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

The RYR3 gene encodes a brain-type ryanodine receptor that functions to release calcium from intracellular storage and plays an essential role in calcium signaling. The associations between RYR3 variants and brain disorders remain unknown. We performed whole-exome sequencing in patients with idiopathic (non-lesional) partial epilepsy of unknown etiology. One de novo missense and six biallelic missense RYR3 variants were identified in seven unrelated cases. These variants had no or extremely low allele frequencies in the general population and were predicted to alter hydrogen bonds/decrease protein stability. Patients presented with partial seizures or secondarily generalized tonic-clonic seizures. All patients were seizure-free with/without anti-seizure treatment. Four showed antecedent febrile seizures, a typical susceptibility disorder that is related to the precipitating factor of fever. The genetic dependence nature (GDN) of RYR3, which is defined as the distinct impact of the absence of a gene on normal life, is "obligatory" (causing disease phenotypes). Complete abolishing of RYR3 results in abnormal phenotypes instead of lethality, whereas partial/mild impairment (usually more common) is associated with mild disease or increased susceptibility to disease, consistent with our findings. RYR3 is therefore potentially a candidate disease gene or susceptibility gene for idiopathic partial epilepsy.

People with mosaicism for trisomy 21 have been shown to exhibit many of the same phenotypic traits present in people with non-mosaic Down syndrome, but with varying symptom severity. However, the behavioral phenotype of people with mosaic Down syndrome (mDS) has not been well characterized. This study aimed to examine the prevalence of self-report and caregiver-report symptoms of depression and anxiety among a sample of 62 participants with mDS aged 12-46 and assess their association with the percentage of trisomy 21 in blood and/or buccal mucosa cells. The overall MANCOVA revealed a significant effect of trisomy on the set of internalizing scales, controlling for age and gender (p = 0.038, partial eta² = 0.22). However, follow-up univariate analyses showed that the initial significant effect of trisomy on fear (p = 0.049, partial eta² = 0.08) did not survive correction for multiple comparisons (adjusted p = 0.300). No other effects were significant. This study highlights the high occurrence of depression and anxiety symptoms in individuals with mDS and the need for routine assessment to optimize their care. It also demonstrates the ability of people with mDS to complete these evaluations, thereby supporting their inclusion in research studies/clinical trials.

BackgroundInsomnia is a common neurological disorder that exhibits connections with the gut microbiota; however, the exact causal relationship remains unclear. MethodsWe conducted a Mendelian randomization (MR) study to systematically evaluate the causal effects of genus-level gut microbiota on insomnia risk in individuals of European ancestry. Summary-level datasets on gut microbiota were sourced from the genome-wide association study (GWAS) of MiBioGen, while datasets on insomnia were obtained from the GWAS of Neale Lab and FinnGen. The primary analytical approach used was the inverse-variance weighted (IVW) method, supplemented by MR-Egger, maximum likelihood, MR-robust adjusted profile score, and weighted median. Sensitivity analyses were conducted to ensure robustness. ResultsThe microbial taxa Enterorhabdus, Family XIII AD3011 group, Paraprevotella, and Lachnospiraceae UCG004 were associated with an increased risk of insomnia, whereas Coprococcus1, Coprobacter, Desulfovibrio, Flavonifractor, Olsenella, Odoribacter, and Oscillibacter were linked to a decreased risk. Regarding the insomnia phenotype characterized by trouble falling asleep, the microbial taxon Eisenbergiella was correlated with an increased risk, while Haemophilus and the Eubacterium brachy group were associated with a reduced risk. Furthermore, for the insomnia phenotype characterized by waking too early, the microbial taxa Family XIII UCG001, Lachnospiraceae FCS020 group, and Olsenella were linked to an increased risk, whereas the Eubacterium brachy group and Victivallis were associated with a lower risk. The results remained robust across all sensitivity analyses. ConclusionOur MR study identified multiple genus-level gut microbial taxa that may exhibit potential causal effects on insomnia from a genetic perspective. These findings provide evidence supporting the theory of the microbiota-gut-brain axis and offer new insights into potential prevention and therapeutic targets for insomnia.

