Hilary Coon - US grants

Ethnographic, micro-social and structured interview methods are combined in a study of the practices and experiences constituting dissociative episodes in Tibetan monastic meditation training and ethnopsychiatry. Case studies are conducted with two groups of subjects, one drawn from monks being trained in meditation, one drawn from subjects currently in ethnopsychiatric treatment for a dissociative syndrome. All subjects will have recently experienced at least one dissociative episode fitting at least one of three types of dissociative phenomena: Identity discontinuity, depersonalization/derealization, or imaginative involvement. Outcomes will include a descriptive analysis of Tibetan practices and experiences around these types of dissociation, an interpretive motivational analysis of the organization of these practices and experiences, content, thematic and narrative-structural analyses of experiences, and a preliminary comparison between Tibetan diagnostic practices and those of Western biomedical psychiatry, the latter represented by the outcomes of administrations of the SCID-D (Structured Clinical Interview Diagnosis - Dissociative). Cross-sectional comparisons will be made between SCID-D diagnoses for subjects in the monastic meditation training and those in ethnopsychiatric treatment; also between expert Tibetan diagnoses for these two groups. A diachronic comparison will compare original SCID-D and Tibetan diagnoses with 6 month follow-ups.

DESCRIPTION (Adapted from applicant's abstract): The purpose of this study is to combine information from variables measuring psychophysiological endopheno-types of smooth pursuit eye movement (SPEM), and the P50 evoked auditory response to investigate the genetics of schizophrenia. Studies will be conducted in two unique populations, which will allow us to an underlying quantitative liability to schizophrenia: 1) 14 moderately-sized Utah pedigrees genotyped with over 500 highly informative microsatellite markers, so that a disease gene will map 5 cM or less from a genetic marker. These families have also been phenotyped with P50, and new pilot SPEM data are promising. 2) A sample from an isolated island population in Palau, Micronesia which will include information from all living schizophrenics and their families. Because it is a population isolate, a gene contributing to disease suscept-ibility may be more likely to reside on a single ancestral haplotype. Experiment 1) Variable Selection: Utah Sample. Variables from P50 and SPEM will be examined in the Utah families to determine which abnormalities co-occur with illness, show substantial variability, show abnormalities in unaffected family members, and finally show overlap across phenotypic domains. Experiment 2) Segregation Analysis: Utah Sample. Using the Utah families, the best quantitative measures from experiment 1 will be tested to determine which are the most consistent with sample patterns of transmission. A composite variable including schizophrenia and the endophenotypes will be generated which will represent a quantitative liability for schizophrenia. Experiment 3) Linkage analysis: Utah Sample. Linkage analyses will be performed using the Utah families described above with the quantitative endophenotypes and liability measure. Experiment 4) Normative studies of the endophenotypes in Palau. A sample of 70 Palauan schizophrenics and 70 matched normal controls will be analyzed to determine the phenotypic distribution of SPEM and P50 variables in Palauan subjects. Experiment 5) Application of Experiments 1-3 in the Palau Sample. Analytic strategies developed and tested in the Utah sample in experiments 1-3 will be applied to the Palau sample. Although regions of potential linkage will take priority in the linkage phase of the Palau sample, a genome scan will also be employed as genes for schizophrenia may differ in this ethnic isolate. Experiment 6) Linkage disequilibrium. In the event that a region of linkage is found, they will test for linkage disequilibrium using techniques that have been applied in similar Finnish populations.

Core B is designed to handle all aspects of statistics associated with the Utah Autism Program, including data entry from each of the projects into a common format, and data analyses for the individual projects as well as cross-project analyses. As planned, the core will be directed by two people: Dr. Lynn Jorde, who specializes in population genetics, and Dr. Hilary Coon, who is listed as a statistical geneticist and genetic epidemiologist. Data will be entered by 3 half-time employees. Standard data checking and validation procedures will be instituted. The planned data manage t procedures will enable the investigative team to merge data ac s projects. The diagnostic core will provide centralized ascertainment and diagnostic services for all projects of the program project. In addition, it will organize the scheduling of subjects, controls, and their families so that participation in the various components of the program project will be well coordinated and time-efficient for both participating families and staff. It will serve as the central location for storage and maintenance of-raw data. It will also serve as a source of referral for any autistic subjects, control subjects, or parents who are identified during the program project study as needing medical or psychiatric evaluation and/or treatment.

