A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes

Generalized structured component analysis (GSCA) is a multivariate method for examining theory-driven relationships between variables including components. GSCA can provide the deterministic component score for each individual once model parameters are estimated. As the traditional GSCA always standardizes all indicators and components, however, it could not utilize information on the indicators' scale in parameter estimation. Consequently, its component scores could just show the relative standing of each individual for a component, rather than the individual's absolute standing in terms of the original indicators' measurement scales. In the paper, we propose a new version of GSCA, named convex GSCA, which can produce a new type of unstandardized components, termed convex components, which can be intuitively interpreted in terms of the original indicators' scales. We investigate the empirical performance of the proposed method through the analyses of simulated and real data.

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

OBJECTIVE

The primary objective of this study is to address the challenge posed by the increasing number of variants of unknown clinical significance (VUS) within the GABRD gene, which encodes the δ subunit of γ-Aminobutyric acid type A receptors. The focus is on predicting the most pathogenic GABRD VUS to enhance precision medicine and improve our understanding of relevant pathophysiology.

METHODS

The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. Initially, separate algorithms based on sequence homology are utilized to assess this variant set. Subsequently, consensus variants predicted as pathogenic undergo further evaluation through a web server employing an algorithm based on structural homology. The resulting 11 variants are then validated using in silico tools that assess variant effects based on genetic and molecular data. The evaluation includes consideration of disease association and protein stability due to amino acid substitutions.

RESULTS

The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. These variants are evolutionarily conserved, mapped onto the extracellular domain of the δ subunit, and associated with idiopathic generalized epilepsy.

CONCLUSIONS

The findings suggest structural or functional consequences that lead to pathogenicity, offering valuable insights for wet-lab experimentation. Besides, the findings contribute to the validation of clinically significant genetic variants in the GABRD gene, which is critical for epilepsy precision medicine.

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches - the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM - under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.

Exposure to black carbon is associated with symptoms of depression: A retrospective cohort study in college students

BACKGROUND

Previous studies have revealed a significant association of fine particulate matter (PM_2.5) with emotional disorders. However, as a crucial component of PM_2.5, little is known about the potential effect of exposure to black carbon (BC) on the symptoms of depression and anxiety.

OBJECTIVES

To explore the associations of long-term exposure to BC during the past six years with the current symptoms of depression and anxiety in a group of incoming college students.

METHODS

This was a retrospective cohort study of incoming students in five universities of China. Symptoms of depression and anxiety during the past two weeks were measured by the Patient Health Questionnaire-2 (PHQ-2) and Generalized Anxiety Disorder Scale-2 (GAD-2), respectively. Levels of BC and other environmental factors during 2013 ∼ 2018 (six years prior to the recruitment) was obtained from public repositories and linked to individual data by home addresses. Averagely daily dose of BC exposure was estimated according to the respiratory rate. Demographic and behavioral variables were collected through a questionnaire. The associations of BC with symptoms of depression and anxiety were estimated by mixed linear models adjusting for socioeconomic and behavioral characteristics, and the principal components of multiple environmental exposures. Subgroup analysis was conducted to assess the effect modification by covariates. Overall effect of environmental mixture was evaluated by weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR).

RESULTS

A total of 20,079 participants was included in the current study. After adjustment for covariates, long-term BC exposure was significantly associated with symptoms of depression (β = 0.17, P < 0.001) but not anxiety (β = 0.07, P = 0.125). Effect modification by sex and parental educational level: BC was correlated with depressive symptoms in women (β = 0.23, P < 0.001) but not in men (β = 0.04, P = 0.581), and higher educational level was associated with decreased effect sizes of BC. Sensitivity analysis showed that the acute and short-term effects of BC on depression was consistent with its long-term exposure (β varied from 0.18 to 0.20). WQS identified BC as the primary pollutant in association with symptoms of depression but not anxiety. BKMR identified no significant interaction between BC and other exposures.

CONCLUSION

Exposure to BC is associated with symptoms of depression but not anxiety in college students, and the relationship is modified by sex and education.