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

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.

Sex chromosome aneuploidy (SCA) is a group of conditions characterized by an atypical number of X and/or Y chromosomes, which are associated with various mental health diagnoses (MHD). Individuals with Klinefelter syndrome (KS), Turner syndrome (TS), or Turner mosaicism (TM) were identified using an electronic health record screening algorithm, followed by a review of karyotype data and clinical notes. Each patient with KS was matched with 10 non-SCA males, and each TS or TM participant was matched with 10 non-SCA females. Poisson regression models accounting for matched design and controlling for enrollment duration were used to calculate prevalence ratios (PR) and 95% confidence intervals (CI) for various MHD categories. When 282 KS patients were compared with male referents, the top three differences in prevalence were observed for feeding and eating disorders (PR = 4.2; 95% CI: 1.6, 11.1), disruptive, impulsive-control, and conduct disorders (PR = 3.4; 95% CI: 2.3, 5.0), and suicidal ideation (PR = 3.5; 95% CI 2.0, 5.9). Among 263 TS/TM patients, the corresponding PR estimates relative to female referents were the highest for neurodevelopmental disorders (3.6; 95% CI: 2.7, 4.8) and disruptive, impulsive-control, and conduct disorders (2.0; 95% CI: 1.2, 3.6). When the data were stratified according to age at the time of MHD linkages, the PR estimates varied across the groups, but the 95% CIs largely overlapped. People with SCA carry a greater burden of MHD than comparable persons without SCA. Management of mental health comorbidities is a relatively neglected healthcare priority in this group of patients.

Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. In a study of 10,887 participants across nine cohorts, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes that are genetically correlated with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. On the test subset, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70–0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.