1. Imaging Genetics: Towards Mechanistic Understanding of Psychiatric Disorders Yihong Zhao Ph.D. Department of Child and Adolescent Psychiatry New York University Langone Medical Center

2. Overview ● Discovery Science in Psychiatric Research ♦ Transition from traditional dichotomous diagnoses to dimensional approaches (RDoC) ♦ Potential utility of endophenotypes in identifying causal genes and understanding etiological processes ● Big Data Approach ♦ Currently Available Big Data ♦ Application, promise and challenges

3. Psychiatric disorders are a major health concern Autism spectrum disorder (ASD) Attention-deficit/hyperactivity disorder (ADHD) Alzheimer disease (AD) Depression Personality disorders Schizophrenia (SZ) Substance abuse Others ● Approximately 25% of the adult population have one or more diagnosable mental disorders in a given year. ● The cumulative lost economic output: $16,000 billion over the next 20 years. Diagnostic and Statistical Manual of Mental Disorders (DSM):

4. Endophenotypes Genes Genomics Epigenomics Expression RNA genes, protein-coding genes Transcriptomics Proteomics Metabolomics Interactomics neuron development, organelle Neuroscience Imaging Brain interactome Cell biology Neuroscience Diagnosis Self-report Environmental, social and psychological factors de novo mutations Epigenomic modifications feedback Cells RNA, proteins, metabolites Molecules Brain Structure, circuits, physiology Symptoms Behavioral tests Zhao Y, Castellanos FX (2016) Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations. J Child Psychol Psychiatry. 57: 421-439 Psychiatric disorders: from genes to symptoms

5. “Missing heritability” in many psychiatric disorders ADHD: 75-90% ASD: ~80% SZ: 60-85% AUD (Alcohol use disorders): 40-70% Psychiatric disorders are highly heritable: But major genes involved have not been identified! Common variants (allele frequency >5%): > 90-95% of the heritable component of a disease has been left unexplained after extensive GWAS studies; cannot be used to explain disease prevalence Rare variants (allele frequency <5%)? Polygenic control and pleiotropic effect. Epigenetic modifications are involved.

6. Current status and challenges in psychopathology of mental disorders ● Symptom-based nosology of mental disorders: ♦ Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD) ♦ Diagnostic category labels: based on observing specific patterns of symptoms. ● Limitations of categorical model of mental disorders ♦ Comorbidity - Typical patients meet criteria for more than one specific diagnosis; - Many putatively distinct disorders have etiologic factors in common. ♦ Within-category heterogeneity with respect to key clinical feature - Compulsive personality disorder: 4/8 symptoms for a diagnosis ♦ Validity of subthreshold symptomatology - Continuous phenomena in nature

7. Endophenotypes Genes Genomics Epigenomics Expression RNA genes, protein-coding genes Transcriptomics Proteomics Metabolomics Interactomics neuron development, organelle Neuroscience Imaging Brain interactome Cell biology Neuroscience Diagnosis Self-report Environmental, social and psychological factors de novo mutations Epigenomic modifications feedback Cells RNA, proteins, metabolites Molecules Brain Structure, circuits, physiology Symptoms Behavioral tests Zhao Y, Castellanos FX (2016) Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations. J Child Psychol Psychiatry. 57: 421-439 Psychiatric disorders: from genes to symptoms Imaging genetics: Using neuroimaging technologies as phenotypic assays to evaluate genetic variation. Endophenotypes: Structural or functional brain imaging phenotypes. Closer to the action of genes and can explain the behavior. Genetically tractable.

8. Research Domain Criteria (RDoC): Provides a dimensional and dynamic framework for characterizing psychiatric dysfunction. New Taxonomy for Mental Disorders RDoC is an attempt to create a new kind of taxonomy for mental disorders by bringing the power of modern research approaches in genetics, neuroscience, and behavioral science to the problem of mental illness. Dimensional System It relies on dimensions that span the range from normal to abnormal. Incorporating Many Levels of Data RDoC also incorporates units of analysis beyond those found in the DSM: Allowing RDoC to be informed by insights into genes, molecules, cells, circuits, physiology, and large-scale paradigms

9. RDoC approach: Major domains 1. Negative Valence Systems - Acute Threat ("Fear"), Potential Threat ("Anxiety"), Sustained Threat, Loss, Frustrative Nonreward 2. Positive Valence Systems - Approach Motication, Initial Responsiveness to Reward Attainment, Sustained Responsiveness to Reward Attainment, Reward Learning, Habit 3. Cognitive Systems - Attention, Perception, Declarative Memory, Language, Cognitive Control, Working Memory 4. Social Processes - Affiliation and Attachment, Social Communication, Perception and Understanding of Self, Perception and Understanding of others 5. Arousal and Regulatory Systems - Arousal, Circadian Rhythms, and Sleep-Wakefulness

