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Imaging Genetics Becomes a Teenager: Is there trouble ahead?
Daniel R. Weinberger, M.D.
Genes, Cognition and Psychosis Program
National Institute of Mental Health, NIH
Bethesda, Maryland 20892 USA
[email protected]
Imaging Genetics: The Principle
cognition
The Wisconsin Card Sorting Task
behavior
temperament
Genes:
Cells:
Systems:
Behavior:
multiple alleles each of small effect
subtle molecular bottlenecks
Variable development/
information processing
complex functional interactions and emergent phenomena
Some milestones in imaging genetics
Bigos and Weinberger NeuroImage 2010
Imaging genetics as a budding teenager:
New adventures
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The GWAS bandwagon
Exploration of new phenotypes
Exploration of complex genetic mechanisms
Imaging pharmacogenetics
“Adolescence is a crazy time”
(Peter Blos, in On Adolescence)
Some pet peeves:
Studies of patients and interactions with diagnosis in search of epistasis
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Studies of “resting state”, particularly in patient samples
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Problem: uninterpretable results
Solution: don’t do them
Uncertain origin of many imaging measures
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Problem: findings may not be related to illness or risk of illness
Solution: study siblings lacking illness‐related state variables
Problem: genes are not about numbers in voxels
Solution: think thrice about searching imaging space qua imaging space
GWAS
Imaging genetics, imaging phenotypes and gene x dx
interactions : Phenomenology of confounders
MRI images are affected by:
• Body weight
• Lipid levels
• Fluid status
• Endogeneous steroid levels
• Alcohol consumption
• Cannabis use
• Stimulants
• Medications
• Activity (mental and physical)
• Mood
• Smoking
• Menstrual cycle
Neuroimaging GWAS: What do we really want to know?
• What variant in the human genome affects imaging measures of voxel “i” in an imaging dataset? Or,
• What is the genetic architecture of processing information “b” in region/circuit “qrs”? Effect of genome‐wide significant ZNF804A risk associated genotype on cortical dynamics
Esslinger et al Science 2009
Is this a neural system mechanism of the association with schizophrenia? Genetic association and brain function: Some caveats
• Most genes are likely to impact on brain function
• Genetic association with brain function and neural mechanisms of clinical risk are not necessarily linked.
• This linkage requires demonstration that the neural association is with a
heritable, susceptibility‐related phenotype (i.e. an intermediate phenotype). Abnormal regulation of amygdala response is not associated with increased genetic risk for schizophrenia
amyg‐cing coupling measure
Rasetti et al AJP 2008
Prefrontal‐hippocampal coupling dynamics during working memory: Evidence for a schizophrenia associated intermediate phenotype
p=.05,FWE
p<.01
N’s= 78 probands
171 unaffected siblings
151 controls
Seeded coupling analysis
p=.05
p<.05,FWE
Psychophysiologic interaction analysis
Rasetti et al (under review)
ZNF804A risk associated SNP modulates prefrontal‐
hippocampal coupling during executive cognition “Psychophysiologic
interaction analysis”
p=0.048 FWE‐corrected
“seeded connectivity
analysis”
p=0.004 uncorrected
Rasetti et al (under review)
Genetic network implicated in mediating variation in starvation stress in flies
Ayroles et al Nat Gen 2009
The Random Forest MLA uses recursive partitioning to build classification trees
Top “variable importance” SNPs identified by machine learning algorithms in DISC1 interactome
Abnormal prefrontal “efficiency“ : A schizophrenia intermediate phenotype
The “N Back” working
memory task
fMRI
Patients > Controls
(N=13) (N=18)
Healthy Siblings > Controls
(N=48)
(N=33)
Callicott et al. Cereb Cortex 2000
Callicott et al. Am J Psychiatry 2003
Biological validation of genetic epistasis between DISC1 and CIT and NDEL1 and CIT
F(1,213) = 5.3 p<0.05
F(1,233) = 4.4 p\0.05
F(1,233) = 4.4, p <0.05
Nicodemus et al Hum Genetics 2010
ErbB4 signaling is a schizophrenia risk pathway
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Epistatic interactions in NRG1‐ERBB4‐AKT1
ERBB4
AKT1
NRG1
NRG1 x erbB4 x AKT1 risk genotypes at all three loci:
OR 27.13 (CI 3.3-233), p<.002.
