NAMIC Core 3.2NAMIC Core 3.2Steven Potkin - UCISteven Potkin - UCI

James Kennedy – U of TorontoJames Kennedy – U of Toronto

Opportunity & ChallengesOpportunity & Challenges Core 3.2 Goal: Understand brain function Core 3.2 Goal: Understand brain function

in the context of an individual’s unique in the context of an individual’s unique genetic backgroundgenetic background

It is assumed that the integration of the It is assumed that the integration of the multi-modal imaging with genetics will multi-modal imaging with genetics will provide new knowledge not otherwise provide new knowledge not otherwise obtainable: knowledge discoveryobtainable: knowledge discovery

Requires Core 1 and 2 integrative tools to Requires Core 1 and 2 integrative tools to meet the daunting challengesmeet the daunting challenges

Opportunity & ChallengesOpportunity & Challenges Schizophrenia as the DBPSchizophrenia as the DBP::

Heterogeneous symptoms and course; Heterogeneous symptoms and course; Heritable; Heritable; Subtle differences in structure and function; Subtle differences in structure and function;

Must involve brain circuitry Must involve brain circuitry Challenges: Behavior and performance, cause Challenges: Behavior and performance, cause

and effect, medication, structure and/or functionand effect, medication, structure and/or function Genetic background influences brain Genetic background influences brain

development, function, and structure in both development, function, and structure in both specific and non specific ways specific and non specific ways

A Collaborative Approach to ResearchA Collaborative Approach to Research

Sheitman BB, Lieberman JA. J Psychiatr Res. 1998(May-Aug);32(3-4):143-150Age (Years)

Good

Function

Poor15 20 30 40 50 60 70

Premorbid Progression StableRelapsingPr

odro

me

?Improving

• F

irst

Ep

isod

e

To understand the time course of the disease – why first episode patients become chronically ill

Statistical Parametric MapStatistical Parametric MapMai et al Human Atlas, 2001Mai et al Human Atlas, 2001

????????

????????

????????

COMT effects

Actual site of “anatomical” DLPFC in this subject

Average canonical “anatomical” DLPFC in the group

Non-COMT effects

“Physiological” DLPFCIn normal subject for one“DLPFC Task”

“Physiological” DLPFCIn sz subject for one“DLPFC Task”

17/V1

18d,19d-V2-3,V6

18d,19d-V2-3,V6

tectum

tectum

pulvinar

pulvinarmesopontine reticular formation

precuneus

Paracingulate/precuneusSMA

Implied circuitry- retinal/meso-tectal-pulvinar-prestriate-precuneus-SMA

Potentially an arousal related visual posterior attention/orienting pathway

Clozapine: The First Atypical AntipsychoticClozapine: The First Atypical Antipsychotic

EfficacyEfficacy– Reduction of positive and negative symptomsReduction of positive and negative symptoms– Improvements treatment refractory patientImprovements treatment refractory patient– Reduction of suicidality in SA & schizo. patientsReduction of suicidality in SA & schizo. patients

Side effectsSide effects low EPS,low EPS, TDTD risk of agranulocytosisrisk of agranulocytosis risk of respiratory/cardiac arrest & myopathyrisk of respiratory/cardiac arrest & myopathy moderate-to-high weight gainmoderate-to-high weight gain potential for seizurespotential for seizures

Receptor bindingReceptor binding– Lowest D2 affinityLowest D2 affinity– Highest D1 affinityHighest D1 affinity

1980s1980s

Potkin et al ,2003

Clozapine Challenges DogmaClozapine Challenges Dogma The EPS associated with The EPS associated with

conventional antipsychotics led to conventional antipsychotics led to the misconception that EPS were the misconception that EPS were required for an antipsychoticrequired for an antipsychotic

Clozapine’s lack of EPS established Clozapine’s lack of EPS established that EPS are not a necessary for a that EPS are not a necessary for a therapeutic responsetherapeutic response

AIMS Scores for DRD3 Msc I Polymorphism after AIMS Scores for DRD3 Msc I Polymorphism after Typical Neuroleptic TreatmentTypical Neuroleptic Treatment