10. RDoC approach: Incorporating various levels of data ● Conventional diagnostic systems typically rely on self- report and behavioral measures alone. ● RDoC framework has the explicit goal of allowing investigators access to a wider range of data: genes molecules cells neural circuits physiology behaviors self-reports

11. RDoC approach: A matrix Domains/structuresGenesMoleculesCellsCircuitsPhysiologyBehaviorSelf-reportsParadigms Negative Valence Systems XXXX Positive Valence Systems XXXX Cognitive Systems XXXX Social Processes XXXX Arousal and Regulatory Systems XXXX

12. Endophenotypes Genes Genomics Epigenomics Expression RNA genes, protein-coding genes Transcriptomics Proteomics Metabolomics Interactomics neuron development, organelle Neuroscience Imaging Brain interactome Cell biology Neuroscience Diagnosis Self-report Environmental, social and psychological factors de novo mutations Epigenomic modifications feedback Cells RNA, proteins, metabolites Molecules Brain Structure, circuits, physiology Symptoms Behavioral tests Zhao Y, Castellanos FX (2016) Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations. J Child Psychol Psychiatry. 57: 421-439 Psychiatric disorders: from genes to symptoms

13. Big data resources Databases Genetic data Molecular data Brain Imaging Behavior or symptom Psychiatric disorder-oriented Psychiatric Genome Consortium (PGC)+ + The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium + ++ International Schizophrenia Consortium (ISC) + + Autism Consortium + The Autism Sequencing Consortium+ + National Database for Autism Research (NDAR) + ++ Simons Simplex Collection+ + Autism Genetics Resource Exchange+ + ADHD-200 Consortium ++ Human brain-focused Scalable Brain Atlas + The Human Brain Atlas (MSU) + 1000 Functional Connectomes Project (FCP) ++ Nathan Kline Institute-Rockland Sample ++ Allen Brain Atlas + Human Brain Transcriptome (HBT) + Human genetics-related Human Genome Project+ Gene Ontology (GO) + Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways + The ENCyclopedia Of DNA Elements (ENCODE) Project + Human Gene Expression (HuGe) Index + Human Interactome Project +

14. Big Data - Extremely complex - Highly Heterogeneous - Highly dimensional Transfer Big Data into Knowledge -Big data approach is hypothesis -Linking data across levels to formulate hypotheses -Identifying reliable measures for disease subgroup detection -Provide insights into biological mechanism of psychiatric disorders

15. Big Data analysis pipeline Study design Extracting/cleaning data Analysis/modeling Prioritizing associated signals Replication of top associated signals Dimensionality reduction

16. Large-scale Molecular Genetics of Schizophrenia (MGS) consortium (3,827 controls, 4,196 cases) Identification of 42 SNP sets (associated with a >=70% risk of SZ) Preselection of 2,891 SNPs (showing at least a loose association with SZ) Identification of 723 interacting SNP sets (regardless of clinical status) Generalized factorization SNP-set kernel association test Hypergeometric test Identification of genotypic networks (composed of SNP sets sharing SNPs or subjects) Identification of 342 phenotypic sets or clinical syndromes (which cluster in particular cases with SZ regardless of genetic background) Non-negative matrix factorization Hypergeometric test Genotypic-Phenotypic Architecture Identification of 8 classes of SZ (based on grouping of the relationships between SNP sets or networks and phenotypic sets) Genotype Phenotype A Big Data approach example: Uncovering the genotypic- phenotypic architecture of the schizophrenia (SZ) More than 81% of the genotypic- phenotypic relationships found in the MGS dataset could be replicated in two independent samples. Importantly, the interactive networks explained the risk for SZ more than the average effects of all SNPs (24%).

17. Promise of Big Data Approach Disease classification - Reclassifying disorders into molecular subtypes - Modeling behaviors from subgroups representing “rare events” Biomarker discovery - Neural phenotypes & molecular biomarker Detection of common and rare genetic variants - Increasing the power by using endophenotypes - Rare events  rare variants  highly penetrant Systems view of pathophysiology - How genetic contributors and environmental factors intertwine to cause/influence brain dysfunction

18. Challenges/Limitations Data sharing Data integration and missing data - Variations caused by experiments cross sites  standard ontologies or protocols needed - Systematic data collection (RDoC matrix) Possibility of complementing the inaccessibility of the brain - Blood and in particular brain cerebrospinal fluid sample Integrative analysis - Deep data and broad data Validation of robustness of results - Hypothesis-free, data-driven discovery science needed to be validated

19. Acknowledgments NIH-NIAAA grant (R21AA023800) Neuroscience Fellowship from the Leon Levy Foundation