Nicodemus et al Arch Gen Psychiatry (2010)
Modeling clinical epistasis in brain physiology: Combined risk genotypes in 5’ and 3’ NRG1 SNPs show maximum inefficiency
in prefrontal WM processing
rs4560751 and rs3802160 show biologic epistasis in prefrontal cortical physiology (N=87 , p<.025, FWE corrected)
Nicodemus et al Arch Gen Psychiatry (2010)
Epistatic effect of NRGI‐ERBB4 (rs1050329‐rs1026882) on DLPFC function
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c.
N=114 normal volunteers
F(1,110)=11.308, z=3.20, p<0.05 FWE
Nicodemus et al Arch Gen Psych (2010)
The path from here to there…
cognition
The Wisconsin Card Sorting Task
schizophrenia
temperament
NRG1
ErbB4
AKT1
Cells:
Systems:
synaptic
biology
exaggerated
inefficiency
Behavior:
Epistatic effect of NRG1‐ERBB4‐AKT1
on DLPFC function
T‐score
Non‐risk
n=114; p<0.005, uncorrected
for display
Risk
Nicodemus et al Arch Gen Psychiatry (2010)
r
s
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1
3
0
2
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3
Non‐risk
Risk
AKT1 rs1130233 ANOVA: 3-way interaction
F(1,106)=4.663, p=0.033
A
K
T
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Comparisons of genotype risk groups across three genes
(NRG1‐ERBB4‐AKT1)
T score
1.6
Parameter estimates in R-BA9
1.4
1.2
1
0.8
0.6
0.4
0.2
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Risk NRG1 ErbB4 AKT1
Ref group
NRG1/ ErbB4/ AKT1 genotype
Nicodemus et al Arch Gen Psych (2010)
Imaging genetics as a teenager:
New adventures
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The GWAS bandwagon
Exploration of new phenotypes
Exploration of genetic mechanisms
Imaging pharmacogenetics
Genotype effects should predict gene targeted drug effects in brain: e.g. COMT and tolcapone
Placebo > Tolcapone
RT. DLPFC
LT. DLPFC
SPM 99, p < 0.025 SVC
Apud et al Neuropsychopharm 2007
Tolcapone normalizes prefrontal interhemispheric
and prefrontal‐hippocampal coupling in patients with schizophrenia during working memory
N’s = 30 controls (HV) and 21 patients (PT)
*PFWE‐corr<0.044
Mattay et al ACNP 2010
Bipolar risk associated SNP in CACNA1C shows effects in hippocampus during declarative memory N=116 normal subjects
right hippocampus: puncorr=0.001,pFDR=0.052, left hippocampus: puncorr=0.003, pFDR=0.052. but also in DLPFC during
executive cognition
N=316, p=.01 FDR corrected
Extrapolated to N=10,000, p< 4.87e‐109
Bigos et al Arch Gen Psychiatry 2010
GCAP Investigators…
Neuroimaging
Clinical genetics
Molecular genetics
Joseph Callicott
Anand Mattay
Fabio Sambatero
Eugenia Radulescu
Stefano Marenco
Kristi Bigos
Hao-Yang Tan
Jose Apud
Lewellyn Bigelow
Kristin Nicodemus
Fengyu Zhang
Richard Straub
Bhaskar Kolachana
Krishna Vakkalanka
Amanda Law
Imaging genetics charges towards adolescence: Conclusions
• Imaging genetics is a proven and powerful strategy for mapping gene effects in brain.
• Imaging genetics is a promising strategy for characterizing the genetic architecture of brain related phenotypes.
• Imaging genetics has unique potential to identify novel drug effects related to genetic mechanisms of brain disorders. • imaging genetics has many opportunities for new directions and new analyses as it enters adolescence. Its future impact will depend on how well investigators avoid the temptations and opportunities for poor judgment that make adolescence a “crazy time.”