The genetic transmission of eye tracking abnormalities will be studied. Also, the P50 auditory evoked potential (an EEG abnormality), phenotypes abnormal to schizophrenics, and about 50% of the unaffected relatives will be studied. These phenotypes will be used to determine a quantitative liability to schizophrenia, which will be used as a sensitive phenotype to search for predisposing gene(s) for schizophrenia. Linkage and linkage disequilibrium will be employed using data from: 1) moderately sized Utah pediagrees; 2) a fully ascertained sample form the island population of Palau, Micronesia, and 3) schizophrenics and their first degree relatives form the isolated inbred populations of Dahestan, Russia. This is a "core lab only" protocol.

The purpose of this project is to search for susceptibility genes for autism using intermediate phenotypes. We will test 8-10 large extended Utah pedigrees, and 50 smaller multiplex pedigrees (2-4 affected cases). Seven large pedigrees and 57 smaller multiplex pedigrees have already been identified, and we have preliminary evidence that extensive phenotyping in these pedigrees will be feasible. Pedigrees will be typed with microsatellite markers and the Affymetrix 10k SNP chip. The phenotypes we have chosen to measure on these pedigrees show evidence of heritable variation. New pedigrees will be identified through the ascertainment of 800 trio families (86 trios are already complete and 214 more waiting to be scheduled at Utah, and 100 will come from our collaborating site in Colorado). The Utah Population DataBase, used to identify the first extended pedigrees, will be used to find new extended/multiplex pedigrees. Blood for DNA and selected phenotypes is collected on the trio families as they enter the study. This up-front DNA and phenotype collection provides an immediate resource not only for pedigree identification but also for later follow-up of positive linkage results and for the study of the best positional candidate genes found through the linkage analyses. The measurement of intermediate phenotypes on the trios as well as the pedigrees will allow us to stratify our sample to a degree that has not before been possible in large-scale genetic studies of autism. University of Utah molecular genetics labs will give us world-class expertise in genotyping, fine mapping, candidate gene selection, sequencing, and interpretation of results. Our specific aims are: 1) Ascertain 550 more trios for a total of 800 to find new multiplex/extended pedigrees, to use for linkage follow-up, and to study 2 positional candidate genes once genome scanning (Aim 2) is complete. 2) Perform additional specific phenotyping in 8-10 extended autism families and 50 smaller multiplex families. Test intermediate phenotypes for heritable variation. Use a whole genome scan to search for linkage using intermediate phenotypes. Fine map regions of interest. Study the two best positional candidate genes using pedigrees and trios.

DESCRIPTION (provided by applicant): Our unique resource of extended pedigrees with autism spectrum disorder (ASD) will allow us to make important contributions to genetic studies of ASD. We will sequence family members from the most informative pedigrees to study genetic variation contributing to ASD and related phenotypes. We will work to discover new variation, and also use the resource to characterize variants in conjunction with existing whole exome data available through our collaborations. We will test findings in up to 10 other families available from the Autism Genome Project (AGP) network of collaborators. We will also make the resource available to the broader scientific community. Extended families offer an excellent opportunity to identify and study genetic variation, giving a complementary approach to ongoing studies of simplex and small multiplex families. The current collection of families represents some of the largest pedigrees with ASD in the world. We have already detected significant linkage evidence in some of these families with clinical diagnosis and also with related phenotypes, including gender; Full Scale IQ; discrepancy between verbal and nonverbal IQ; language delay, Insistence on Sameness, Repetitive Sensory-Motor Actions (RSMA), overall clinical severity, and regressive onset (all derived from the ADI); head circumference; and the Broader Autism Phenotype. Sequence data in these extended families will result in highly accurate and extensive genetic information. We will identify familial variation in these data, and predict potentially deleterious variants using new informatics approaches. We will refine information about risk by comparing to ongoing sequence projects. We will also use the ongoing sequence projects to help prioritize the familial variant discovery, and choose the best for replication efforts in other AGP families. Finally, we will investigate sequence variants found by simplex/small family sequencing to determine specificity and penetrance in our extended families. Our proposed project will benefit from the continued collaboration of excellent molecular, analytic, and clinical expertise in the Autism Genome Project to enable the most effective use of this unique resource.

