American Journal of Medical Genetics Part B: Neuropsychiatric Genetics最新文献

Nicotine addiction, like many other forms of addiction, is a multifaceted behavior influenced by both genetic and environmental factors. Despite the well-established role of these influences, relatively few studies have simultaneously examined the interaction between genetic predispositions and environmental variables in the context of substance addiction. This study investigated the role of specific genetic variants within nicotinic acetylcholine receptor (nAChR) and cytochrome P450 genes in relation to nicotine and other substance addictions, as well as environmental factors. From a total of 66,936 genetic variants, a Polygenic Risk Score (PRS) was computed using selected SNPs within the nAChR and cytochrome gene families. Structural Equation Modeling (SEM) was employed to examine the relationships between PRS, the Wisconsin Index of Smoking Dependence Motives (WISDM), socioeconomic status (SES), and various forms of substance addiction, using a dataset comprising 2969 individuals (1786 females, 1168 males). The results demonstrate that substance addiction is shaped by a complex interplay of genetic, environmental, and psychological factors. The PRS significantly predicted the Fagerström Test for Nicotine Dependence (FTND) and DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) based nicotine dependence. PRS had smaller, yet significant, effects on alcohol and marijuana dependence and WISDM. However, it showed no meaningful relationship with SES or other addictions. In contrast, SES strongly influenced smoking motives (WISDM), which in turn significantly predicted multiple types of substance addiction, positioning WISDM as a key mediator. Additionally, alcohol dependence emerged as a central factor, showing cascading effects on nicotine, marijuana, and cocaine addiction. Overall, while genetic predisposition plays a notable role, especially in nicotine-related outcomes, social context and motivational factors exert broader and more substantial effects across various addiction pathways, underscoring the need for multifaceted intervention strategies.

A core challenge in the genetic analysis of major depressive disorder (MDD) is how to recruit large numbers of stringently diagnosed cases with sufficient information to explore the interplay between genetic and environmental risk factors and evaluate genetic influences on putative MD subtypes and key clinical features. Currently, most genome-wide association studies of MDD rely on self-administered questionnaires or electronic health records, both of which are limited in diagnostic accuracy and introduce systematic, heritable biases that confound the interpretation of genetic analyses. Here, we describe how to address this problem through a combination of targeted ascertainment and in-depth phenotyping by clinical interview. We increase the homogeneity of the sample, reducing the number of cases needed to detect genetic signals, by recruiting only women with recurrent depressive episodes, ascertained through hospitals. Structured interviews capture detailed information on the known and putative risk factors for the disorder. We trained 347 interviewers working at 47 participating hospitals across South Korea and recruited 5704 cases and 4995 screened controls over 4 years toward a total target sample of 10,000 cases and 10,000 controls. We met and overcame a series of logistic challenges, including restrictions due to COVID-19 and an ongoing medical crisis. We confirmed that our cases have recurrent, severe MDD and are suitable to explore the causes of recurrent episodes of disturbances of sleep and appetite, suicidality, guilty ruminations, anhedonia, and low mood. Our study design provides deeply phenotyped cases and screened controls at scale and can be adapted for deployment in other countries to yield cohorts for the genetic analysis of MDD.

Genomic instability is a prominent hallmark of cancer, and Long non-coding RNAs (LncRNAs) have been implicated in cancer biology. This study aimed to develop a prognostic model for glioma by focusing on genomic instability-associated lncRNAs (GILnc). A computational framework was implemented to identify GILnc, followed by immuno-scoring and immune cell infiltration analyses using the ESTIMATE and CIBERSORT algorithms. Putative drugs and downstream target proteins were predicted using the Connectivity Map (Cmap) and STITCH database. Cox regression analysis was employed for prognostic modeling, and a competitive endogenous RNA (ceRNA) network was constructed using the miRcode database, miRDB, miRTarBase, and TargetScan. A set of GILnc was successfully identified in glioma, showing a significant correlation with prognosis. Based on GILnc, a 16-gene prognostic model (GILncSig) was developed to assess risk scores for glioma patients. A clinical-accessible and high-performance nomogram was formulated by integrating independent clinical parameters. Furthermore, several putative drugs with potential anti-tumor effects in genomically unstable glioma were identified. This study contributes novel insights into GILnc in gliomas and presents the GILncSig prognostic model with robust independent predictive capabilities. These findings offer valuable implications for personalized treatment strategies in glioma. The putative drug predictions provide promising avenues for therapeutic intervention in glioma patients with genomic instability. Overall, our research advances the understanding of lncRNA involvement in cancer genomic instability and establishes a basis for future investigations in this field.

Problem behaviors (PB) commonly co-occur in individuals with neurodevelopmental genetic syndromes (NGDs) and increase the risk of injury to oneself and others. Despite the prevalence and clinical impact of these behaviors, knowledge regarding the psychological risk markers for PB among individuals with NGDs is currently lacking. To fill this gap, we explored the relative contributions of key developmental (age, speech production) and clinical (emotion regulation, anxiety, sensory sensitivity, social communication) characteristics as predictors of unique PB subdomains in a sample of 255 individuals with NGDs (M_age = 14.16; SD_age = 10.45; 51.0% male). Emotion dysregulation was a strong predictor of all subdomains of PB and the strongest predictor of aggression, conduct problems, and property destruction, after controlling for speech level and other clinical features. Lower social communication was the strongest predictor of elopement and self-injury. Distinct facets of anxiety showed unique patterns of associations with PB subdomains, such that higher physiological anxiety was significantly associated with elopement and aggression, while lower worry was associated with elopement. Speech level was a significant negative predictor of conduct problems and elopement. Future research is needed to replicate reported findings and to understand the predictors, maintaining factors, and complex interplay between these factors in the occurrence of PB subdomains among individuals with NGDs.

