Glahn Lab | Research in Review

Abnormal structural and functional connectivity (interaction between brain regions) is central to the pathophysiology of psychotic illnesses like schizophrenia and psychotic bipolar disorder. Modern neuroimaging techniques and analytic strategies provide an unprecedented capacity to more fully characterize the functional and structural psychotic disconnectivity. Individuals with psychotic illness and their unaffected relatives have abnormal connectivity, suggesting that at least a portion of psychotic disconnectivity is associated with genetic predisposition for the diseases. Imaging-based connectivity endophenotypes are ideally suited to aid the functional characterization of putative risk genes, allowing us to move beyond a genotype-phenotype association to delineating mechanisms that give rise to psychotic illnesses. Recently, large-scale exome sequencing in individuals of European ancestry provided the strongest evidence to date for specific genetic variants that increase risk for psychosis. These primarily rare mutations were spread across gene networks involved in neuronal processes, including calcium channels and postsynaptic signaling. Our goals are to replicate these promising genetic findings in a different ethnic group, African-Americans, and determine whether and how these gene sets impact psychotic disconnectivity. African-Americans, an underserved population, have ~32% more highly deleterious non-synonymous rare variants in these networks than individuals of European ancestry, improving our power to detect rare variants. Our aims are to: (1) use modern MRI acquisition and analysis techniques based on the Human Connectome Project to document psychotic disconnectivity in 750 African Americans (375 with a psychotic disorder and 375 demographically matched comparison subjects). We will test hypotheses that diagnostic and dimensional indices of psychosis are associated with reduced global functional connectivity but intact global structural connectivity, combined with aberrant connectivity between specific regions or tracts; (2) conduct whole exome sequencing (WES) to test the influence of rare non-synonymous variants from genes in previously identified gene sets on psychosis risk using a network-centered analysis strategy. We will test hypotheses that the voltage-gated calcium ion channel, and the ARC-associated scaffold protein and the NMDAR postsynaptic signaling complexes influence diagnostic and dimensional indices of psychosis; and (3) apply this same network-centric test to determine if gene sets implicated in illness risk also influence functional and structural psychotic disconnectivity. Linking these genetic pathways to psychotic disconnectivity will provide mechanistic insights into the genomic influences on psychotic illness. Our collaborative application includes sites at Yale/Hartford Hospital (DC Glahn PI), Stanford (RA Poldrack PI) and Texas Biomedical Research Institute (J Blangero PI). Our results should bolster our understanding of the genetic architecture of psychotic illness and provide important clues for traversing the chasm between identified genetic networks and the behaviorally defined disorder. Title: “Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders” PI: David C Glahn, PhD, John Blangero, PhD, Raquel E Gur, MD, PhD Abstract: Our goal is to identify genes that increase risk for affective and psychotic disorders like schizophrenia, bipolar disorder and major depression. Although these highly heritable diseases are associated with substantial morbidity and mortality, their etiologies remain poorly understood. Identifying genes that contribute to their risk should provide critical information leading to the development of novel diagnostic and therapeutic strategies. We propose an eight site international consortium designed to identify rare causal variants for affective and psychotic illnesses using extended multiplex pedigrees. These multigenerational families were previously identified and include at least three individuals with confirmed diagnoses. We focus on the identification of rare variants (with population MAF d 0.01) that have a large absolute effect size, although it may be present in a small number of related affected individuals. While such rare functional variants may have a small effect on population attributable risk or variant-specific heritability, they can be sufficient to verify that a given gene is involved in illness risk. Pedigree-based studies represent an implicit enrichment strategy for identifying the rarest (e.g., private or pedigree-specific) variants, as Mendelian transmissions from parents to offspring maximize the chance that multiple copies of rare variants exist in the pedigree. Whole genome sequencing (WGS) allows a comprehensive search for rare single nucleotide variants (SNVs) or more complex sequence variation such as CNVs or INDELS. To identify rare, potentially private, variants that increase risk for affective or psychotic illness, we will create a repository of 4043 individuals from previously collected multiplex pedigrees (n=331) that will be analyzed with WGS. 1915 of these individuals have available WGS and we will obtain sequence data for 2128 additional subjects. Phenotypes include classical dichotomous diagnoses, quantitative scales derived from standardized interviews reflecting dimensional symptom classes, and neurocognitive endophenotypes. Our specific aims are to: 1) synergize phenotypic assessments, create dimensional indices of psychopathology, and rank endophenotypes across sites; 2) obtain WGS on 2128 individuals from extended pedigrees by direct sequencing of 1000 samples at 30x coverage and perform highly accurate pseudo-sequencing using a high density SNP framework to obtained the remaining 1128; 3) localize and identify QTLs influencing illness phenotypes /endophenotypes; 4) perform pedigree-based genome-wide association using likely functional variants; 5) identify rare functional CNV/INDELs influencing illness risk or endophenotypes; 6) perform gene-centric association tests in an independent sample. Our collaborative project includes applications from Yale University (DC Glahn, PD/PI), Texas Biomedical Research Institute (J Blangero, PD/PI) and the University of Pennsylvania (RE Gur, PD/PI). In addition, the Universities of Pittsburgh (V Nimgaonkar), Costa Rica (H Ravents), Edinburgh (AM McIntosh), and Western Australia (A Jablensky) and the intramural NIMH (F McMahon) will participate.

