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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 • • • • 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 • – – Studies of “resting state”, particularly in patient samples • – – Problem: uninterpretable results Solution: don’t do them Uncertain origin of many imaging measures • – – • 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 ** * * * 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 . 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 1 1 3 0 2 3 3 Non‐risk Risk AKT1 rs1130233 ANOVA: 3-way interaction F(1,106)=4.663, p=0.033 A K T 1 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 0 Risk NRG1 ErbB4 AKT1 Ref group NRG1/ ErbB4/ AKT1 genotype Nicodemus et al Arch Gen Psych (2010) Imaging genetics as a teenager: New adventures • • • • 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.”