02468

10121416

Ser/Ser Ser/Gly Gly/Gly

CorrectedCorrectedMean Mean AIMSAIMSscorescore

DRD3 GenotypeDRD3 GenotypeF[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297F[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297

n=34n=34 n=53n=53 n=25n=25

19

1,1 1,2 2,2

Basile et al 2000

FDG Metabolic Changes With Haloperidol FDG Metabolic Changes With Haloperidol By DBy D33 Alleles Alleles

Gly-Gly Other AllelesOther Alleles

UCI Brain Imaging Center

Negative Symptom SchizophreniaNegative Symptom Schizophrenia

Potkin et al A J Psychiatry 2002

Failure to activate Failure to activate frontal cxfrontal cx

Cerebellar attempt toCerebellar attempt to compensatecompensate

1 27kb

COMT-S START CODON

COMT-MB START CODONTRANSMEMBRANE SEGMENT STOP CODON

PROMOTER

5´

22q11.2222q11.23CHROMOSOME 22

NlaIIINlaIII NlaIIINlaIIINlaIII

5´-GATGACCCTGGTGATAGTGG5´-CTCATCACCATCGAGATCAA210 BP

PCR

…CATG…

..AGMKD...

…CGTG…

..AGVKD..

high-activity (3-4X)thermo-stable

Low Dopamine Available

low-activity (1X)thermo-labile

More Dopamine Available

G1947 A1947

COMT-MB/S:

Val158/108 Met158/108

SOURCE: NCBI, GEN-BANK, ACCESSION # Z26491

The COMT GeneThe COMT Gene

Dopamine terminals in striatum and in Dopamine terminals in striatum and in prefrontal cortex are not the sameprefrontal cortex are not the same

modified after: Sesack et al modified after: Sesack et al J. NeurosciJ. Neurosci 1998, 1998, Weinberger, ICOSR, 2003Weinberger, ICOSR, 2003

StriatumStriatum

Prefrontal cortexPrefrontal cortex

DADA

DA transporterDA transporterDA receptorDA receptor

COMTCOMT

NE transporterNE transporter

Genotype Effect (F=5.41, df= 2, 449); Genotype Effect (F=5.41, df= 2, 449); p<.004p<.004..

COMT Genotype Effects Executive FunctionCOMT Genotype Effects Executive Function

sibspatientscontrols

COMT Genotype

WC

STPe

rsev

erat

ive

Erro

rs (t

-sco

res)

30

35

40

45

50

55

60

v v v m m m

Egan et al Egan et al PNASPNAS 2001 2001

n = 218n = 181

n = 58

COMT Genotype and Cortical Efficiency During COMT Genotype and Cortical Efficiency During fMRI Working Memory TaskfMRI Working Memory Task

Val-val>val-met>met-met use more DLPFC to do same task, SPM 99, p<.005 Egan et al Egan et al PNASPNAS 2001 2001

Transdisciplinary Imaging Genetics Center Transdisciplinary Imaging Genetics Center Synergies With NAMICSynergies With NAMIC

Neuroimaging

-48-48 GG

Inheritedgenotype

-48-48 AA

3’3’5’5’--

3’3’5’5’--

DRD1

Clinical and cognitive measures

0.150.18

0.140.11

-0.14-0.10-0.06-0.020.020.060.100.140.180.220.260.30

ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBOTreatment Group

5 6 2 8 1

+

8

+

3

5 6 2 8 15 6 2 8 1

++

88

++

33

Combine neuroimagingWith behavioral and clinical measuresand genetics

To identify useable endophenotypes & targeted therapeuticsDNA

Proto-endophenotypesProto-endophenotypes Combinations of Combinations of

– Imaging measures (sMRI, FMRI, PET, EEG)Imaging measures (sMRI, FMRI, PET, EEG)– GenotypesGenotypes– Clinical profilesClinical profiles– Treatment responseTreatment response– Cognitive behaviorCognitive behavior

Iterative refinements to develop Iterative refinements to develop endophenotypesendophenotypes

Studies like these represent a wealth of Studies like these represent a wealth of potential information ---if they can be potential information ---if they can be combinedcombined

How many genes are needed for one disease ?How many genes are needed for one disease ?