DESCRIPTION (provided by applicant): Suicide is a significant health concern. There are over 33,000 suicide deaths per year in the United States, accounting for 1.3% of all fatalities (WISQARS, 2005), and about 2% of deaths worldwide (World Health Organization, 2000). Aggregated data across multiple large studies has produced heritability estimates of completed suicide of 45%. The Rocky Mountain States have much higher age-adjusted suicide rates, and Utah is consistently in the top ten. In Utah, suicide is the leading cause of death for males between the ages of 15 and 54. Our project will use a large DNA resource already collected from decedents through a long-term collaboration with the centralized Utah State Office of the Medical Examiner (OME). Records of >2,000 decedents with DNA were linked to the Utah Population DataBase (UPDB), a computerized genealogy database that includes medical data, demographic information, and genealogical data for over 6.5 million individuals. Using the UPDB, we identified 27 high risk families containing ~150 suicide decedents with DNA. As a rare condition (1-2/10,000 per year), aggregation of suicide completion in high-risk pedigrees represents a unique resource to study risk factors. We will use the genealogical, demographic, and medical data in the UPDB to identify and focus on the most compelling of these high-risk suicide pedigrees; those that contain both a significant excess of suicide completion and that exhibit the most discriminating characteristics compared to non-familial suicide. By using these phenotypic comparisons to choose the most unique high-risk pedigrees, we will increase homogeneity and strengthen our ability to isolate genetic variants related to suicide risk. These discriminating phenotypes will also identify non-genetic factors associated with high familial risk that can foster other epidemiological studies, and can facilitate future gene x environment analyses. We currently have in hand a large resource of DNA and phenotype information from ~2500 additional Utah suicide decedents. This sample will grow to over 4000 DNAs by the end of the study, the largest population-based sample of DNA from suicide decedents ever collected. We propose to focus on unusual high-risk suicide pedigrees with increased likelihood for more penetrant rare genetic variation, followed by confirmation and follow-up analyses in large cohorts of Utah decedents and publicly-available psychiatric data sets. The detection of genetic variants associated with suicide could shed light on biological pathways leading to suicide risk in the population, or in association with specific disorders. We have chosen state-of-the-art analytical methods, and have assembled a team of experts (analytic, phenotypic, and molecular) to explore these unique data resources to identify genetic risk factors for suicide. The detection of rare variants associated with suicide could shed light on biological pathways leading to suicide risk in the population, or in association with specific disorders.

