Overview Univariate - Multivariate Approaches: Joint Modeling of Imaging & Genetic Data • • • • • • • • • • • Vince D. Calhoun, Ph.D. Executive Science Officer & Director, Image Analysis & MR Research The Mind Research Network Distinguished Professor, Electrical and Computer Engineering (primary), Biology, Computer Science, Psychiatry, & Neurosciences The University of New Mexico Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 1 2 Genetic Information The First Challenge • Genetic: single nucleotide polymorphism (SNP) • Genetic: copy number variation (CNV) deletion insertion • Epigenetic: methylation 3 4 Overview • • • • • • • • • • • 5 Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 6 1 A Taxonomy of Approaches Overview • • • • • • • • • • • Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions J. Liu and V. Calhoun, "A review of multivariate analyses in imaging genetics," Frontiers in Neuroinformatics, vol. 8, pp. 1-11, 2014 7 8 Example: APOE4 in Alzheimer’s Disease Candidate gene approach (c1) 9 Properties 10 Why Multivariate? Cross–validation performance using the top M SNP’s selected via two different methods as the basis for an N-factor principle component model. 11 12 2 A prior based multivariate analysis (on genetic, c2) Overview Gene-set enrichment analysis • • • • • • • • • • • Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 13 A. Subramanian, et al, Proc Natl Acad Sci U S A, vol. 102, pp. 15545-15550, Oct 25 2005 A prior based multivariate analysis (on genetic, c2) 14 Overview • GSEA, Methylation, & Hippocampal Volume • • • • • • • • • • • Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions Ehrlich, S, “Associations between DNA methylation and schizophrenia-related intermediate functional and structural imaging phenotypes – a gene set enrichment analysis”, in preparation. 15 Data-driven multivariate analysis (on genetic, c2) 16 Methylation Gender Correction (c2) • Reduction of genetic data • Penalized regression (LASSO) • PCA • ICA 17 J. Liu, K. Hutchison, M. Morgan, N. I. Perrone-Bizzozero, J. Sui, and V. D. Calhoun, "Identification of Genetic and Epigenetic Factors Contributing to Population Structure," PLoS ONE, vol. 5, pp. 1-8, 2010 18 3 Epigenetic-methylation study • • Ancestry effect on methylation Genomic ~27,000 sites from 23 pairs of chromosomes 130 subjects (heavy drinker 33 females, 97 males, age 31.39.7) Goal: Association with gender, age, BMI, alcohol use, cigarette use, marijuana use, depression, stress, etc. Results: Methylation correlation 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.95 0.55 0.49 0.4 Gender Age* BMI* 0.45 Max_drinks* MJ_use* *: after gender effect correction J. Liu, K. Hutchison, M. Morgan, N. I. Perrone-Bizzozero, J. Sui, and V. D. Calhoun, "Identification of Genetic and Epigenetic Factors Contributing to Population Structure," PLoS ONE, vol. 5, pp. 1-8, 2010 19 Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 21 Overview • • • • • • • • • • • 20 Component-based analysis (on imaging, c3) Overview • • • • • • • • • • • J. Liu, K. Hutchison, M. Morgan, N. I. Perrone-Bizzozero, J. Sui, and V. D. Calhoun, "Identification of Genetic and Epigenetic Factors Contributing to Population Structure," PLoS ONE, vol. 5, pp. 1-8, 2010 D. C. Glahn, A. M. Winkler, P. Kochunov, L. Almasy, R. Duggirala, M. A. Carless, J. C. Curran, R. L. Olvera, A. R. Laird, S. M. Smith, C. F. Beckmann, P. T. Fox, and J. Blangero, "Genetic control over the resting brain," Proc Natl Acad Sci U S A, vol. 107, pp. 1223-1228, Jan 19 2010 22 Multiple brain variables & multiple genetic variables (c4) Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions J. Liu and V. Calhoun, "A review of multivariate analyses in imaging genetics," Frontiers in Neuroinformatics, vol. 8, pp. 1-11, 2014 23 24 4 RRR: one (of many) possible frameworks fMRI/SNP connection • fMRI component: specific brain regions with common independent brain function. • SNP component: a linearly weighted group of SNPs functioning together. • Relationship assumption fMRI GWAS PPI database Spatial ICA Imaging Endo-phenotype Extraction If an association of SNPs partially define a certain brain function in specific brain regions, Then, the linked fMRI and SNP components should share a similar pattern of existence across subjects. ICA Components Genetic Network construction Genetic module extraction Modules fMRI components Expression pattern SNP components Subject 1 .. .. Subject N Two sample t-test Collaborative sparse reduced rank regression Association analysis Expression pattern Module, ranking Gene, SNP Functional analysis enrichment 25 Canonical Correlation Analysis (CCA) model Parallel ICA: Two Goals Data2: (SNP) Data1: (fMRI) X1 Identify Hidden Features X2 W1 W2 A1 A2 S1 Identify Hidden Features S2 Identify Linked Features Illustration of combining both imaging and SNP data with the CCA model to identify correlated genes and voxels. MAX : {H(Y1) + H(Y2) }, <Infomax> g()=Correlation(A1 ,A 2 ) 2 = Subject to: arg max g{W1,W2 ŝ1,ŝ2 }, H. Cao, J. Duan, D. Lin, Y. Shugart, V. D. Calhoun, and Y.-P. Wang, "Sparse Representation Based Biomarker Selection for Schizophrenia with Integrated Analysis of fMRI and SNPs," NeuroImage, in press, Simulation Cov(a1i ,a 2j ) 2 Var(a 1i )×Var(a 2j ) J. Liu, O. Demirci, and V. D. Calhoun, "A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function," IEEE Signal Proc. Letters, vol. 15, pp. 413-416, 2008. 28 Initial Proof of Concept: SNP/fMRI Fusion Simulation: Designed to provide a more complete understanding of Parallel ICA while applied to genomic SNP array studies. We specified the parameters for each component and input them into PLINK, an open-source whole genome association analysis toolset [http://pngu.mgh.harvard.edu/purcell/plink/]. Data Description: 20 Sz & 43 Healthy controls fMRI: one image per subject (Target activation in AOD task) SNP: one array per subject (384 SNP genotypes - -> 367 SNPs) Conditions: sample size effect. case to control ratio, SNP array size effect, case-related SNP’s vs. total SNP’s, odds ratio, connection strength between genotype and phenotype effects Control vs Patient p<0.001 Simulation results suggest that parallel ICA, in general, is able to extract more accurately the components and connections than a correlation test, in particular for weak linkages. Results also indicate that the ratio of sample size to SNP size should be at least 0.02. However, when the data have a low odds ratio or cases vs. controls ratio, the correlation test provides results reliably, though with lower accuracy. Liu, J, et al, IEEE Bioinformatics and Biomedicine. 2008: Philadelphia, PA. 29 SNP Rs1466163 Rs2429511 Rs3087454 Rs821616 Rs885834 Rs1355920 R4765623 Rs4784642 Z score -4.08 3.97 -3.09 2.96 -2.78 -2.77 2.73 -2.71 Rs2071521 Rs7520974 2.58 2.55 Gene AADC: aromatic L-amino acid decarboxylase ADRA2A: alpha-2A adrenergic receptor gene CHRNA7: alpha 7 nicotinic cholinergic receptor DISC1: disrupted in schizophrenia 1 CHAT: choline acetyltransferase CHRNA7: cholinergic receptor, nicotinic, alpha 7 SCARB1: scavenger receptor class B, member 1 GNAO1: guanine nucleotide binding protein (G protein), alpha activating activity polypeptide O APOC3: apolipoprotein C-III CHRM3: muscarinic-3 cholinergic receptor J. Liu, G. D. Pearlson, A. Windemuth, G. Ruano, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA," Hum.Brain Map., vol. 30, pp. 241-255, 2009 30 5 SNP Selection and Approach Number of Components • Schizophrenia patients and healthy controls • SNP data component number selection • MCIC data: Boston, Iowa, Minnesota and New Mexico • Genome-wide 1M SNP data - [biallelic coding (AA, AB, or BB] • fMRI sensorimotor task- Block design motor response to auditory stimulation • 208 subjects with SNP (777365 SNPs) and fMRI data (52322 voxels) • Component number estimated to be 8 for fMRI data (MDL) • Increase component