 3
C C C T C C G D G 2
T A T A T C A I A 3
C C C T C C A D G 2
T A T A T T G I A 3
Clade A
X
T A T A T
C A D G 2
T A C A T
C A D G 2
Clade C
Clade B
Keavney et al 1998
Oct 2005: Characterisation of
most of the common genetic
variation present genomewide in
four world populations
HapMap project
• Phase I: 1 common SNP (MAF>0.05) every 5 Kb in
269 DNA samples (1 million SNPs)
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•
•
•
Yoruba from Ibadan, Nigeria
European ancestry from Utah, US
Han Chinese from Beijing
Japanese from Tokyo
• 10 x 500Kb regions
• Resequenced in 48 individuals
• All SNPs genotyped in 269 samples
• Phase II : 4 million common SNPs
• Goal: to assess feasibility of whole-genome
association studies and provide the “road map”of
SNPs to type
HapMap phase I data
Recombination rates, haplotype lengths and gene location
Chromosome 9q13
The POMC gene
Exon 1
(85bp)
Intron 1
(3709bp)
Exon 2
(151bp)
Intron 2
(2887bp)
Exon 3
(833bp)
5’
RsaI
C1032G
C8246T
There are no common polymorphisms in the translated sequence
Baker et al Diabetes 2005
0.5
Adjusted standardised WHR
0.4
0.3
0.2
0.1
0.0
-0.1
P<0.0001
Means (95% CIs)
-0.2
C/C
C/T
POMC C8246T genotype
T/T
WHR adjusted for age, sex, smoking, alcohol, exercise, with or without BMI
Difference 0.2 SD per allele. P=0.003 for C1032G; p=NS for RsaI
N=1426
Baker et al. Diabetes 2005
Genome-wide association studies
are feasible: HapMap data
Chip-based genotyping
provides the possibility to
type 500,000 SNPs in a
single individual today.
Chip-based WGA study
using 116,204 SNPs
identified the role of
Factor H in AMD (Klein
et al. April 2005)
The within-population component of genetic variation accounts
for most of human genetic diversity
1052 individuals from 52 populations; 377 autosomal microsatellites
47% of 4199 alleles present in all regions
7% alleles region-specific; median q=0.01
Rosenberg et al. Science 2003
Few SNPs rare in one panel are common in another
HapMap 2005
Heterogeneity of allele frequencies and disease O.R.s in
meta-analyses of 43 gene-disease associations
I2=75% shown by red line
Ioannidis et al. Nat Genet. 2004
Disease-causing variants: common
or rare alleles?
With a few exceptions (e.g. ACE I/D and plasma ACE) this is empirically confirmed
Leptin gene polymorphisms and
cardiovascular risk
20Kb shown
All common haplotypes at LEP are captured by these markers
C538T is a rare allele (q<0.01)
Gaukrodger et al. 2005
LEP C538T polymorphism, arterial
stiffness and carotid IMT
Trait
Pulse pressure
Mean IMT
Estimate (SE)
95% CI
Displacement*
1.00 (0.31)
0.39 – 1.61
Polygenic h2$
0.24 (0.06)
0.12 – 0.36
Displacement
0.90 (0.36)
0.19 – 1.61
Polygenic h2
0.20 (0.07)
0.06 – 0.34
Residual
correlation
0.13 (0.04)
0.04 – 0.21
Gaukrodger et al. JMG 2005
Rare alleles with large effect contribute to HDL
cholesterol variation in the “normal range”
Sequenced
Coding
Region
APOA1
ABCA1
LCAT
128 High HDLC
(>95%)
128 Low HDLC
(<5%)
Low
HDLC
High
HDLC
Var +
21
3
Var -
107
125
Cohen et al. Science 2004
• Variants affected function
• Replicated in 2nd population
• No association between HDLC and common
variants in these genes
• 1/6 of those with HDLC <5% had a mutation
• These would be missed by a “common
variant only” strategy
High-throughput
sequencing
technologies from
September 2005 issues
of “Science” and
“Nature”
Conclusions
• Technological progress is very rapid: prospect of
WGA scans on large numbers of samples in
near future
• Many studies (eg UK Biobank) focus on geneenvironment interaction but often environmental
heterogeneity is minimal
• There remains a pressing need to describe and
validate genetic associations with CVD in
populations other than US and Western
European Caucasians