ABSTRACT Suicide is the 10th leading cause of death, with over 47,000 preventable deaths per year in the U.S. alone. The rate of suicide death across the U.S. has risen by 33% over the past two decades. In spite of this dramatic public health crisis, suicide research lags far behind other major health conditions due to the perception that risk factors are too complex and uncontrollable for study. Importantly, while environment has undeniable impact, evidence suggests that genetic factors play a major role in suicide death. While the study of genetic risks is therefore promising, most studies of suicide genetics have focused on the much more common traits of suicidal thoughts and behaviors. This strategy has allowed other research groups to acquire sufficiently statistically-powered samples. However, suicidal behaviors can be difficult to quantify, and represent individuals with a wide range of risk for later suicide death. Using the unique resources available to the Utah Suicide Genetic Risk Study (USGRS), we are able to study the genetic risks of the unambiguous, high-impact health outcome of suicide death directly. The USGRS currently has DNA from >6,000 population-ascertained suicide deaths; this resource grows by ~650 cases per year through an unprecedented two-decade collaboration with the Utah Department of Health?s centralized Office of the Medical Examiner (OME). We have completed whole genome sequence (WGS) data on a subset of 281 of the Utah suicide deaths selected for high genetic risk. We have Illumina PsychArray data on these cases and additional Utah suicides (total N=4,382). All cases are linked to the Utah Population Database (UPDB), a statewide resource that includes demographic data and comprehensive medical records. The UPDB phenotypic data also includes unique information on familial risk far exceeding that of other data resources through genealogical records that go back to the 1700s. To truly understand risk of suicide death and to implement highly effective interventions that provide appropriate, targeted services to those most likely to die, we must understand the risks specifically associated with suicide deaths. This proposal focuses on the identification, validation, characterization, and replication of variants with high functional impact that implicate genes and gene pathways important for risk of suicide death. From our WGS data, we have already detected high-impact structural variants (SVs) and single nucleotide variants (SNVs) showing genome-wide significant gene pathway enrichment and protein-protein interactions. These pathways are also supported by genes implicated in our genome-wide association analyses of 3,413 Utah suicide deaths, suggesting overlap at the functional level of rare and common risk variation. Extensive familial risk data and large sample size will allow us to select an additional subset of 760 suicides with enhanced genetic risk to replicate and extend our current findings, setting the stage for identification of high-risk individuals, and for development of targeted interventions.

ABSTRACT Suicide is a leading cause of death that continues to increase, with over 47,000 preventable suicide deaths per year in the U.S. Although we have made great strides in using electronic health records (EHR) and other factors to predict suicidal ideation and behavior, our ability to reliably predict suicide death is close to zero. From a healthcare standpoint, predicting suicide deaths is tricky. We know that the incidence of suicide behaviors is far more common (~4%-5% per year) compared to suicide death (~0.01%-0.02% per year). Essentially, only a small fraction of those who engage in suicidal behaviors will go on to die by suicide. Knowledge of who these highest risk individuals are is critically important in directing prevention efforts and development of future targeted interventions. In addition, well over half of suicide deaths occur with no prior attempts, even accounting for lack of documentation of attempts in diagnostic codes. These ?out of the blue? cases suggest one or more high-risk groups even more elusive to accurate prediction and prevention. Including genetic data of suicide deaths may offer substantial predictive improvement; genetic factors account for close to 50% of the risk of suicide death. Using the extensive genetic data, statewide longitudinal EHR resources, demographic, and familial data available to the Utah Suicide Genetic Risk Study (USGRS), we are uniquely poised to address this critical knowledge gap. Our primary focus will be to use machine learning methods develop models that predict suicide deaths. In addition, our large suicide death research resource will also allow us to model differences of suicide deaths with vs. without prior attempts. Of the ~9,000 Utah suicide deaths with demographics and environmental data, familial data, and 2 decades of longitudinal EHR data, the USGRS also currently has DNA from >6,000, which will increase to ~10,000 during the award period. Genome- wide molecular data is in hand for over 5,000 of these Utah suicides, allowing for tests of association of suicide subtypes identified using EHR data with ?genetic phenotypes? represented by polygenic risk scores. The USGRS also has demographics, familial data, and longitudinal EHR data from 5 age/sex- matched Utah population controls for each suicide death, allowing for comparisons of non-lethal attempts to suicide deaths. In addition, we will collaborate with colleagues at the Mount Sinai School of Medicine, who are currently developing EHR and polygenic risk models to study substance use disorder, anxiety, and major depressive disorder in 37,510 participants in the Mount Sinai BioMe Biorepository. They will expand this work to include suicidality to provide an additional resource of suicide attempt for our model development and testing. We will additionally study polygenic risk scores associated with suicide death vs. attempt using our resources, Mount Sinai BioMe, and a collaboration with Vanderbilt University for access to their Biobank and to suicide attempts in the UK Biobank.. Independent validation will be possible through genotyping of new Utah suicides collected throughout the project, with additional comparisons to attempt cases in large datasets available through the PsychEMERGE consortium.