Transcript Slide 1

Disease Genomics
What is genomics?
• Looking at the properties of the genome as a
whole
– “seeing the wood for the trees”; identifying
patterns by considering many data points at once.
– Examining large-scale properties requires a model
of what is expected just by chance, the null
hypothesis.
What is disease genomics?
• OED: A condition of the body, or of some part or
organ of the body, in which its functions are
disturbed or deranged;
• So disease genomics is about taking a whole-genome
view to genetic disorders so we can discover:
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The identification of the underlying genetic determinants
insights into the pathoetiology of the disease
How to select the appropriate treatment
How to prevent disease
Preventive Medicine
• Empower people to make the appropriate life-style choices
– 23andMe, Coriell Study
• Treat the cause of the disease rather than the symptoms
– E.g. peptic ulcers
• “All medicine may become pediatrics”
Paul Wise, Professor of Pediatrics, Stanford Medical School, 2008
• Effects of environment, accidents, aging, penetrance …
– Somatic change, understanding how the genome changes over a lifetime
– cancer
• Health care costs can be greatly reduced if
– Invest in preventive medicine
– Target the cause of disease rather than symptoms
23andMe
© 23andMe 2009
23andMe Spittoon
23andMe Research Reports
Human genetic variation
• Substitutions
ACTGACTGACTGACTGACTG
ACTGACTGGCTGACTGACTG
– Single Nucleotide Polymorphisms (SNPs)
• Base pair substitutions found in >1% of the population
• Insertions/deletions (INDELS)
ACTGACTGACTGACTGACTG
ACTGACTGACTGACTGACTGACTG
– Copy Number Variants (CNVs)
• Indels > 1Kb in size
Human genetic variation
• Variation can have an effect on function
– Non-synonymous substitutions can change the
amino acid encoded by a codon or give rise to
premature stop codons
– Indels can cause frame-shifts
– Mutations may affect splice sites or regulatory
sequence outside of genes or within introns
How much genetic variation does
an individual possess?
• Compared to the Human genome reference sequence, which is itself
constructed from 13 individuals
1000 Genomes project: A map of human genome variation from populationscale sequencing, Nature 467:1061–1073
Penetrance of genetic variants
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Highly penetrant Mendelian single gene diseases
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Reduced penetrance, some genes lead to a predisposition to a disease
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Many cancers (solid tumors) require somatic mutations that induce cell proliferation,
mutations that inhibit apoptosis, mutations that induce angiogenesis, and mutations
that cause metastasis
Cancers are also influenced by environment (smoking, carcinogens, exposure to UV)
Atherosclerosis (obesity, genetic and nutritional cholesterol)
Some complex diseases have multiple causes
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BRCA1 & BRCA2 genes can lead to a familial breast or ovarian cancer
Disease alleles lead to 80% overall lifetime chance of a cancer, but 20% of patients
with the rare defective genes show no cancers
Complex diseases requiring alleles in multiple genes
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Huntington’s Disease caused by excess CAG repeats in huntingtin’s protein gene
Autosomal dominant, 100% penetrant, invariably lethal
Genetic vs. spontaneous vs. environment vs. behavior
Some complex diseases can be caused by multiple pathways
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Type 2 Diabetes can be caused by reduced beta-cells in pancreas, reduced
production of insulin, reduced sensitivity to insulin (insulin resistance) as well as
environmental conditions (obesity, sedentary lifestyle, smoking etc.).
The search for disease-causing variants
Adapted from Nature 461, 747-753 (2009)
Inheritance models
Dominant vs additive inheritance
Trait value
100%
Dominant
50%
Additive
0%
0
1
Number trait alleles inherited
2
Inheritance models
Dominant vs additive inheritance
Disease
Trait value
100%
Healthy
Dominant
50%
Additive
0%
0
1
Number trait alleles inherited
2
Identifying the genetic causes of
highly penetrant disorders
• de novo mutations
• Mendelian disorders
de novo mutations
• Humans have an exceptionally high pergeneration mutation rate of between 7.6 ×
10−9 and 2.2 × 10−8 per bp per generation
• An average newborn is calculated to have
acquired 50 to 100 new mutations in their
genome
– -> 0.86 novel non-synonymous mutations
• The high-frequency of de novo mutations may
explain the high frequency of disorders that
cause reduced fecundity.
Look at the epidemiology of the disease for clues
Prevalence
Age onset
(%)
Mortality
Fertility
Heritability
Paternal age
effect
Autism
0.30
1
2.0
0.05
0.90
1.4
Anorexia nervosa
0.60
15
6.2
0.33
0.56
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Schizophrenia
0.70
22
2.6
0.40
0.81
1.4
Bipolar affective
disorder
1.25
25
2.0
0.65
0.85
1.2
Unipolar depression
10.22
32
1.8
0.90
0.37
1
Anxiety disorders
28.80
11
1.2
0.90
0.32
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The role of genetic variation in the causation of mental illness: an evolution-informed framework
Uher, R. Molecular Psychiatry (2009) Dec;14(12):1072-82, “
How do we identify the de novo
mutation responsible?