Our goal is to identify genes that increase risk for affective and psychotic disorders like schizophrenia, bipolar disorder and major depression. Although these highly heritable diseases are associated with substantial morbidity and mortality, their etiologies remain poorly understood. Identifying genes that contribute to their risk should provide critical information leading to the development of novel diagnostic and therapeutic strategies. We propose an eight site international consortium designed to identify rare causal variants for affective and psychotic illnesses using extended multiplex pedigrees. These multigenerational families were previously identified and include at least three individuals with confirmed diagnoses. We focus on the identification of rare variants (with population MAF d 0.01) that have a large absolute effect size, although it may be present in a small number of related affected individuals. While such rare functional variants may have a small effect on population attributable risk or variant-specific heritability, they can be sufficient to verify tha a given gene is involved in illness risk. Pedigree-based studies represent an implicit enrichment strategy for identifying the rarest (e.g., private or pedigree-specific) variants, as Mendelian transmissions from parents to offspring maximize the chance that multiple copies of rare variants exist in the pedigree. Whole genome sequencing (WGS) allows a comprehensive search for rare single nucleotide variants (SNVs) or more complex sequence variation such as CNVs or INDELS. To identify rare, potentially private, variants that increase risk for affective or psychotic illness, we will create a repository of 4043 individuals from previously collected multiplex pedigrees (n=331) that will be analyzed with WGS. 1915 of these individuals have available WGS and we will obtain sequence data for 2128 additional subjects. Phenotypes include classical dichotomous diagnoses, quantitative scales derived from standardized interviews reflecting dimensional symptom classes, and neurocognitive endophenotypes. Our specific aims are to: 1) synergize phenotypic assessments, create dimensional indices of psychopathology, and rank endophenotypes across sites; 2) obtain WGS on 2128 individuals from extended pedigrees by direct sequencing of 1000 samples at 30x coverage and perform highly accurate pseudo-sequencing using a high density SNP framework to obtained the remaining 1128; 3) localize and identify QTLs influencing illness phenotypes /endophenotypes; 4) perform pedigree-based genome-wide association using likely functional variants; 5) identify rare functional CNV/INDELs influencing illness risk or endophenotypes; 6) perform gene-centric association tests in an independent sample. Our collaborative project includes applications from Yale University (DC Glahn, PD/PI), Texas Biomedical Research Institute (J Blangero, PD/PI) and the University of Pennsylvania (RE Gur, PD/PI). In addition, the Universities of Pittsburgh (V Nimgaonkar), Costa Rica (H Ravents), Edinburgh (AM McIntosh), and Western Australia (A Jablensky) and the intramural NIMH (F McMahon) will participate.