In complex traits, genes act together and we must In complex traits, genes act together and we must understand “how” if we want to understand the biology of understand “how” if we want to understand the biology of disease: disease:

modelling gene^gene interactions – the Epistasis effectmodelling gene^gene interactions – the Epistasis effectGene A Gene B

+++

++++

+

+ +++++++

Strategies for Discovering Novel Candidate Strategies for Discovering Novel Candidate Genes & Drug Targets in SchizophreniaGenes & Drug Targets in Schizophrenia

Candidates FromMicroarray Studies in

AnimalsDrug Models

(e.g., PCP, amphetamine)Treatment Models (e.g, neuroleptics)

Knowledge of Pathophysiology of Neuronal CircuitsCandidates FromNeurotransmitter

SystemsPharmacology of

DiseaseCandidate

Genes

Candidates From

Microarray Screens(30,000 Genes)

Plus validation with

In situ hybridization

Microsatellite SurveysIdentifying “Hotspots” &

and Genes in ROI

Candidates FromReplicated Genome Wide

WE Bunney

Computer analysis

NeuroarrayWWW:

Analyze Image

Probabilities of medication

response and development of

side-effects

Efficacy Negative Cognitive DM Weight SuicideClozapine 90 80 25 50 85 2Asenapine 90 80 50 10 15 ?Olanzapine 80 70 20 70 90 4Ziprasidone 85 75 30 20 10 ?

Imaging Genetics ConferenceImaging Genetics Conference The First International Imaging Genetics The First International Imaging Genetics

Conference was held January 17 and 18, Conference was held January 17 and 18, 2005.2005.

To assess the state of the art in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics.

Legacy Dataset-UCI 28Legacy Dataset-UCI 28 fMRIfMRI PETPET Structural MRIStructural MRI Genetic - SNPGenetic - SNP Clinical measuresClinical measures Cognitive measuresCognitive measures EEG EEG

– 28 subjects, chronic Sz 28 subjects, chronic Sz

fMRI: Working MemoryfMRI: Working Memory Sternberg task: Sternberg task: Example ResultsExample Results

5 6 2 8 1

+

8

+

3

PET: Continuous Peformance TaskPET: Continuous Peformance Task

Continuous Continuous Performance Task Performance Task (CPT)(CPT)– Sustained attentionSustained attention– Selective attentionSelective attention– Motor control taskMotor control task

+ 0

+9

PET results: PET results: – Same as fMRI except no Same as fMRI except no

time course datatime course data

Structural MRIStructural MRI Cortical thickness measures in mmCortical thickness measures in mm By defined regionBy defined region

GeneticsGenetics

5HT2A (T102C)

DRD1(DdeI)

DRD2(BstNI) _141

DRD2(Taq1A)

DRD2_rs1799978

DRD2_rs1800498

DRD2_rs4648317

5058 2 2 1 1 2 2 1 2 1 1 1 1 1 1

5059 1 2 1 1 2 2 1 1 1 1 2 2 1 1

5061 1 2 1 1 2 2 1 2 1 1 1 2 1 1

5064 1 2 1 2 2 2 1 1 1 1 1 1 1 1

5024 2 2 1 1 1 2 1 2 1 1 1 2 1 2

5028 2 2 2 2 2 2 1 1 1 1 1 2 1 1

5030 1 2 2 2 2 2 1 1 1 1 1 2 2 2

5034 1 2 1 2 1 2 1 2 1 1 1 2  

5035 1 2 1 1 2 2 2 2 1 1 1 1 1 1

5037 1 2 1 2 2 2 1 1 1 1 1 2 1 1

Clinical ScoresClinical Scores PANSSPANSS

– Thirteen subscales/factorsThirteen subscales/factors– Positive, negative, and global summary Positive, negative, and global summary

scoresscores– Lindenmayer 5-factors summaryLindenmayer 5-factors summary– Marder 5-factors summaryMarder 5-factors summary

Cognitive ScoresCognitive ScoresImmediate Word List Recall Total (total words recalled across all 3 trials)

Delayed Word List Recall Total (total words recalled from the 15 presented, after ~25 min delay)

Delayed Word List Recognition Total (total words correctly identified, when presented visually with 35 distractor words after ~25 min delay)

Visual Recognition Correct (total correct hits; pt is shown 15 geometric shapes, then those are mixed with 15 similar, distractor, shapes, and pt says 'Yes, I saw that one', or 'No, I didn't see that one'.

