Testing associations between human anxiety and genes previously implicated by mouse anxiety models

Albuminuria and Mental Illness Risk: Results From National Health and Nutrition Examination Survey 2005-2018 and Mendelian Randomization Analyses

BACKGROUND

Recent evidence suggests a link between albuminuria and mental illness. However, whether this association is stable, and its specific mechanisms remain unclear.

METHODS

The cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Weighted multivariable-adjusted logistic regression, subgroup analysis, interaction tests, and restricted cubic spline (RCS) were conducted to assess the correlation between albuminuria and the risk of mental illness (depression). Subsequently, two-sample Mendelian randomization analyses were performed to investigate the relationship between albuminuria and various mental illnesses (anxiety disorder, persistent delusional disorder, schizophrenia, schizotypal personality disorder, panic disorder, post-traumatic stress disorder [PTSD], obsessive-compulsive disorder, bipolar I disorder, bipolar II disorder, depression, autism, social anxiety disorder).

RESULTS

Albuminuria was consistently found to have a significant association with the risk of depression, regardless of its classification as a continuous or outcome variable. A positive correlation was found between albuminuria and depression in different age groups, gender, race, education attainment, and those with hypertension, coronary heart disease, and diabetes. Further, there is a positive correlation between albuminuria and the occurrence of schizophrenia and persistent delusional disorder.

CONCLUSION

There is a close association between albuminuria and mental illness, with albuminuria being a risk factor for schizophrenia and persistent delusional disorder. Further research is needed to establish the specific connections.

Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the All of Us Research Program

Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the All of Us Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.07 to 24.6 times more phenotypic variance across 11 psychiatric conditions. Meanwhile, many disorder-specific PRSs retained independent but smaller contributions, highlighting the complementary nature of shared and disorder-specific genetic risk. While alternative multi-factor models improved model fit, the CPG PRS provided comparable or superior predictive performance across most disorders, including overall comorbidity burden. Cross-ancestry analyses however revealed notable limitations of European-centric GWAS datasets for other populations due to ancestral differences in genetic architecture. These findings underscore the potential value of transdiagnostic PRSs for psychiatric genetics while highlighting the need for more equitable genetic risk models.

Computer reconstruction of gene networks controlling anxiety levels in humans and laboratory mice

Anxiety is a normotypic human condition, and like any other emotion has an adaptive value. But excessively high or low anxiety has negative consequences for adaptation, which primarily determines the importance of studying these two extreme conditions. At the same time, it is known that the perception of aversive stimuli associated with anxiety leads to changes in the activity of the brain's cingulate cortex. The advantage of animals as models in studying the genetic bases of anxiety in humans is in the ability to subtly control the external conditions of formation of a certain state, the availability of brain tissues, and the ability to create and study transgenic models, including through the use of differentially expressed genes of small laboratory animals from the family Muridae with low and high anxiety. Within the framework of the translational approach, a three-domain potential gene network, which is associated with generalized anxiety in humans, was reconstructed using mouse models with different levels of anxiety by automatically analyzing the texts of scientific articles. One domain is associated with reduced anxiety in humans, the second with increased anxiety, and the third is a dispatcher who activates one of the two domains depending on the status of the organism (genetic, epigenetic, physiological). Stages of work: (I) A list of genes expressed in the cingulate cortex of the wild type CD-1 mouse line from the NCBI GEO database (experiment GSE29014). Using the tools of this database, differences in gene expression levels were revealed in groups of mice with low and high (relatively normal) anxiety. (II) Search for orthologs of DEG in humans and mice associated with anxiety in the OMA Orthology database. (III) Computer reconstruction using the ANDSystem cognitive system based on (a) human orthologous genes from stage (III), (b) human genes from the MalaCards database associated with human anxiety. The proven methods of the translational approach for the reconstruction of gene networks for behavior regulation can be used to identify molecular genetic markers of human personality traits, propensity to psychopathology.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Disentangling differing relationships between internalizing disorders and alcohol use

Both internalizing disorders and alcohol use have dramatic, wide-spread implications for global health. Previous work has established common phenotypic comorbidity among these disorders, as well as shared genetic variation underlying them both. We used genomic structural equation modeling to investigate the shared genetics of internalizing, externalizing, and alcohol use traits, as well as to explore whether specific domains of internalizing symptoms mediate the contrasting relationships with problematic alcohol use compared to alcohol consumption. We also examined patterns of genetic correlations between similar traits within additional Finnish and East Asian ancestry groups. When the shared genetic influence of externalizing psychopathology was accounted for, the genetic effect of internalizing traits on alcohol use was reduced, suggesting the important role of common genetic factors underlying multiple psychiatric disorders and their genetic influences on comorbidity of internalizing and alcohol use traits. Individual internalizing domains had contrasting effects on frequency of alcohol consumption, which demonstrate the complex system of pleiotropy that exists, even within similar disorders, and can be missed when evaluating only relationships among formal diagnoses. Future work must consider the broad effects of shared psychopathology along with the fine-scale effects of heterogeneity within disorders to more fully understand the biology underlying complex traits.