The goal of this study is to localize and characterize genes that influence normal maturation-related changes in neurocognitive, neuroanatomic and neurophysiological indices. A substantial number of brain-related traits exhibit gene by age interactions, suggesting a heritable basis for neurocognitive, neuroanatomic and neurophysiological changes with age and highlighting the potential role of genes in differential brain maturation. We will utilize novel analytical approaches to investigate genetic and environmental influences on variation in brain, cognition, and behavior from a developmental perspective, modeling complex effects in both the genetic and environmental realms, and potential interactions between them, while allowing for changes in these effects across development, in the Philadelphia Neurodevelopmental Cohort (PNC). The PNC includes ~9500 individuals between the ages of 8 - 21 years who have been characterized for brain, behavior, and genetic variables. All participants were assessed neuropsychiatrically and completed a Computerized Neurocognitive Battery (CNB). Interviews included assessment of demographics, life events and stressors, school performance, medical history, and screening for psychopathology and presence and duration of associated symptoms. The CNB included an estimate of IQ as well as 14 tests designed to assess five neurobehavioral functions. A subset of ~1450 participants underwent neuroimaging, including structural and functional MRI and diffusion tensor imaging. Approximately 8700 PNC participants have been genotyped for 500K - 950K genome-wide SNPs, obtained on a diverse variety of genotyping platforms. The large size of this data set, combined with the rich and detailed assessments in a variety of phenotypic domains, provides an unparalleled opportunity to study gene-by-age and gene-by-environment interactions in neurodevelopment and their potential contribution to risk of mental illness. The specific aims of the proposed study are to: 1) Localize genes influencing variation in neurodevelopment in the PNC; 2) Assess whether these genetic effects change with age and identify additional loci exhibiting gene- by-age interactions; and 3) Assess complex environmental effects on neurodevelopment and test whether they interact with, and moderate or mediate, genetic effects. Laura Almasy, Texas Biomedical Research Institute, is contact PI of this application and co-PIs David Glahn, Yale University, and Raquel Gur, University of Pennsylvania, will lead subcontracts. Dr. Almasy provides expertise in genetic analysis of complex phenotypes, including gene-by-age and gene-by-environment interactions. Drs. Glahn and Gur provide expertise in cognitive neuropsychology, neuroimaging, and psychiatry.

The ENIGMA Center for Worldwide Medicine, Imaging and Genomics is an unprecedented global effort bringing together 287 scientists and all their vast biomedical datasets, to work on 9 major human brain diseases: schizophrenia, bipolar disorder, major depression, ADHD, OCD, autism, 22q deletion syndrome, HIV/AIDS and addictions. ENIGMA integrates images, genomes, connectomes and biomarkers on an unprecedented scale, with new kinds of computation for integration, clustering, and learning from complex biodata types. ENIGMA, founded in 2009, performed the largest brain Imaging studies in history (N>26,000 subjects; Stein +207 authors, Nature Genetics, 2012) screening genomes and images at 125 institutions in 20 countries. Responding to the BD2K RFA, ENIGMA'S Working Groups target key programmatic goals of BD2K funders across the NIH, including NIMH, NIBIB, NICHD, NIA, NINDS, NIDA, NIAAA, NHGRI and FIC. ENIGMA creates novel computational algorithms and a new model for Consortium Science to revolutionize the way Big Data is handled, shared and optimized. We unleash the power of sparse machine learning, and high dimensional combinatorics, to cluster and inter-relate genomes, connectomes, and multimodal brain images to discover diagnostic and prognostic markers. The sheer computational power and unprecedented collaboration advances distributed computation on Big Data leveraging US and non-US infrastructure, talents and data. Our projects will better identify factors that resist and promote brain disease, that help diagnosis and Prognosis, and identify new mechanisms and drug targets. Our Data Science Research Cores create new algorithms to handle Big Data from (1) Imaging Genomics, (2) Connectomics, and (3) Machine Learning & Clinical Prediction. Led by world leaders in the field who developed major software packages (e.g., Jieping Ye/SLEP), we prioritize trillions of computations for gene-image clustering, distributed multi-task machine learning, and new approaches to screen brain connections based on the Partition Problem in mathematics. Our ENIGMA Training Program offers a world class Summer School coordinated with other BD2K Centers, Worldwide scientific exchanges. Challenge-based Workshops and hackathons to stimulate innovation, and Web Portals to disseminate tools and engage scientists in Big Data science.

Watch the video ENIGMA Consortium. Dr. David Glahn reflects on the setbacks in neuroscience that prompted ENIGMA, allowed the collaboration to flourish, and where that can take us next.

