human genetics and gene mapping of complex...
TRANSCRIPT
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Human Genetics and Gene Mapping of Complex
Traits
Advanced Genetics, Spring 2015
Human Genetics Series
Tuesday 4/07/15
Nancy L. Saccone, [email protected]
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What is different about Human Genetics (recall from Cristina Strong's lectures)
• Can study complex behaviors and cognition, neurgenetics
• Extensive sequence variation leads to common/complex
disease
1. Common disease, common variant hypothesis
2. Large # of small-effect variants
3. Large # of large-effect rare variants
4. Combo of genotypic, environmental, epigenetic interactions
• Imprinting – uniquely mammalian
• Trinucleotide repeat diseases – "anticipation“
Greg Gibson, Nature Rev Genet 2012
• Clinical impact
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Testing for interactions
• GxG (epistasis), GxE
• Question: Does the effect of 1st variable on the outcome differ for different values of the 2nd variable?
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GxE interaction example – not in humans
Cooper and Zubek, 1958
2 rat strains: "maze dull" and "maze bright" - selectively bred based on maze performance.
Then, two environments:
- normal lab rearing
- "enriched" environment (e.g. playthings, open space)
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Animal models
Cooper and Zubek, 1958: mean number of learning errors
50
100
150
200
maze dull
maze bright as presented in
Rowe, 2003
normal enriched
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Other possible outcomes
Another way to get a GxE interaction: cross-over
50
100
150
200
maze dull
maze bright
normal enriched
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Possible outcomes
Here's what we might see if there were main effects only (no GxE interaction)
50
100
150
200
maze dull
maze bright
normal enriched
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Possible outcomes
another possibility: if no GxE interaction AND no main effect of environment
50
100
150
200
maze dull
maze bright
normal enriched
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Testing for interactions
• Traditional approach is to use a “product term” in the regression model
• Example (case-control logistic regression analysis)
Let P = probability of being a case
Let x1 = SNP genotype (additive coding)
x2= sex (0=male, 1=female)
To test for interaction between x1 and x2,
i.e. does the SNP effect differ in men vs women:
Is there significant evidence that b3 is different from 0?
2211 xx + =P-1
Pln bb
)x(xxx + =P-1
Pln 2132211
bbb
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Example: x1 = SNP genotype, x2 = sex
• Full model with interaction:
• Odds Ratio for SNP when x2 = 0 (male)
• Odds Ratio for SNP when x2 = 1 (female)
2131122 ][
1
xxxxe
P
P bbb
1
1
21
21311
21
21311
00[...]
0[...]
0,0
[...]
0,1
[...]
00
11
)1/(
)1/( bb
bb
bb
ee
e
e
e
PP
PPOR
xx
xxx
xx
xxx
male
32
32
21
21311
21
21311
[...]
[...]
1,0
[...]
1,1
[...]
00
11
)1/(
)1/( bbbb
bb
bb
e
e
e
e
e
PP
PPOR
xx
xxx
xx
xxx
female
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Genomic Imprinting
• Occurs in mammals (insects, plants)
• Departure from classical Mendelian genetics:
– Maternally and paternally inherited alleles are distinguished
– Expression of allele depends on parent of origin • Can be tissue specific
• Called maternally imprinted if maternal allele is inactive
• Paternally imprinted if paternal allele is inactive
– Example of epigenetics (study of inherited factors that are independent of the DNA sequence itself)
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Dots indicate non-expressing carriers
From Weiss, Genetic variation and human disease, 1993 /
Hall, 1990a, ASHG
Imprinting can generate “non-Mendelian” patterns
Trait
manifests only
when allele is
inherited from
the mother:
Trait
manifests only
when allele is
inherited from
the father:
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Genomic Imprinting and Human Disease
• Prader-Willi Syndrome and Angelman Syndrome (see Joe Dougherty’s lecture)
• Beckwith-Wiedemann Syndrome
– IGF2 gene – normally is imprinted in humans, with the maternal allele silenced
– Improper activation of the maternal allele leads to BWS
– Symptoms: overgrowth, increased risk of cancer
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Genomic Imprinting and Human Disease
• Prader-Willi Syndrome and Angelman Syndrome
From Hartl, Essential Genetics 6th Ed.
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Executive Summary “Personalized medicine” refers to the tailoring of medical treatment to
the individual characteristics of each patient. It does not literally mean
the creation of drugs or medical devices that are unique to a patient, but
rather the ability to classify individuals into subpopulations that differ in
their susceptibility to a particular disease or their response to a specific
treatment. Preventive or therapeutic interventions can then be
concentrated on those who will benefit, sparing expense and side
effects for those who will not.