• Compared to the Human genome reference sequence, which is itself
constructed from 13 individuals
1000 Genomes project: A map of human genome variation from populationscale sequencing, Nature 467:1061–1073
Identifying a causative de novo mutation
Veltman and colleagues - Nat Genet. 2010 Dec;42(12):1109-12
(1) Sequence
genome
Patient with
idiopathic
disorder
(3) Exclude
known variants
seen in healthy
people
(2) Select only
coding mutations
~22,000 variants
(exome re-sequencing)
MSGTCASTTR
MSGTNASTTR
~5,640 coding variants
(4) Sequence
parents and
exclude their
private variants
~143 novel
coding variants
For 6/9 patients,
they were able to
identify a single
likely-causative
mutation
(5) Look at
affected gene
function and
mutational
impact
~5 de novo
novel coding
variants
Mendelian disease
• Definition: Diseases in which the phenotypes are largely
determined by the action, lack of action, of mutations at
individual loci.
• Rare 1% of all live born individuals
• 4 types of inheritance
: Autosomal dominant
: Autosomal recessive
: X linked dominant
: X linked recessive
Mendelian disease
Definitions
Locus: Location on the genome
SNP: “Single Nucleotide Polymorphism” a mutation found in >1% of the population,
that produces a single base pair change in the DNA sequence
alleles
A
A
A
C
C
A
G
A
A
A
C
A
T
T
alternate forms of a SNP
C
A
T
G
A
T
both alleles at a locus form a genotype
genotypes
haplotypes
A
A
C
A
T
A
C
G
A
T
the pattern of alleles on a chromosome
Genetic Association: Correlation between (alleles/genotype/haplotype) and a
phenotype of interest.
Single Nucleotide Polymorphisms
(SNPs)
Recombination
X/x: unobserved causative mutation
A/a: distant marker
B/b: linked marker
Gametophytes
(gameteproducing cells)
A
BX
a
b x
Recombination
Gametes
a
B X
A
b x
Linkage Disequilibrium & Allelic Association
Marker
1
2
3
D
n
LD
Markers close together on chromosomes are often transmitted
together, yielding a non-zero correlation between the alleles.
This is linkage disequilibrium
It is important for allelic association because it means we don’t
need to assess the exact aetiological variant, but we see trait-SNP
association with a neighbouring variant
SNPs can be used to track the segregation
of regions of DNA
Individual 1
Individual 2
Locus 1
Locus 2
ACGTGCTCGATCGATCCGC TAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTCGATT GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
Time + recombination
Individual 3
Individual 4
Individual 5
Individual 6
Individual 7
ACGTGCTCGATCGATCCGC TAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTCGATT GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTCGATTGATCCGC TAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTCGATC GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTAGATT GATCCGCTAACTCGAATCCTCAGAATCTAGCCATATCG
More time (+ recombination)
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Individual
ACGTGCTCGATCGATCCGC TAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTCGATC GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTCGATTGATCCGC TAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTCGATC GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTAGATT GATCCGCTAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTCGATCGATCCGC TAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTAGATT GATCCGCTAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTCGATTGATCCGC TAACTCGAATCCTCAGGATCTAGCCATATCG
ACGTGCTCGATC GATCCGCTAACTCGAATCCTCAGAATCTAGCCATATCG
ACGTGCTAGATT GATCCGCTAACTCGAATCCTCAGAATCTAGCCATATCG
SNPs can be used to associate regions of
DNA with a trait (disease)
Locus 1
Case
Control
C allele
0
5
T allele
3
2
Case
Control
A allele
2
3
G allele
1
4
Locus 2
Genetic Case Control Study
Controls
Cases
T/G
T/A
C/A
C/G
T/A
C/G
T/G
T/G
C/A
C/A
Allele T is ‘associated’ with disease
Measures of Association: The Odds Ratio
• Odds are related to probability: odds = p/(1-p)
– If probability of horse winning race is 50%, odds are
1/1
– If probability of horse winning race is 25%, odds are
1/3 for win or 3 to 1 against win
• If probability of exposed person getting disease
is 25%, odds = p/(1-p) = 25/75 = 1/3
• We can calculate an odds ratio = cross-product
ratio (“ad/bc”)
Odds ratio example: Association of a SNP with the
occurrence of Myocardial Infarction
Presence of Disease
Variant Allele
Absent
Present
813
3,061
Absent
794
3,667
1,507
6,728
Total
OR =
Present
Odds in Exposed
Odds in Unexposed
=
813 / 3,061
794 / 3,667
=
813 x 3,667
794 x 3,061
= 1.23
Family-based Linkage Analysis
Healthy
Disease
A/A
a/A
a/A
a/A
Where is
???