number from 2 to 60 for SNP data • Compare the identified fMRI and SNP components obtained with different SNP component numbers • SNP selection • Sliding window covering 5 consecutive component numbers • Locate a region in which identified components remain stable (select 5% most contributing SNPs from the identified component and evaluate the overlapping ratios) • SNPs differentiating schizophrenia patients and healthy controls are included (p-value < 0.005, 3318 SNPs) • SNPs related to schizophrenia risk genes are included (1843 SNPs selected, related to DISC1, COMT, etc.) • Combine selected SNPs: 5157 SNPs as final input Reference SNP component number = 5 • Parallel ICA SNP component number Overlapping ratio Correlation 6 0.843 0.982 7 0.927 0.984 8 0.851 0.957 9 0.970 0.951 • Use subject type (SZ patients. vs. healthy control) as reference • Apply reference PCA to SNP and fMRI data to reduce dimension and select components of interest • Apply parallel ICA to identify linked components optimized to the correlation between the two modalities J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, vol. 61, pp. 866-875, 2012. 31 J. Chen, V. D. Calhoun, and J. Liu, "ICA Order Selection Based on Consistency: Application to Genotype Data," in Proc. EMBS, San Diego, CA, 2012. Consistency Resulting Linked Component • fMRI component number = 8, SNP component number = 5 • One pair of linked components is identified, with p-value passing Bonferroni correction • Leave-one-out validation • Divide subjects into 10 sets • Apply parallel-ICA to 90% of the data in each validation run fMRI component SNP component index index • Evaluate the consistency of the components based on correlations between components identified in each validation run and the reference components identified with the full dataset 1 2 …… 32 Subjects 10 …… rfMRI-SNP P-value 1 3 -0.065 3.49E-01 2 2 0.042 5.48E-01 3 1 0.099 1.51E-01 4 2 -0.138 4.54E-02 5 1 0.141 4.16E-02 6 5 -0.128 6.44E-02 7 1 0.178 9.95E-03 8 4 0.282 3.39E-05 • Bootstrap: Multiple runs of parallel-ICA with 5157 randomly selected SNPs. The median correlation was 0.16 33 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, vol. 61, pp. 866-875, 2012. Validation Results • • • • For each validation run, identify the replicated fMRI/SNP component exhibiting most similar pattern to the reference fMRI/SNP component identified with full dataset Calculate the fMRI-SNP correlation between the replicated components obtained in the validation run Determine if the replicated components are the highest linked pair (highest correlation) A successful validation: both SNP and fMRI components show similar patterns to the reference SNP and fMRI components, respectively; and they are linked 34 Identified fMRI component Index of validation run rSNP-fMRI 1 0.301 Highest linked pair? 2 -0.295 √ 3 -0.334 0.339 4 -0.301 √ 5 0.246 √ 6 -0.249 0.259 7 -0.306 √ 8 0.347 √ Brain region Brodmann area Volume Max z-score 9 0.387 √ Postcentral Gyrus 1, 2, 3, 5, 7, 40, 43 9.7/4.4 6.23(42,-32,60)/5.70(-39,-32,62) 10 0.304 √ Precentral Gyrus 4, 6 8.2/1.7 6.03(42,-12,56)/4.40(-59,-12,42) Inferior Parietal Lobule 40 3.0/0.5 6.03(45,-35,57)/4.88(-45,-32,57) Medial Frontal Gyrus :6, 32 1.0/1.6 4.82(3,-3,53)/5.27(0,-3,53) Superior Temporal Gyrus 21, 22, 38 1.4/0.5 3.24(62,-12,1)/2.93(-50,-3,-5) √ J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, vol. 61, pp. 866-875, 2012. 35 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, vol. 61, pp. 866-875, 2012. 