Visual Recognition Correct (total false alarms; pt says 'yes', when he should've said 'no')

Visual Retention Ratio (calculated as Vrcor/Vrfa)

Letter Number Span (total correct; pt hears mixed up numbers and letters, which they must recite in order--numbers, small to large and then letters--alphabetically.)

Trails A (time to complete a task of connecting numbered circles in order)

Trails A Errors (incorrect numbers connected)

Trails B (time to complete a task of connecting alternating numbered and lettered circles in order)

Trails B Errors (incorrect numbers or letters connected)

Example Query of Federated DatabaseExample Query of Federated Database

PET & fMRI

How can you predict which prodromal subject will develop first episode schizophrenia ?

Integrated View

Receptor Density ERP

Web

PubMed, Expasy

Wrapper

WrapperWrapper

Wrapper

Structure

Wrapper

Clinical

Wrapper

Mediator

0.150.18

0.140.11

-0.14-0.10-0.06-0.020.020.060.100.140.180.220.260.30

ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBOTreatment Group

Anatomical AccuracyAnatomical Accuracy Operational Plan (Fallon led effort)Operational Plan (Fallon led effort)

– Step 1. Core 3-2 will develop operational criteria and Step 1. Core 3-2 will develop operational criteria and guidelines for differentiation of areas and subareas.guidelines for differentiation of areas and subareas.

– Step 2. Core 3-2 will develop 10 training sets in which areas Step 2. Core 3-2 will develop 10 training sets in which areas and subareas of BA 9 and 46 have been differentiated and subareas of BA 9 and 46 have been differentiated as a as a rule–based averaged functional anatomical unit rule–based averaged functional anatomical unit applied to individual subjects. applied to individual subjects.

Needs to be applied to UCI 28 by TannenbaumNeeds to be applied to UCI 28 by Tannenbaum Gliches in Freesurfer, Slicer must be overcome and Gliches in Freesurfer, Slicer must be overcome and

features added eg subcortical white matter features added eg subcortical white matter segmentation for tractographysegmentation for tractography

Extend to visualization (Falko Kuester)Extend to visualization (Falko Kuester) Supplement Slicer with multiple segmentation programs Supplement Slicer with multiple segmentation programs

in addition to Freesurferin addition to Freesurfer

Anatomical AccuracyAnatomical Accuracy Specified Operational PlanSpecified Operational Plan

– Step 3. Core 1 will develop algorithms Step 3. Core 1 will develop algorithms and methods for defining areas based and methods for defining areas based on the training dataset.on the training dataset.

– Step 4. Iterations of Steps 1 through 3 Step 4. Iterations of Steps 1 through 3 will perfect and validate the various will perfect and validate the various methods for defining areas.methods for defining areas.

– Step 5. The area identification methods Step 5. The area identification methods will be implemented by Core 3. will be implemented by Core 3.

Identified 80 ROIs Relevant to DBP Identified 80 ROIs Relevant to DBP of Schizophreniaof Schizophrenia

LEFT AMYGDALA.txt* RIGHT AMYGDALA.txt*LEFT ANGULAR GYRUS.txt* RIGHT ANGULAR GYRUS.txt*LEFT ANTERIOR CINGULATE.txt* RIGHT ANTERIOR CINGULATE.txt*LEFT ANTERIOR COMMISSURE.txt* RIGHT ANTERIOR COMMISSURE.txt*LEFT ANTERIOR NUCLEUS.txt* RIGHT ANTERIOR NUCLEUS.txt*LEFT BRODMANN AREA 10.txt* RIGHT BRODMANN AREA 10.txt*LEFT BRODMANN AREA 11.txt* RIGHT BRODMANN AREA 11.txt*LEFT BRODMANN AREA 13.txt* RIGHT BRODMANN AREA 13.txt*LEFT BRODMANN AREA 17.txt* RIGHT BRODMANN AREA 17.txt*LEFT BRODMANN AREA 18.txt* RIGHT BRODMANN AREA 18.txt*LEFT BRODMANN AREA 19.txt* RIGHT BRODMANN AREA 19.txt*LEFT BRODMANN AREA 1.txt* RIGHT BRODMANN AREA 1.txt*LEFT BRODMANN AREA 20.txt* RIGHT BRODMANN AREA 20.txt*LEFT BRODMANN AREA 21.txt* RIGHT BRODMANN AREA 21.txt*LEFT BRODMANN AREA 22.txt* RIGHT BRODMANN AREA 22.txt*LEFT BRODMANN AREA 23.txt* RIGHT BRODMANN AREA 23.txt*LEFT BRODMANN AREA 24.txt* RIGHT BRODMANN AREA 24.txt*LEFT BRODMANN AREA 25.txt* RIGHT BRODMANN AREA 25.txt*

