A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. / Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. / Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. / Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders

Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms

Anxiety disorders are prevalent and disabling yet understudied from a genetic standpoint, compared with other major psychiatric disorders such as bipolar disorder and schizophrenia. The fact that they are more common, diverse and perceived as embedded in normal life may explain this relative oversight. In addition, as for other psychiatric disorders, there are technical challenges related to the identification and validation of candidate genes and peripheral biomarkers. Human studies, particularly genetic ones, are susceptible to the issue of being underpowered, because of genetic heterogeneity, the effect of variable environmental exposure on gene expression, and difficulty of accrual of large, well phenotyped cohorts. Animal model gene expression studies, in a genetically homogeneous and experimentally tractable setting, can avoid artifacts and provide sensitivity of detection. Subsequent translational integration of the animal model datasets with human genetic and gene expression datasets can ensure cross-validatory power and specificity for illness. We have used a pharmacogenomic mouse model (involving treatments with an anxiogenic drug—yohimbine, and an anti-anxiety drug—diazepam) as a discovery engine for identification of anxiety candidate genes as well as potential blood biomarkers. Gene expression changes in key brain regions for anxiety (prefrontal cortex, amygdala and hippocampus) and blood were analyzed using a convergent functional genomics (CFG) approach, which integrates our new data with published human and animal model data, as a translational strategy of cross-matching and prioritizing findings. Our work identifies top candidate genes (such as FOS, GABBR1, NR4A2, DRD1, ADORA2A, QKI, RGS2, PTGDS, HSPA1B, DYNLL2, CCKBR and DBP), brain–blood biomarkers (such as FOS, QKI and HSPA1B), pathways (such as cAMP signaling) and mechanisms for anxiety disorders—notably signal transduction and reactivity to environment, with a prominent role for the hippocampus. Overall, this work complements our previous similar work (on bipolar mood disorders and schizophrenia) conducted over the last decade. It concludes our programmatic first pass mapping of the genomic landscape of the triad of major psychiatric disorder domains using CFG, and permitted us to uncover the significant genetic overlap between anxiety and these other major psychiatric disorders, notably the under-appreciated overlap with schizophrenia. PDE10A, TAC1 and other genes uncovered by our work provide a molecular basis for the frequently observed clinical co-morbidity and interdependence between anxiety and other major psychiatric disorders, and suggest schizo-anxiety as a possible new nosological domain

Interplay Between Childhood Physical Abuse and Familial Risk in the Onset of Psychotic Disorders

Background: Childhood abuse is considered one of the main environmental risk factors for the development of psychotic symptoms and disorders. However, this association could be due to genetic factors influencing exposure to such risky environments or increasing sensitivity to the detrimental impact of abuse. Therefore, using a large epidemiological case-control sample, we explored the interplay between a specific form of childhood abuse and family psychiatric history (a proxy for genetic risk) in the onset of psychosis. Methods: Data were available on 172 first presentation psychosis cases and 246 geographically matched controls from the Aetiology and Ethnicity of Schizophrenia and Other Psychoses study. Information on childhood abuse was obtained retrospectively using the Childhood Experience of Care and Abuse Questionnaire and occurrence of psychotic and affective disorders in first degree relatives with the Family Interview for Genetic Studies. Results: Parental psychosis was more common among psychosis cases than unaffected controls (adjusted OR = 5.96, 95% CI: 2.09–17.01, P = .001). Parental psychosis was also associated with physical abuse from mothers in both cases (OR = 3.64, 95% CI: 1.06–12.51, P = .040) and controls (OR = 10.93, 95% CI: 1.03–115.90, P = .047), indicative of a gene-environment correlation. Nevertheless, adjusting for parental psychosis did not measurably impact on the abuse-psychosis association (adjusted OR = 3.31, 95% CI: 1.22–8.95, P= .018). No interactions were found between familial liability and maternal physical abuse in determining psychosis caseness. Conclusions: This study found no evidence that familial risk accounts for associations between childhood physical abuse and psychotic disorder nor that it substantially increases the odds of psychosis among individuals reporting abuse

Isolated and dynamical horizons and their applications

Over the past three decades, black holes have played an important role in quantum gravity, mathematical physics, numerical relativity and gravitational wave phenomenology. However, conceptual settings and mathematical models used to discuss them have varied considerably from one area to another. Over the last five years a new, quasi-local framework was introduced to analyze diverse facets of black holes in a unified manner. In this framework, evolving black holes are modeled by dynamical horizons and black holes in equilibrium by isolated horizons. We review basic properties of these horizons and summarize applications to mathematical physics, numerical relativity and quantum gravity. This paradigm has led to significant generalizations of several results in black hole physics. Specifically, it has introduced a more physical setting for black hole thermodynamics and for black hole entropy calculations in quantum gravity; suggested a phenomenological model for hairy black holes; provided novel techniques to extract physics from numerical simulations; and led to new laws governing the dynamics of black holes in exact general relativity.Comment: 77 pages, 12 figures. Typos and references correcte