a/a
A/A
a/A
= non-viable so not observed
A/A
Family Based Tests of Association
Aa
AA
• Related individuals are from
the same family
• We assume we’re tracking
the same causative mutation
within the family
AA
• Testing for Transmission
Disequilibrium
Example
Log of the Odds (LOD) score used
to define disease locus
Problems
Aa
AA
• Difficult to gather large
enough families to get power
for testing
• Recombination events near
disease locus may be rare
• Resolution often 1-10Mb
AA
• Difficult to get parents for
late onset / psychiatric
conditions
Genome-wide Association Studies
(GWAS)
• Looking for the segregation of disease (case/control)
with particular genotypes across a whole population
• A lot of recombination within the population so you
can very finely map loci
• Based on the common-disease, common-variant
hypothesis
– Only makes sense for moderate effect sizes (odds ratio < 1.5)
GWAS
•
Technology makes it feasible
-- Affymetrix: 500K; 1M chip arrived 2007.
(Randomly distributed SNPs)
-- Illumina: 550K chip costs (gene-based)
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
Good for moderate effect sizes ( odds ratio < 1.5).
Particularly useful in finding genetic variations that contribute to common,
complex diseases.
Whole Genome Association
Scan Entire Genome
- 500,000s SNPs
Identify local regions
of interest, examine
genes, SNP density
regulatory regions, etc
Replicate the finding
*
*
**
*
Common disease common variant (CDCV) hypothesis
QQ-plots
Log QQ plot
Tests of association
Major allele
homozygote (0)
Heterozygote
(1)
Minor allele
homozygote (2)
Case
Control
• Treat genotype as factor with 3 levels, perform 2x3 goodnessof-fit test (Cochran-Armitage). Loses power if additive
assumption not true.
• Count alleles rather than individuals, perform 2x2 goodness-offit test. Out of favour because
• sensitive to deviation from HWE
• risk estimates not interpretable
• Logistic regression
• Easily incorporates inheritance model (additive, dominant, etc)
• Can be used to model multiple loci
Genome-Wide Scan for Type 2 Diabetes in a
Scandinavian Cohort
http://www.broad.mit.edu/diabetes/scandinavs/type2.html
HapMap
• Rationale: there are ~10 million common SNPs in
human genome
– We can’t afford to genotype them all in each association
study
– But maybe we can genotype them once to catalogue the
redundancies and use a smaller set of ‘tag’ SNPs in each
association study
• Samples
– Four populations, 270 indivs total
• Genotyping
– 5 kb initial density across genome (600K SNPs)
– Second phase to ~ 1 kb across genome (4 million)
– All data in public domain
Haplotypes
Nature Genetics 37, 915 - 916 (2005)
Published Genome-Wide Associations through 12/2009,
658 published GWA at p<5x10-8
NHGRI GWA Catalog
www.genome.gov/GWAStudies
Population Stratification can be a problem
• Imagine a sample of individuals drawn from a population consisting of two
distinct subgroups which differ in allele frequency.
• If the prevalence of disease is greater in one sub-population, then this
group will be over-represented amongst the cases.
• Any marker which is also of higher frequency in that subgroup will appear
to be associated with the disease
Traditional Issues Persist
Allelic heterogeneity
– When multiple disease variants exist at the same gene, a single marker may
not capture them well enough.
– Haplotype-based association analysis is good theoretically, but it hasn’t shown
its advantage in practice.
Locus heterogeneity
– Multiple genes may influence the disease risk independently. As a result, for
any single gene, a fraction of the cases may be no different from the controls.
Effect modification (a.k.a. interaction) between two genes may exist with
weak/no marginal effects.
– It is unknown how often this happens in reality. But when this happens,
analyses that only look at marginal effects won’t be useful.
– It often requires larger sample size to have reasonable power to detect
interaction effects than the sample size needed to detect marginal effects.
Localization
• Linkage analysis yields broad chromosome
regions harbouring many genes
– Resolution comes from recombination events
(meioses) in families assessed
– ‘Good’ in terms of needing few markers, ‘poor’ in
terms of finding specific variants involved
• Association analysis yields fine-scale resolution of
genetic variants
– Resolution comes from ancestral recombination events
– ‘Good’ in terms of finding specific variants, ‘poor’ in
terms of needing many markers
Linkage vs Association
Linkage
Association
1.
Family-based
1.
Families or unrelateds
2.
Matching/ethnicity generally
unimportant
Few markers for genome
coverage (300-400
microsatellites)
Can be weak design
2.
Matching/ethnicity crucial
3.
Many markers req for genome
coverage (105 – 106 SNPs)
4.
Powerful design
Good for initial detection; poor
for fine-mapping
Powerful for rare variants
5.
Ok for initial detection; good
for fine-mapping
Powerful for common variants;
rare variants generally
impossible
3.
4.
5.
6.
6.