Schizophrenia is a complex psychiatric disorder with an estimated heritability of 80%. SETD1A, a gene encoding a histone methyltransferase critical for transcriptional regulation, has been identified as a significant risk factor for schizophrenia. Loss-of-function mutations in SETD1A confer up to a 35-fold increased risk, implicating its role in neurodevelopment and synaptic plasticity. Using data from the UK Biobank cohort (468,998 participants), we investigated the association of SETD1A variants with schizophrenia, obesity, and hypertension. Schizophrenia cases were identified using ICD-10 codes, while obesity and hypertension were assessed using specific data fields. Genome-wide association analysis was performed using PLINK, and statistical analyses utilized SPSS v26. Logistic regression assessed the impact of the SETD1A intron variant (rs11150601) alongside age, obesity, and hypertension on schizophrenia risk. Among 1063 individuals diagnosed with schizophrenia, obesity (p < 0.001) and hypertension (p < 0.001) were significantly more prevalent. The rs11150601 GG genotype was associated with an increased risk of schizophrenia in women (OR 1.6, p < 0.001) but not in men. Logistic regression revealed that obesity, hypertension, and age were independent risk factors for schizophrenia in women. SETD1A genotype exerted a significant sex-specific effect, highlighting its potential role in the biological mechanisms underlying schizophrenia. Our findings emphasize the role of SETD1A in the genetic architecture of schizophrenia and its comorbidities, particularly in women. The sex-specific effects of SETD1A variants underscore the importance of incorporating biological sex into studies of psychiatric genetics. Further research is warranted to elucidate the mechanisms by which SETD1A influences neurodevelopment and identify therapeutic strategies targeting its epigenetic functions.

This study aims to determine the causal relationship between gut microbiota (GM) and Non-Suicidal Self-Injury (NSSI) using a two-sample Mendelian Randomization (MR) approach. By identifying Single Nucleotide Polymorphisms (SNPs) linked to both GM and NSSI, we explore bidirectional causal effects to uncover potential therapeutic pathways. A bidirectional MR analysis was conducted using GWAS data. SNPs associated with GM were used as instrumental variables (IVs) to assess the causal impact of GM on NSSI and vice versa. Forward MR analysis applied Inverse Variance Weighted (IVW) and MR-robust adjusted profile score (MR-RAPS) methods to address weak IVs. Sensitivity analyses, including MR-Egger regression, weighted median, and weighted mode methods, were employed to ensure robustness and minimize bias. Reverse MR analysis evaluated the influence of NSSI on GM. Additional tests, such as heterogeneity and leave-one-out analyses, were used for result validation. All analyses were performed using R software (v4.3.2) and the “TwoSampleMR” package. Our analysis identified significant associations between GM and NSSI. In the forward MR analysis, 38 GM taxa at the genus level were linked to NSSI, including Dorea (OR = 1.462, p = 0.023) and Escherichia Shigella (OR = 0.731, p = 0.035), which impacted hospital treatment needs. Notable taxa like Lachnospiraceae UCG001 (OR = 0.755, p = 0.012) and Paraprevotella (OR = 1.229, p = 0.022) were associated with self-injurious behavior. Reverse MR identified 10 significant associations, including Prevotella9 (p = 0.001) linked to inflammation and Faecalibacterium (p = 0.014) with protective effects. Sensitivity analyses confirmed the robustness of findings, with no pleiotropy or bias detected. This study establishes a complex relationship between GM and NSSI, revealing both risk-enhancing and protective associations. These findings suggest that GM may influence NSSI behaviors and could serve as a target for future therapeutic interventions. Further research is needed to explore underlying mechanisms and validate these results.

Repetitive transcranial magnetic stimulation (rTMS) has been a common technique used to stimulate neuromodulatory changes, which can induce therapeutic benefits. However, the effects are variable, possibly resulting from personal factors such as genetic contribution and heterogeneous methodologies. The brain-derived neurotrophic factor (BDNF) plays a crucial role in rTMS-dependent neuroplasticity, but it is unclear how BDNF genotypes interact with the stimulation parameters of rTMS and contribute to the variable responses to neuromodulation. This systematic review aimed to (1) examine how BDNF polymorphisms are related to the after-effects of rTMS in humans and (2) investigate the association between BDNF polymorphism and rTMS stimulation parameters as contributing factors to the response to rTMS. Of the 36 studies included in this systematic review, 35 studies had at least one domain of high or unclear risk of bias; 53% of the studies in healthy individuals showed differences in TMS-derived or behavioral measures between Val/Val homozygotes and Met allele carriers. In stroke, neuromodulatory effects on corticospinal excitability and motor deficits were more evident in Val/Val homozygotes than Met allele carriers. Similarly, in depression, Val/Val homozygotes demonstrated more symptom improvement compared with Met allele carriers following rTMS. We discuss methodological considerations and provide suggestions for future research.