Transcriptome-based polygenic score links depression-related corticolimbic gene expression changes to sex-specific brain morphology and depression risk

Studies in post-mortem human brain tissue have associated major depressive disorder (MDD) with cortical transcriptomic changes, whose potential in vivo impact remains unexplored. To address this translational gap, we recently developed a transcriptome-based polygenic risk score (T-PRS) based on common functional variants capturing ‘depression-like’ shifts in cortical gene expression. Here, we used a non-clinical sample of young adults (n = 482, Duke Neurogenetics Study: 53% women; aged 19.8 ± 1.2 years) to map T-PRS onto brain morphology measures, including Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index, as well as broad MDD risk, indexed by self-reported family history of depression. We conducted side-by-side comparisons with a PRS independently derived from a Psychiatric Genomics Consortium (PGC) MDD GWAS (PGC-PRS), and sought to link T-PRS with diagnosis and symptom severity directly in PGC-MDD participants (n = 29,340, 59% women; 12,923 MDD cases, 16,417 controls). T-PRS was associated with smaller amygdala volume in women (t = −3.478, p = 0.001) and lower prefrontal gyrification across sexes. In men, T-PRS was associated with hypergyrification in temporal and occipital regions. Prefrontal hypogyrification mediated a male-specific indirect link between T-PRS and familial depression (b = 0.005, p = 0.029). PGC-PRS was similarly associated with lower amygdala volume and cortical gyrification; however, both effects were male-specific and hypogyrification emerged in distinct parietal and temporo-occipital regions, unassociated with familial depression. In PGC-MDD, T-PRS did not predict diagnosis (OR = 1.007, 95% CI = [0.997–1.018]) but correlated with symptom severity in men (rho = 0.175, p = 7.957 × 10−4) in one cohort (N = 762, 48% men). Depression-like shifts in cortical gene expression have sex-specific effects on brain morphology and may contribute to broad depression vulnerability in men

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well

Synapsin II Is Involved in the Molecular Pathway of Lithium Treatment in Bipolar Disorder

Bipolar disorder (BD) is a debilitating psychiatric condition with a prevalence of 1–2% in the general population that is characterized by severe episodic shifts in mood ranging from depressive to manic episodes. One of the most common treatments is lithium (Li), with successful response in 30–60% of patients. Synapsin II (SYN2) is a neuronal phosphoprotein that we have previously identified as a possible candidate gene for the etiology of BD and/or response to Li treatment in a genome-wide linkage study focusing on BD patients characterized for excellent response to Li prophylaxis. In the present study we investigated the role of this gene in BD, particularly as it pertains to Li treatment. We investigated the effect of lithium treatment on the expression of SYN2 in lymphoblastoid cell lines from patients characterized as excellent Li-responders, non-responders, as well as non-psychiatric controls. Finally, we sought to determine if Li has a cell-type-specific effect on gene expression in neuronal-derived cell lines. In both in vitro models, we found SYN2 to be modulated by the presence of Li. By focusing on Li-responsive BD we have identified a potential mechanism for Li response in some patients

Theorems on existence and global dynamics for the Einstein equations

This article is a guide to theorems on existence and global dynamics of solutions of the Einstein equations. It draws attention to open questions in the field. The local-in-time Cauchy problem, which is relatively well understood, is surveyed. Global results for solutions with various types of symmetry are discussed. A selection of results from Newtonian theory and special relativity that offer useful comparisons is presented. Treatments of global results in the case of small data and results on constructing spacetimes with prescribed singularity structure or late-time asymptotics are given. A conjectural picture of the asymptotic behaviour of general cosmological solutions of the Einstein equations is built up. Some miscellaneous topics connected with the main theme are collected in a separate section.Comment: Submitted to Living Reviews in Relativity, major update of Living Rev. Rel. 5 (2002)

A genome-wide association study of kynurenic acid in cerebrospinal fluid: implications for psychosis and cognitive impairment in bipolar disorder

Elevated cerebrospinal fluid (CSF) levels of the glia-derived N-methyl-D-aspartic acid receptor antagonist kynurenic acid (KYNA) have consistently been implicated in schizophrenia and bipolar disorder. Here, we conducted a genome-wide association study based on CSF KYNA in bipolar disorder and found support for an association with a common variant within 1p21.3. After replication in an independent cohort, we linked this genetic variant—associated with reduced SNX7 expression—to positive psychotic symptoms and executive function deficits in bipolar disorder. A series of post-mortem brain tissue and in vitro experiments suggested SNX7 downregulation to result in a caspase-8-driven activation of interleukin-1β and a subsequent induction of the brain kynurenine pathway. The current study demonstrates the potential of using biomarkers in genetic studies of psychiatric disorders, and may help to identify novel drug targets in bipolar disorder