Circuitry AnalysisCircuitry Analysis Specified Operational PlanSpecified Operational Plan

– Step 1. Core 3-2 will collaborate with Core 2 to Step 1. Core 3-2 will collaborate with Core 2 to implement algorithms for structural equation modeling, implement algorithms for structural equation modeling, and the canonical variate analysis. and the canonical variate analysis.

Fallon & Kilpatrick, piloted but as a first step need to better Fallon & Kilpatrick, piloted but as a first step need to better quantify and automate ROI based on literature, Knowledge quantify and automate ROI based on literature, Knowledge Based Learning as a general tool.Based Learning as a general tool.

– Step 2. Core 3-2 will use step 1 software to test Core 3-2 Step 2. Core 3-2 will use step 1 software to test Core 3-2 hypotheses.hypotheses.

– Step 3. Core 3-2 in collaboration with Core 2 will extend Step 3. Core 3-2 in collaboration with Core 2 will extend the canonical variate analysis methods of Step 1 to the canonical variate analysis methods of Step 1 to determine images that distinguish among tasks, clinical determine images that distinguish among tasks, clinical symptoms, and cognitive performance.symptoms, and cognitive performance.

– Step 4. Core 3-2 and Core 1 will collaborate to integrate Step 4. Core 3-2 and Core 1 will collaborate to integrate canonical variate analyses with machine learning canonical variate analyses with machine learning approaches for detecting circuitry.approaches for detecting circuitry.

Genetic Analysis in Combination Genetic Analysis in Combination with Imaging Datawith Imaging Data

Specified Operational PlanSpecified Operational Plan– Step 1. Core 3 will type multiple genetic Step 1. Core 3 will type multiple genetic

markers at selected genes relevant to markers at selected genes relevant to schizophrenia and brain structure.schizophrenia and brain structure.

– Step 2. Core 2 will extend Toronto “in-house” Step 2. Core 2 will extend Toronto “in-house” Phase v2.0 software for measuring two gene-Phase v2.0 software for measuring two gene-gene interactions to multiple genes and make gene interactions to multiple genes and make the software more user friendly to the software more user friendly to neuroscience and genetic researchers in neuroscience and genetic researchers in general.general.

– Step 3. Core 3-2 will determine linkage Step 3. Core 3-2 will determine linkage disequilibrium structure on the genetic data disequilibrium structure on the genetic data using specific programs such as Haploview, using specific programs such as Haploview, GOLD, and 2LD and construct haplotypes.GOLD, and 2LD and construct haplotypes.

Genetic Analysis in Combination Genetic Analysis in Combination with Imaging Datawith Imaging Data

Specified Operational Plan (cont.)Specified Operational Plan (cont.)– Step 4. Core 3-2 will complete genetic analyses Step 4. Core 3-2 will complete genetic analyses

on the haplotypes developed, identified by the on the haplotypes developed, identified by the Core 3-2 software in Step 3, and test for Core 3-2 software in Step 3, and test for gene-gene-gene interactiongene interaction using refinement of Toronto using refinement of Toronto Phase v2.0 software from Step 2.Phase v2.0 software from Step 2.

– Step 5. Core 3-2 will collaborate with Core 1 to Step 5. Core 3-2 will collaborate with Core 1 to develop methods for develop methods for combining genetic and combining genetic and imaging dataimaging data using machine learning using machine learning technologies and Bayesian hierarchical technologies and Bayesian hierarchical modeling.modeling.

– Step 6. Iterations of Step 5 will develop Step 6. Iterations of Step 5 will develop predictive models and suggest hypotheses.predictive models and suggest hypotheses.

