Biology and genetics of substance abuse

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Biology and genetics of
substance abuse
Tomas Drgon PhD
NIDA/NIH
Baltimore MD
Decreased brain function in amphetamine abusers
(DAT imaging)
Volkow et al (2001) Am. J. Psychiatry 158:377-382
Annual cost to society
Addictions
Alzheimer + dementias
Pain (w migraine)
Head and spinal cord injury
Anxiety disorders
Schizophrenia
Depressive illnesses
Developmental disorders
Stroke
Parkinson disease
Multiple sclerosis
Seizures
Huntington disease
Overall
Uhl and Grow (2004) Arch Gen Psychiatry 61:223-229.
Heritability
0.4
0.53
0.4
0.05
0.3
0.7
0.4
0.33
0.1
0.1
0.4
0.6
1
Billion $
544.11
170.86
150.8
94.41
82.63
57.08
53.14
35.68
27.03
15.96
7.62
1.04
0.23
1240.59
Heritability
• Family studies
• Twin studies
Heritability
• Family studies
• Twin studies
Alcohol Dependence
Dick et al 2006 Ann Clin Psychiatry 18: 223–231.
Agrawal 2006 Addiction 101, 801–812
Cannabis
Author
Year
Population
a2(95% CI)
c2(95% CI)
e2(95% CI)
1998
Adult female
0.72 (0.56–0.84)
–
0.28 (0.16–0.44)
2000
Adult male
0.76 (0.68–0.83)
–
0.24 (0.17–0.32)
1998
Adult males
0.33
0.29
1998
Adult female
0.62 (0.43–0.77)
–
0.38 (0.23–0.57)
2000
Adult male
0.58 (0.35–0.75)
–
0.42 (0.26–0.65)
1998
Adult female
0.58
0.03
0.39
Adult male
0.78
0.04
0.17
Adult female
–
0.56 (0.44–0.67)
0.44 (0.33–0.56)
Adult male
0.45 (0.17–0.66)
0.23 (0.17–0.44)
0.32 (0.22–0.46)
Adolescent
0.34 (0.00–0.67)
0.36 (0.10–060)
0.30 (0.16–0.48)
Cannabis abuse
Kendler
Tsuang
0.38
Cannabis
dependence
Kendler
Van den Bree
Lynskey
Rhee
2002
2003
Cocaine
Agrawal et al (2004) American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 129B:125–128
Nicotine/alcohol
Swan et al (1996) Journal of Substance Abuse 8:19-31
Polysubstance dependence
Kendler et al (2007) Arch Gen Psychiatry 64:1313-20
Polysubstance dependence
Kendler et al (2006) Psychol Med. 36: 955–962.
Brains of addicts are different
from brains of non-addicts
But where they the same to start
with??
Thompson PM (2001) Nature Neuroscience 4:1253-1258
Thompson PM (2001) Nature Neuroscience 4:1253-1258
Substance dependent individuals display less prefrontal and
temporal grey matter than controls
(Liu et al1998; Franklin et al 2002)
prefrontal
Brains of addicts are different
from brains of non-addicts
But where they the same to start
with??
Not necessarily….
Genetic architecture
• From intake and metabolism to brain and
reward, example – flushing syndrome
• From oligo-genic to polygenic, example –
flushing syndrome
25-50 million deaths in Europe
30%-60% of European population
Substances and targets
•
•
•
•
•
•
Cocaine – DAT
Marijuana – cannabinoid receptors
Amphetamine – VMAT2 (?)
Caffeine – adenosine receptors
Nicotine – acetylcholine receptors
Alcohol - GABRA4(??)
Current working model of genetic architecture for
substance dependence in the population
Environment e2
Genetic a2
Working ideas:
Substantial overall genetic influences.
Polygenic genetic architecture with small effects at each locus.
Additive models provide first approximations.