36 6 Summary of SNP Results ADNI Data • Conduct pathway analysis and functional annotation clustering based on identified 94 genes • European-American ADNI subjects (N=757) • IPA (Ingenuity Pathway Analysis) identifies “Schizophrenia of humans” as one of the top biofunctions, involving 11 genes • IPA also identifies a number of significant canonical pathways, four of which are related to neurotransmitter signaling • David’s Bioinformatics Resource reports the most significant cluster to be functionally related to synapse. A cluster annotated as “cell projection” is also identified Disease and disorder Gene p-value Schizophrenia of humans BDNF, COMT, DISC1, DRD3, ERBB4, GAD1, GRIN2B, HTR7, NOTCH4, NRG1, NRG3 6.49E-09 Neurotransmitter signaling pathway Gene p-value GABA receptor signaling Dopamine receptor signaling GABRA4, GABRG3, GAD1 COMT, DRD3, PPP2R2C 2.13E-03 7.66E-03 Neuregulin signaling Glutamate receptor signaling NRG1, NRG3, ERBB4 GRIN2B, GRID2 1.15E-02 3.74E-02 Functional annotation cluster Gene p-value Synapse GABRA4, GABRG3, GAD1, GRIN2B, GRID2, ERBB4, SHC4, OTOF, PSD3, CTBP2 DRD3, GAD1, GRIN2B, MYCBP2, DNAH11, WNT2, ESR1, CDH13, ALCAM, MYO5A 5.20E-03 Cell projection • 209 HC (Mean/SD Age = 76.05/4.94; 113 Males) with no past history of neurological or psychiatric disorder, • 367 subjects with MCI (Mean/SD Age = 74.95/7.37; 239 Males) • 181 subjects (Mean/SD Age = 75.57/7.48; 100 Males) with clinically-assessed AD • SNP data • 533,872 SNPs (after QA) • sMRI data • Baseline 1.5T MRI scans • Segmented into GM maps 9.70E-02 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, vol. 61, pp. 866-875, 2012. 37 ADNI Data S. Meda, B. Narayanan, J. Liu, N. I. Perrone-Bizzozero, M. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Sayking, and G. D. Pearlson, "A large scale multivariate parallel ICA method reveals novel imaginggenetic relationships for Alzheimer's Disease in the ADNI cohort," NeuroImage, vol. 60, pp. 1608-1621, 2012 S. Meda, B. Narayanan, J. Liu, N. I. Perrone-Bizzozero, M. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Sayking, and G. D. Pearlson, "A large scale multivariate parallel ICA method reveals novel imaginggenetic relationships for Alzheimer's Disease in the ADNI cohort," NeuroImage, vol. 60, pp. 1608-1621, 2012 38 Genetic Components by Group 39 Alzheimer’s disease functional interaction pathway S. Meda, B. Narayanan, J. Liu, N. I. Perrone-Bizzozero, M. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Sayking, and G. D. Pearlson, "A large scale multivariate parallel ICA method reveals novel imaginggenetic relationships for Alzheimer's Disease in the ADNI cohort," NeuroImage, vol. 60, pp. 1608-1621, 2012 40 sMRI/SNP Structural deficits in brain regions consistently implicated in previous schizophrenia reports, including frontal and temporal lobes and thalamus were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal CNS development, including AKT, PI3k, SLC6A4, DRD2, CHRM2 and ADORA2A. A. sMRI component –A (group difference) B. sMRI component –B (linked, but no group difference) modified from Sleegers K et al., TIGS 2009 41 K. Jagannathan, V. D. Calhoun, J. Gelernter, M. Stevens, J. Liu, F. Bolognani, A. Windemuth, G. Ruano, and G. D. Pearlson, "Genetic associations of brain structural networks in schizophrenia: a preliminary study using parallel ICA," Biological Psychiatry, vol. 68, pp. 657-666, 2010 42 7 Genetics and P3 ERP generation ERP Topography & SNP Associations • Subjects: 41 healthy subjects (24 female, 17 male) • EEG collected during AOD task, target/novel ERPs extracted Target Stimuli SNPs 0.55 Novel Stimuli SNPs • Blood sample collected, genotyped 384 SNPs from 222 genes 6 physiological systems. J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp. 809-816, 2009. Genes rs1800545 ADRA2A rs7412 APOE rs1128503 ABCB1 rs6578993 TH rs1045642 ABCB1 rs2278718 MDH1 rs4784642 GNAO1 rs521674 ADRA2A Genes rs1800545 ADRA2A rs7412 APOE rs6578993 TH rs2278718 MDH1 rs1128503 ABCB1 rs429358 APOE rs3813065 PIK3C3 rs4121817 PIK3C3 rs521674 ADRA2A 0.47 J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp. 809-816, 2009. 43 Pathway Analysis 44 Classification with SNP & fMRI SVM Classification J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp. 809-816, 2009. Subjects 20 Patients 20 Healthy Controls fMRI data Auditory Oddball Task Gene data 367 SNPs Dataset Number of Training Subjects Number of Testing Subjects 1 4 36 2 12 28 3 20 20 4 28 12 5 36 4 H. Yang, J. Liu, J. Sui, G. Pearlson, and V. D. Calhoun, "A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data to Classify Schizophrenia," Frontiers in Human Neuroscience, vol. 4, pp. 1-9, 2010. 