James L Kennedy MD, FRCPCJames L Kennedy MD, FRCPC

I’Anson Professor of Psychiatry and Medical Science

Head, Neurogenetics Section, Clarke Division,

Director, Department of Neuroscience Research

Centre for Addiction and Mental Health (CAMH),

University of Toronto & SG Potkin, D Mueller, M Masellis,

N Potapova, F Macciardi

Genetics and Neuroimaging: Genetics and Neuroimaging: Current Findings and Future StrategiesCurrent Findings and Future Strategies

How do genes determine brain characteristics?How do genes determine brain characteristics?

Molecular Genetic Approach

Gene Variants

Pharmacology

Phenotype

Sub-pheno

Endophenotype

Neurobiology

Pharmacogenetics

Gene Expression

-Psychophysiology;

Neuroimaging

Cytoarchitectural abnormalities

Control

Schizophrenia

Comparison of hippocampal pyramids at the CA1 and CA2 interface between control and schizophrenic.

Cresyl violet stain, original magnification X250

Conrad et al. (1991) Arch Gen Psychiatry

Will the Brain Derived Neurotrophic Factor (BDNF) Gene Predict Grey Matter Volume?

Val-66-met(GT)n repeat (function? mRNA stability)

Exon 11

BDNF-1 SNP BDNF-2 BDNF-3 BDNF-4

BDNF val66met: MRI functional brain imaging (Egan et al, Cell 2003)

The red/yellow areas indicate brain regions (primarily hippocampus) that function differently between val/val (n=8) and val/met (n=5) subjects while performing a working memory task. Subjects with the met allele had more abnormal function.

Haplotype TDT: BDNF (GT)n repeat & val66met in schizophrenia

2

7

26

10

5 5 6

12

0

5

10

15

20

25

30

TransmissionsNon Trans

** HTDT for 170-val66

2 = 7.11; 1 df; p = 0.007

Muglia et al, (2002)

Figure 1d: Principal deformation for the right hippocampus for normal controls (top) and schizophrenia patients (bottom). Four views (front, lateral, back, medial) of each shape are shown. The color indicates the direction and the magnitude of the deformation, changing from blue (inwards) to green (no deformation) to red (outwards).

Hippocampal shape as a phenotype for genetic studies

Neuroanatomical Distributions of Neuroanatomical Distributions of Dopamine ReceptorsDopamine Receptors

(Seeman etal, 1995)

Dopamine D2 Receptor: 5 Genetic Markers Studied

5) TaqIA1 2 3 4 5 6 7 8

3) TaqIB

2) –141 Ins/Del

1) -241 A/G

4) C957T

Dopamine D2 Gene LD: Potkin new SCZ sample (N=28)

• Linkage Disequilibrium map (Haploview)

• 5 markers across the DRD2 gene

DRD2 Schiz Responder/Non-Resp. (chi2) Potkin N=48 SNP Genotype Res (Freq) No-Res (Freq) P-Value

-241 A/G 11 11 (0.79) 16 (0.80) 0.1511 = A 12 1 (0.07) 4 (0.20)2 = G 22 2 (0.14) 0 (0.00)

-141C Ins/Del 11 0 (0.00) 2 (0.10) 0.0501 = Del 12 2 (0.14) 9 (0.45)2 = Ins 22 12 (0.86) 9 (0.45)

TaqIB C/T 11 1 (0.07) 0 (0.00) 0.4751 = C 12 3 (0.21) 5 (0.25)2 = T 22 10 (0.72) 15 (0.75)

C957T C/T 11 6 (0.42) 7 (0.35) 0.8851 = C 12 4 (0.29) 6 (0.30)2 = T 22 4 (0.29) 7 (0.35)

TaqIA T/C 11 1 (0.07) 0 (0.00) 0.2901 = T 12 3 (0.21) 8 (0.40)2 = C 22 10 (0.72) 12 (0.60)

Del -> Non-Responder

DRD2 Quantitative Data: Total BPRS (ANCOVA) Potkin N-48 SNP Genotype (N) Mean (SD, 95%CI) P-Value