Analysis
• Assumptions
• Method (Affy chips)
– Pre-planned analysis
– Nominal statistics
– Genomic Clustering
– Convergence
• Systems biology approach/Functional
enrichment
Analysis
• Assumptions
– There are variants that make individuals
vulnerable to substance abuse regardless of
• Substance (alcohol, polysubstance, amphetamine,
nicotine)
• population (Caucasian-American, AfricanAmerican, Japanese, Taiwanese)
– These variants are common
– The effects of these variants are additive
Analysis
• In other words: the variants coming from
this screen will NOT be
– Drug specific
– Population specific
– Rare
– Interactive
Samples
• Samples in this analysis:
–
–
–
–
EA and AA polysubstance abusers (n=1600/5000)
COGA alcoholics (n=280)
Taiwan amphetamine abusers (n=380)
Japan amphetamine abusers (n=200)
• Related samples:
– Duke University Smoking cessation (n=400)
– U Penn Smoking dependence and cessation (n=200)
– U Rhode Island Smoking cessation (n=200)
500K chip
500 000 SNPs
1M chip
1 000 000 SNPs
1 000 000 CNVs
Each spot
represents a
hybridization
intensity of a SNP or
a CNV probe.
These can be used
in binary mode to
identify presence or
absence of an allele
in an individual, or in
a quantitative mode
to assay allele/CNV
frequency in a pool
of individuals.
Principal Component Analysis
• No anticipation of structure in the data
• No hypothesis
• Separates overall variance in data to
variance in certain directions
Nominal t statistics
• Only used to rank data
• Expected effect sizes small = not expected to
survive Bonferronni correction
• Focus on type II error, not type I error.
• Actual statistical significance will be tested
empirically (Monte Carlo)
Bayes theorem.
Bayes theorem.
Bayes theorem.
Bayes theorem.
Genomic clustering of positive SNPs
• Assuming there is LD, true positives
should cluster together in the genome
• SNP dense areas are more likely to have
clusters of positive SNPs
• Significance of clustering tested
empirically (Monte Carlo)
www.hapmap.org
www.hapmap.org
Convergence of positive SNPs
between independent samples
• Assuming all the detected differences are
noise, the probability of a SNP being
positive in two independent subpopulations is low (can be calculated)
• Significance of convergence tested
empirically (Monte Carlo)
Histogram of nominal t-values
Histogram of nominal t-values
Histogram of nominal t-values
Emperor Joseph II: My dear
young man, don't take it too
hard. Your work is ingenious. It's
quality work. And there are
simply too many notes, that's
all. Just cut a few and it will be
perfect.
Mozart: Which few did you have
in mind, Majesty?
Decreased brain function in amphetamine abusers
(DAT imaging)
Volkow et al (2001) Am. J. Psychiatry 158:377-382
Brains of addicts are different
from brains of non-addicts
But where they the same to start
with??
Connectivity constellation
• Constellation of neuronal connections
(Uhl 2008)
– Physical
– Quantitative (# of synapses)
– Qualitative (quality of synapses)
– Interactive (connectedness of circuits, plasticity)
Positions of stars are driven by
a set of physical laws and by
effects of other stars.
Position/phenotypes of
synapses in the brain are driven
by a set of rules and effects of
surrounding cells.
Cell adhesion molecules
mediate the forces that shape
the brain, the connections and
ultimately the phenotypes (in
development AND throughout
adulthood)
While the availability of the
entire human genome sequence
and high throughput methods for
analysis of the human genetic
variants allow us to collect and
analyze large amounts of
genetic data, the field of
psychiatric genetics is still in the
stage of “mythology”.
We can now see the stars, we
see that there are certain
relationships between them and
we can measure their intensity,
mass, distance from other stars
etc.
Our analytical approaches need
a paradigm shift.
Conclusions
• There are VERY FEW large single gene contributions to
substance abuse vulnerability
• We have identified a set of markers that explains substantial
portion of the genetic vulnerability to substance abuse
• Large portion of these markers appear to tag genes involved in
cell-to-cell adhesion, a phenomenon crucial for brain
development and synaptic plasticity
• Brains of individuals vulnerable to substance abuse and other
psychiatric disorders are sub-anatomically different from those
of healthy individuals. Cell adhesion molecules may mediate
this difference.
Concepts, design, funding
George R Uhl
CY Li
Cathy Johnson
Tomas Drgon
Donna Walther
Tim Liu
analysis, bioinformatics
Wet lab
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