45 46 Overview • • • • • • • • • • • fMRI + Gene fMRI Gene Constrained ICA Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions CDK14 (7q21) ZNF879 GRM6 (5q35) LOC 100506971 UGDH SLC5 ZNF354C ADAMTS2 47 J. Liu, M. Ghassemi, A. Michael, D. Boutte, W. Wells, N. I. Perrone-Bizzozero, F. Macciardi, D. Mathalon, J. Ford, S. Potkin, J. Turner, FBIRN, and V. D. Calhoun, "An ICA with reference approach in identification of genetic variation and associated 48 brain networks," Frontiers in Human Neuroscience, vol. 6, pp. 1-10, 2012. 8 Parallel ICA w/ Reference (pICA-R) pICA-R: Schizophrenia & ANK pathway 1 1/2 A 1/2 X 1/2 A 1/2 S 1/2 W S 1/2 W1/2 X 1/2 1 Y1/2 , U1/2 W1/2 X W10/20 1 e U1/2 F1 max H Y1 max E lnf y 1 Y1 F2 max H Y2 dist 2 r, S 2k max E lnf y 2 Y2 λ r (W2 X 2 )k 2 cov A 1i , A 2j 2 F3 max corr A 1i , A 2j max i, j i, j var A 1i var A 2j 2 2 J. Chen, V. D. Calhoun, G. D. Pearlson, N. Perrone-Bizzozero, J. Sui, J. A. Turner, J. Bustillo, S. Ehrlich, S. Sponheim, J. Canive, B. C. Ho, and J. Liu, "Guided Exploration of Genomic Risk for Gray Matter Abnormalities in Schizophrenia Using Parallel Independent Component Analysis with Reference," NeuroImage, vol. 83, pp. 384-396, 2013. 49 J. Chen, V. D. Calhoun, G. D. Pearlson, N. Perrone-Bizzozero, J. Sui, J. A. Turner, J. Bustillo, S. Ehrlich, S. Sponheim, J. Canive, B. C. Ho, and J. Liu, "Guided Exploration of Genomic Risk for Gray Matter Abnormalities in Schizophrenia Using Parallel Independent Component Analysis with Reference," NeuroImage, in press Overview • • • • • • • • • • • 50 3-way parallel ICA Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 51 52 Results (N=112, fMRI, sMRI, 65K SNPs in coding genes) (a) sMRI Component (b) fMRI Component Corr = 0.44 Pval = 1.5e-5 Corr = 0.39 Pval = 9.0e-4 Genetics Corr = 0.34 Pval = 2.0e-3 (c) Genetic Component Functional Activity Structural Variation Overview • • • • • • • • • • • Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions (d) Pairwise Connections V. Vergara, A. Ulloa, V. D. Calhoun, D. Boutte, J. Chen, and J. Liu, "A Three-way Parallel ICA Approach to Analyze Links among Genetics, Brain Structure and Brain Function," NeuroImage, in press, 54 9 55 Overview • • • • • • • • • • • 56 Challenges & Issues • Overfitting (cross-validation, prior information) • Replication • Interpretation (e.g. GSEA does not model exact interaction among SNPs, latent component may not have a biological purpose). • Can be partially addressed by incorporating known biological, cellular, or behavioral specific information. • Lots of information not fully incorporated (proteomic, gene expression, epigenetic, behavioral and environmental variables) • CNV (low incidence) Introduction A Taxonomy of Approaches Candidate gene analysis (c1) A prior based multivariate analysis (on genetic, c2) Data-driven multivariate analysis (on genetic, c2) Component-based analysis (on imaging, c3) Multivariate analysis across both (c4) Multiple variables for brain + gene w/ priors (c4+c2) Multivariate analysis across N>2 phenotypes (c4+) Integration of additional bioinformatics resources Challenges & Future Directions 57 58 Software • http://mialab.mrn.org/software • freeware, written in MATLAB • Group ICA of fMRI Toolbox (GIFT) • • • • • • • Single subject/Group ICA Many algorithms (e.g. ICA, cICA, IVA) MANCOVA testing framework Source Based Morphometry Model order estimation ICASSO (clustering/stability) Dynamic connectivity (dFNC) Left Hemisphere Visual Stimuli Onset • Fusion ICA Toolbox (FIT) • Parallel ICA, jICA • mCCA+jICA & much more! • Simulation Toolbox (SimTB) • Flexible generation of fMRI-like data • Task & rest fMRI • Space * Time * Amplitude 60 10