-241 A/G 11 (27) -5.33 (11.9, -10.0/-0.6) 0.3071 = A 12 (5) -4.20 (8.7, -15.0/6.6)2 = G 22 (2) -24.50 (6.4, -81.7/32.7)

-141C Ins/Del 11 (2) 4.50 (12, -103/112) 0.1281 = Del 12 (11) -0.73 (9.5, -7.1/5.6)2 = Ins 22 (21) -10.24 (11.9, -15.6/-4.8)

TaqIB C/T 11 (1) -20.00 --- 0.3781 = C 12 (8) -6.00 (14.7, -18.3/6.3)2 = T 22 (25) -5.84 (11.3, -10.5/-1.2)

C957T C/T 11 (13) -7.15 (13.3, -15.2/0.9) 0.8821 = C 12 (10) -6.00 (9.9, -13.1/1.1)2 = T 22 (11) -5.55 (13.2, -14.4/3.3)

TaqIA T/C 11 (1) -20.00 --- *0.0351 = T 12 (11) -1.18 (11.8, -9.1/6.7)2 = C 22 (22) -8.23 (11.6, -13.4/-3.1)

D2 TaqIA Genotypes vs. total BPRS response score(p = 0.035) Potkin N=48

22111N =

D2TAQ1A

22.0012.0011.00

BPR

S6M

OD

20

10

0

-10

-20

-30

-40

1,1 1,2 2,2

D2 TaqIA vs. Positive Symptoms (ANCOVA; p = 0.07) Potkin N=48

22111N =

D2TAQ1A

22.0012.0011.00

BP

OS

6MO

D20

10

0

-10

-20

4443

1,1 1,2 2,2

Migrating Window DRD2 Haplotype Analysis (COCAPhase) Potkin N=48

Window Global P-value

1-2-3 0.019

2-3-4 0.041

3-4-5 0.924

5) TaqIA1 2 3 4 5 6 7 8

3) TaqIB

2) –141 Ins/Del

1) -241 A/G 4) C957T

Individual D2 Haplotype Tests Within Window 1-2-3 (global p = 0.019; COCAPhase; Potkin N=48)

Haplotype Resp. (Freq.)

Non-Resp. (Freq.)

P-value

1-1-2 1 (0.04) 13 (0.33) *0.007

1-2-1 3 (0.11) 5 (0.13) 0.820

1-2-2 19 (0.67) 18 (0.45) 0.115

2-1-2 1 (0.03) 0 (0.00) 1.000

2-2-1 2 (0.07) 0 (0.00) 0.057

2-2-2 2 (0.08) 4 (0.10) 0.924

Mochida, 2000

SNAP-25 Gene Marker LDPotkin new sample N=28

The darker red color denotes stronger relationship (linkage) between any two markers .

Above the diagonal is D’ and below is correlation, r.

SNAP-25 Gene vs SchizophreniaPotkin N=28 Cases versus controls (chi-sq)

0 = control, 1 = schizophrenia * SNAP-25 DdelICrosstab

Count

278 167 22 46716 8 1 25

294 175 23 492

,001,00

0 = control, 1 =schizophrenia

Total

11,00 12,00 22,00SNAP-25 DdelI

Total

Chi-Square Tests

,199a 2 ,905,202 2 ,904

,060 1 ,806

492

Pearson Chi-SquareLikelihood RatioLinear-by-LinearAssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)

1 cells (16,7%) have expected count less than 5. Theminimum expected count is 1,17.

a.

Crosstab

Count

197 224 56 4778 15 2 25

205 239 58 502

,001,00

0 = control, 1 =schizophrenia

Total

11,00 12,00 22,00SNAP-25 MnlI

Total

Chi-Square Tests

1,639a 2 ,4411,649 2 ,438

,164 1 ,685

502

Pearson Chi-SquareLikelihood RatioLinear-by-LinearAssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)

1 cells (16,7%) have expected count less than 5. Theminimum expected count is 2,89.

a.