Priorities for Personalized Medicine Report of the
President’s Council of Advisors on Science and Technology
September 2008
Moving from GWAS to post-GWAS: Genetics, clinical impact and personalized medicine
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• 2011: Green and Guyer (NHGRI), Nature 2011:
“Base pairs to bedside” – not just “bench to bedside”
• 2015: President Obama announced $215 million
Precision Medicine Initiative
• Objectives:
• Cancer treatment
• Voluntary national research cohort (compare with UK research facilitated by nationalized healthcare)
• Privacy protection
• Modernizing regulatory landscape
• Public-private partnerships
Fact sheet at www.whitehouse.gov
Moving from GWAS to post-GWAS: Genetics, clinical impact and personalized medicine
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E D. Green and M.S. Guyer Nature 2011
Improving the
effectiveness
of healthcare:
e.g. genomics
to guide
personalized
medicine
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Pharmacologic Treatment
Costs/Challenges
Side effects
Adherence
Use restrictions
Cost
Benefits
Efficacy
Genomics can guide personalized medicine
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Smoking cessation pharmacotherapy as an
example
But first, some history….
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“The dramatic reduction in smoking [in the US] has been
one of the most important recent public health successes.”
“The potent effect of pervasive societal changes on
[smoking] behavior will far outweigh any possible benefits
of identification of risk genes for individual smokers”
2003
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“Genetics studies should be acknowledged
as currently unlikely to lead to improved lung
cancer prevention compared with the proven,
non–genetic-based strategies for smoking cessation”
2006
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Current smoking rates (CDC data)
• What we have is not enough: – Increased social pressure
– Policy changes
– Heightened awareness of health consequences
– Available pharmacotherapy (e.g. nicotine replacement, others)
Year
% of US adults who
were current smokers
2004-2006 21%
2007 20%
2008-2009 21%
2010-2011 19%
2012-2013 18%
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CHRNA5 predicts cessation success and response to medication
Pharmacogenetics and Genomics:
February 2013 - Volume 23 - Issue 2 - p 94–103
Nicotinic acetylcholine receptor variation and response to smoking cessation therapies
Bergen, Andrew W.a; Javitz, Harold S.a; Krasnow, Rutha; Nishita, Denisea; Michel, Marthaa; Conti, David V.b;
Liu, Jinghuab; Lee, Wonb; Edlund, Christopher K.b; Hall, Sharonc; Kwok, Pui-Yand; Benowitz, Neal L.e; Baker,
Timothy B.f; Tyndale, Rachel F.h; Lerman, Caryng; Swan, Gary E.a
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Study Design
University of Wisconsin TTURC
• N=1073, European Ancestry
• All subjects received intensive behavioral counseling
• Pharmacotherapy arms
(NRT, bupropion, combo)
and 1 placebo arm
• Cessation
Abstinence at 60 days
Time to relapse over 60 days
CHRNA5 haplotype
• rs16969968
Functional amino acid change
in CHRNA5
• rs680244
CHRNA5 mRNA levels in brain
and lung
• Haplotype of 2 variants
– H1 (GC, 20.8%) (high risk for
dependence)
– H2 (GT, 43.7%)
– H3 (AC, 35.5%), low risk for
dependence
Chen, Baker et al, Am J Psychiatry 2012
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CHRNA5 Predicts Cessation & Response to Medication
Abstinence
Chen, Baker et al, Am J Psychiatry 2012
Smokers with
CHRNA5
low risk
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
H1 H2 H3
Counseling
alone
Counseling +
Medication
Smokers with
CHRNA5
high risk
H1: LOW RISK H3: HIGH RISK H2
Placebo +
Counseling
Medication +
Counseling
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Number Needed to Treat (NNT) Varies by Genetic Status
Abstinence
NNT > 1000
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
H1 H2 H3
Counseling
alone
Counseling +
Medication
NNT = 4
H1: LOW RISK H3: HIGH RISK H2
NNT: Number of
patients to treat
for 1 to benefit
Chen, Baker et al, Am J Psychiatry 2012
Placebo +
Counseling
Medication +
Counseling
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In smokers with high genetic risk, NNT for
cessation medication compares very well to
current evidence-based clinical treatments
Disorder Treatment Number needed to treat
(NNT)
Duodenal Ulcer H Pylori Antibiotics 2
COPD Anticholinergic 16
Stroke Alteplase 4.5-21.4
Hypercholesterolemia Statins 63-256
Nicotine Dependence NRT 4 in CHRNA5 high risk gp
1000 in CHRNA5 low risk gp
UpToDate®, 2013
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Summary • Human gene mapping for complex diseases/traits:
– Linkage (e.g. Model-based or model-free analyses in families)
– Association (e.g. allele/genotype frequency differences unrelated cases-controls; linear and logistic regression; effect size)
– Genome-wide association studies
• Linkage disequilibrium (LD)
– Power when tested SNP is in LD with causal SNP
– Genetic imputation
• GxG and GxE interactions
– Visualizing interactions vs main effects
– Testing with a product term
• Imprinting
• Genomics and “personalized medicine” (e.g genotype-by treatment interactions)