Cardiovascular disease (CVD) remains the leading cause of death in the United States. Although CVD risk is heritable, identification of causal genes in risk pathways has been slow. This project focuses on the identification of causal genes that influence variation in susceptibility to CVD by concentrating on genetic dissection of quantitative endophenotypes including carotid wall thickness, lipids, obesity-related phenotypes, blood pressure-related phenotypes, the insulin/glucose axis, inflammatory markers, oxidative stress markers, hemostasis/coagulation factors, and measures of brain white matter hyperintensities that are genetically correlated with CVD risk. We will utilize existing samples/data from a valuable genetic resource, the San Antonio Family Study (SAFS), involving large extended pedigrees of Mexican American individuals. This long- running highly successful project has produced a large number of quantitative trait locus (QTL) localizations of relevance for CVD risk. In this project, we move from QTL localization to causal gene identification. Our approach to CVD-risk gene discovery is comprehensive; we will utilize whole genome sequencing to capture all possible functional variants in 1,957 individuals from 45 large pedigrees. The large pedigrees to be used represent an optimal study design for the detection of rare functional variants. Advanced statistical genetic methods will be employed to identify the likely causal genes/variants in quantitative trait locus (QTL) regions influencing CVD risk. To achieve our objectives, we will (1) localize additional CVD-related QTLs due to rare functional variants using novel pedigree-specific localization methods, (2) obtain whole genome sequence information for 1,957 Mexican American individuals, (3) identify causal genes underlying existing QTLs influencing CVD risk using WGS information, (4) perform agnostic genome-wide direct association scans using non-synonymous coding variants to identify novel rare functional protein-altering variants influencing CVD risk and, (5) use a novel whole genome assay measuring variant-specific functional regulatory potential to permit genome-wide direct association scans using the predicted functional variants to identify novel rare regulatory variants influencing CVD risk. Given the enormous impact of CVD to mortality rates and the economic burden this disease imposes, it is clear that new methods of genomic analysis are necessary to enable the identification of novel genes and pathways involved in disease risk. The results of this project should identify causal genes underlying CVD risk. Identification of the causal genes will obligately generate on the pathways of these genes and will directly identify novel drug targets.

The glutamate hypothesis of schizophrenia posits that N-methyl-D-aspartate (NMDA) receptor function is disrupted in individuals with the illness. Glutamatergic models of schizophrenia were initially based on observations that the administration of agents that block NMDA receptors, such as ketamine or phencyclidine (PCP), induce both positive and negative schizophrenia-like symptoms. Glutamatergic receptors are present throughout the brain and are thought to mediate the local and long-range cortical connectivity that is critical for good cognitive functioning. Furthermore, NMDA receptors are located on brain circuits that regulate dopamine release, suggesting that dopaminergic deficits in schizophrenia may be related to glutamatergic dysfunction. While a number of promising new treatments for schizophrenia are based upon modulating the glutamatergic system, it is unclear if the genes that regulate this system influence risk for the illness directly or, potentially, influence schizophrenia endophenotypes. Endophenotypes are quantitative traits genetically associated with disease liability that can be used to help to characterize how risk genes might lead to the hallmark symptoms of the illness. The goal of this study is to test the influence of the glutamatergic gene pathway as a whole, followed by assessments of individuals genes, on previously collected neurocognitive and functional and structural neuroimaging endophenotypes for schizophrenia in 1550 randomly acquired Mexican-American individuals from large extended pedigrees with available whole genome sequence data. These data, acquired as part of the NIMH funded “Genetic of Brain Structure and Function” study, represent a substantial scientific resource and make the current investigation possible. Our specific aims are to: (1) Empirically establish neuroanatomic, functional connectivity and neurocognitive endophenotypes for schizophrenia using a novel genetic enrichment analysis. This enrichment test determines if signals observed in the large Psychiatric Genetics Consumption (PGC) schizophrenia genome-wide association analysis overlap with variants influencing neurocognitive or neuroimaging traits. If so, a trait is considered an endophenotype for schizophrenia; (2) Apply a random-effects approach to gene pathway-centric testing for arbitrary pedigrees that employs an exact likelihood ratio test to determine if genes in the glutamate system influence neuroanatomic, functional connectivity or neurocognitive endophenotypes for schizophrenia; and (3) for endophenotypes associated with the glutamatergic gene pathway in Aim 2, employ computationally similar gene-centric tests to pinpoint the specific gene influencing the neuroimaging or neurocognitive trait. Our study is designed to use existing phenotypic and whole genome sequence data to empirically establish neurocognitive and neuroimaging endophenotypes for schizophrenia and to associate these endophenotypes to genes in the glutamatergic pathway. If successful, this application will provide a set of brain-related traits that are sensitive to genetic liability for schizophrenia and influenced by glutamatergic genes. Such traits will provide clear markers, and potentially endpoints, for schizophrenia treatment studies applying novel glutamatergic agents. Thus, the significance of this study is the potential to improve our understanding of new and promising treatments for schizophrenia.