0 = control, 1 = schizophrenia * SNAP-25 MnlI

Gene-Gene Interactions in Schizophrenia:

First Steps

M Lanktree, J Grigull, D Mueller, P Muglia, FM Macciardi, JL Kennedy

BIOINFORMATICS APPLICATIONS Vol. 20 no. 0 2004, pages 1–2

PedSplit: pedigree management for stratified analysis

M. B. Lanktree1,., L. VanderBeek1, F. M. Macciardi1,2 andJ. L. Kennedy1

1Neurogenetics Section, Centre for Addiction and Mental Health, Department ofPsychiatry, University of Toronto, 250 College Street, Toronto M5T 1R8, Canadaand 2Department of Human Genetics, University of Milan, Italy

PEDSPLIT is a simple pedigee arrangement software that stratifies the sample conditioned on factors including the proband's sex and genotype status in order to assist investigations into gene-gene interaction, haplotype relative risk, and sexually dimorphic effects.

TDT Polymorphism TDT

T NT 2 p

BDNF(Eco) A2 118 72 11.137 0.000850 BDNF(GT) A3* 88 57 6.628 0.010058 DRD1(Bsp) A1 114 105 0.370 0.543026 DRD1(Ddel) A2 128 116 0.590 0.442428 DRD1(Hae) A2 110 97 0.816 0.366346 DRD4 A4* 100 79 2.464 0.116476 NMDA(Bfa) A2 32 15 6.149 0.013170 NMDA(Bse) A2 42 26 3.765 0.052350 NMDA(Msp) A1 117 100 1.332 0.248430

C-TDT Results D4 & D1

DRD4

Bsp T NT 2 p 1 1 27 33 0.60 0.439 1 2 56 38 3.45 0.063 2 2 11 8 0.47 0.491

not 1 1 67 46 3.90 0.048 Global 7.72 0.100

DRD4 Dde1 T NT 2 p

1 1 8 10 0.22 0.637 1 2 55 36 3.97 0.046 2 2 33 31 0.06 0.803

not 2 2 63 46 2.65 0.103 Global 4.99 0.288

Will MOG gene variants predict white matter abnormalities?

Hypothesized Autoimmune Mechanism in Schizophrenia

B-LymphocyteAntibodies

Inflammation

Mast Cell

Chemokines

Illustration taken from www.phototakeusa.com.

Autoantibodies cross-react with neuronal proteins (eg myelin?) during fetal brain development, causing subtle damage to the CNS, leading to SCZ in early adulthood (Swedo, 1994).

TDT: MOG-(TAAA)n in SCZ

0

5

10

15

20

25

30

*2 *3 *4 *5 *6 *7

Cou

nt

TransmittedNot Transmitted

Figure 7. TDT for MOG-(TAAA)n. Global Chi-Square = 3.550; 5 d.f.; P = 0.726.

2: 0.727 0.947 0.080 1.195 0.000 0.600

P Value: 0.394 0.330 0.777 0.274 1.000 0.439

Allele

Figure 3:1-4: Statistical parametric maps of the fractional anisotropy (FA) (left) and Magnetic Transfer Ratio (MTR) (myelin) (right) group comparison. Similar areas in yellow on both maps correspond to the location of both the internal capsule and prefrontal white matter, and indicate smaller values of FA and myelin in schizophrenia patients (n=14) compared with controls (n=15).

Prefrontal fMRI activity and myelin reduced in schizophrenia

UNC

clustering

Bundle selection

Measurement along tract

Fractional Anisotropy

Hypothesis: MOG, MAG, MBP genes will predict quantity or distribution of myelinated tracts

Fornix

Dorsal stream

Corpus callosumCingulum

Frontal striatial projections

DTI New MRI Imaging Technique Reveals Brain Circuits

Actual white matter tracks in schizophrenic patient revealed

by DTI (colors and location by J. Fallon)

Complexities in Genetics & Neuroimaging

• Genetic variants express themselves in many ways – singularly, or combined (haplotypes, epistasis, partial penetrance…)

• What are the appropriate phenotypes to use from brain imaging data?

• How to control massive multiple testing of genome scan x brain voxels (millions x millions)?

Summary

• D2 role in schizophrenia and clozapine response?• SNAP-25 gene involved in Schizophrenia and

neurodevelopment?• BDNF gene candidate for grey matter measures?• MOG gene candidate for white matter?• Vast expanses of quality data await us: we only

need to develop our informatics sophistication…

National Alliance for Medical Imaging and Computing:

NAMICwww.na-mic.org