The NDE1 genomic locus can affect treatment of psychiatric illness through gene expression changes related to microRNA-484

Genetic studies of familial schizophrenia in Finland have observed significant associations with a group of biologically related genes, DISCI1, NDE1,NDEL1, PDE4B and PDE4D, the 'DISCI network'. Here, we use gene expression and psychoactive medication use data to study their biological consequences and potential treatment implications. Gene expression levels were determined in 64 individuals from 18 families, while prescription medication information has been collected over a 10 -year period for 931 affected individuals. We demonstrate that the NDE1 SNP rs2242549 associates with significant changes in gene expression for 2908 probes (2542 genes), of which 794 probes (719 genes) were replicable. A significant number of the genes altered were predicted targets of microRNA-484 (p = 3.0 x 10(-8)), located on a non -coding exon of NDE1. Variants within the NM. locus also displayed significant genotype by gender interaction to early cessation of psychoactive medications metabolized by CYP2C19. Furthermore, we demonstrate that miR-484 can affect the expression of CYP2C19 in a cell culture system. Thus, variation at the IVDET locus may alter risk of mental illness, in part through modification of miR-484, and such modification alters treatment response to specific psychoactive medications, leading to the potential for use of this locus in targeting treatment.Peer reviewe

SNP Variants at 16p13.11 Clarify the Role of the NDE1/miR-484 Locus in Major Mental Illness in Finland

Peer reviewe

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships

An Exposure-Wide and Mendelian Randomization Approach to Identifying Modifiable Factors for the Prevention of Depression

ObjectiveEfforts to prevent depression, the leading cause of disability worldwide, have focused on a limited number of candidate factors. Using phenotypic and genomic data from over 100,000 UK Biobank participants, the authors sought to systematically screen and validate a wide range of potential modifiable factors for depression.MethodsBaseline data were extracted for 106 modifiable factors, including lifestyle (e.g., exercise, sleep, media, diet), social (e.g., support, engagement), and environmental (e.g., green space, pollution) variables. Incident depression was defined as minimal depressive symptoms at baseline and clinically significant depression at follow-up. At-risk individuals for incident depression were identified by polygenic risk scores or by reported traumatic life events. An exposure-wide association scan was conducted to identify factors associated with incident depression in the full sample and among at-risk individuals. Two-sample Mendelian randomization was then used to validate potentially causal relationships between identified factors and depression.ResultsNumerous factors across social, sleep, media, dietary, and exercise-related domains were prospectively associated with depression, even among at-risk individuals. However, only a subset of factors was supported by Mendelian randomization evidence, including confiding in others (odds ratio=0.76, 95% CI=0.67, 0.86), television watching time (odds ratio=1.09, 95% CI=1.05, 1.13), and daytime napping (odds ratio=1.34, 95% CI=1.17, 1.53).ConclusionsUsing a two-stage approach, this study validates several actionable targets for preventing depression. It also demonstrates that not all factors associated with depression in observational research may translate into robust targets for prevention. A large-scale exposure-wide approach combined with genetically informed methods for causal inference may help prioritize strategies for multimodal prevention in psychiatry

Predicting Barriers to Treatment for Depression in a U.S. National Sample:A Cross-Sectional, Proof-of-Concept Study

OBJECTIVE: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.METHODS: Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.RESULTS: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p&lt;.01), with a balanced accuracy that was also significantly above chance (71%, p&lt;.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p&lt;.05 for all).CONCLUSIONS: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.</p