Recent advances in inexpensive whole exome and whole genome sequencing have opened up the prospect of identifying rare variants influencing common, complex diseases. Though only carried in a few individuals each, highly penetrant rare variants may collectively explain a large portion of Disease risk. Such variants might be private to a single Family, making them difficult to identify even in large case/control samples. However, the study of rare variants in extreme families has the potential to provide groundbreaking insights into the biology underlying common Disease. For example, families with rare forms of hypercholesterolemia taught us much about the biology of heart Disease. Schizophrenia (SCZ) is a heritable mental illness associated with substantial Morbidity and mortality. Identifying genes that contribute to risk of Psychosis, a defining feature of SCZ, shoul provide critical information regarding SCZ pathophysiology. We have identified a large Extended Family that has all the hallmarks of a potentially Mendelian, monogenic Form of Psychosis due to a highly penetrant founder mutation. There are at least 36 individuals with Psychosis (verified by in person Diagnostic interviews) in this Family who are grandchildren or great grandchildren of a single couple. Additionally, the Family comes from an isolated Population in a remote Location in Costa Rica and there is known Consanguinity in the pedigree. Interestingly, there are also indications of Immune system involvement in affected members of the Family. The goal of this study is to utilize exome sequencing to identify the Mutation responsible for Psychosis in this Family and characterize its effects, including Penetrance, variable expressivity in the neurocognitive and Neurological domains, and potential Immune system involvement. We will also assess the Prevalence of the Mutation in individuals with SCZ in Costa Rica and test whether other mutations in this gene may influence SCZ risk in a sample of U.S. Caucasian, Hispanic, and African American cases and controls.

The ultimate goal of our renewal application is the discovery of genes that predispose to mental illnesses. While a number of genome-wide significant quantitative trait loci (QTL) have been localized for mental illnesses, these findings have yet to result in true gene identifications. Yet, progress in elucidating the pathophysiology o major mental disorders, and subsequent treatment interventions, is predicated on causal gene identification. In our renewal application, we will utilize exhaustive genomic information obtained from whole genome sequencing (WGS) to identify causal variants/genes influencing endophenotypes for schizophrenia, bipolar disorder and/or major depression. An endophenotype is a heritable trait that is genetically correlated with disease liability, providing greater power to localize disease-related genes than affection status alone. Rare variants appear to be important in mental illness. Pedigree-based studies represent an implicit enrichment strategy for identifying rare variants and a pedigree-specific rare functional variant can be sufficient to verify that a given gene is involved in phenotypic variation. In the initial phase of our study, we acquired neuroanatomic, neurophysiologic and neurocognitive endophenotypes for mental illness in 1350 Mexican Americans from randomly selected extended pedigrees. Using existing high density SNP data, we successfully localized multiple genome-wide significant QTLs influencing endophenotypic variation. We will now move beyond QTL localization to the identification of genes that influence these endophenotypes. Achieving this goal is greatly enhanced by the availability of WGS data on ~2100 individuals, including all endophenotyped subjects. Our specific aims are to: (1) acquire structural and functional brain images and conduct neuropsychological examinations on 600 additional Mexican American family members with WGS data but without brain-related endophenotypes; (2) identify causal variants underlying existing QTLs influencing mental illness-relevant endophenotypes; (3) perform agnostic pedigree-based genome-wide association using only functional non-synonymous coding variants or putative regulatory variants to identify additional genes/variants influencing brain endophenotypes; and (4) Test for pleiotropic effects of the most likely variants identified in Aims 2 & 3 in a sample of 1000 schizophrenia cases, 1000 bipolar depression cases, 1000 major depressive disorder cases and 1000 controls from the NIMH's Center for Collaborative Genetic Studies of Mental Disorders. Our collaborative project includes applications from John Blangero, Texas Biomedical Research Institute, and David C Glahn, Yale University. Subcontracts for phenotyping (UTHSCSA; RE Olvera) and image analysis (University of Maryland, P Kochunov) are also included. This renewal application is designed to extend our initial study by identifying the specific genes that influence mental illness endophenotypes.