Externalizing traits and behaviors are broadly defined by impairments in self-regulation and impulse control that typically begin in childhood and adolescence. Externalizing behaviors, traits, and symptoms span a range of traditional psychiatric diagnostic categories. In this study, we sought to generate an algorithm that could reliably identify transdiagnostic childhood-onset externalizing cases and controls within a university hospital electronic health record (EHR) database. Within the Vanderbilt University Medical Center (VUMC) EHR, our algorithm identified cases with a clinician-validated positive predictive value of 90% and controls with a negative predictive value of 88%. In individuals of genetically defined European ancestry (CEU-clustered; N_case = 487, N_control = 5638), case status was significantly associated with psychiatric comorbidity and with elevated externalizing polygenic scores (OR: 1.20; 95% CI: 1.09-1.33; p = 1.14 × 10^-3; based on published genome-wide association data). To test whether our cohort definitions could be applied to generate novel genetic insights, we examined rare (allele frequency < 0.5%) copy number variation. An association (OR: 9.70; CI: 3.24-29.0) was identified in the CEU-clustered cohort on chromosome 2 (chr2: 45,408,678-45,551,530; duplication), although the statistical strength of this association was modest (p = 0.052). We also examined the role of an externalizing burden score based on the number of externalizing diagnoses present in cases and found similar results to our case-control analysis. This analysis identified several other statistically significant CNV region associations. This study provides a framework for identifying childhood externalizing case-control cohorts within an EHR. Future work should validate this framework within other health systems. A broadly applicable algorithm, like this one, may allow for detection of rare outcomes or outcomes in populations historically excluded from genomic research through meta-analysis of data across health care systems.

The comprehensive genome-wide nature of transcriptome studies in Alzheimer's disease (AD) should provide a reliable description of disease molecular states. However, the genes and molecular systems nominated by transcriptomic studies do not always overlap. Even when results do align, it is not clear if those observations represent true consensus across many studies. A couple of sources of variation have been proposed to explain this variability, including tissue-of-origin and cohort type, but its basis remains uncertain. To address this variability and extract reliable results, we utilized all publicly available blood or brain transcriptomic datasets of AD, comprised of 24 brain studies with 4007 samples from six different brain regions, and eight blood studies with 1566 samples. We identified a consensus of AD-associated genes across brain regions and AD-associated gene-sets across blood and brain, generalizable machine learning and linear scoring classifiers, and significant contributors to biological diversity in AD datasets. While AD-associated genes did not significantly overlap between blood and brain, our findings highlighted 15 dysregulated processes shared across blood and brain in AD. The top five most significantly dysregulated processes were DNA replication, metabolism of proteins, protein localization, cell cycle, and programmed cell death. Conversely, addressing the discord across studies, we found that large-scale gene co-regulation patterns can account for a significant fraction of variability in AD datasets. Overall, this study ranked and characterized a compilation of genes and molecular systems consistently identified across a large assembly of AD transcriptome studies in blood and brain, providing potential candidate biomarkers and therapeutic targets.

Anorexia nervosa (AN) is a psychiatric disorder with an estimated heritability of around 70%. Although the largest meta-analysis of genome-wide association studies on AN identified independent risk-conferring loci for the disorder, the molecular mechanisms underlying the genetic basis of AN remain to be elucidated. To investigate AN, we performed transcriptome profiling in peripheral blood mononuclear cells from 15 AN patients and 15 healthy controls. We validated our mean results in a mouse model of chronic food restriction, which mimics several aspects of AN. In this exploratory study, we identified 673 significantly differentially expressed genes in AN. Among these genes, we identified seven genes previously found to be dysregulated in IPSC-derived neurons from AN individuals and the Vanin-1 (Vnn1) gene, which appears to play an important role in the regulation of several metabolic pathways. We confirmed underexpression of Vnn1, particularly in the liver, in a mouse model of chronic food restriction. These results indicate that quantitative food restriction affects Vnn1 expression, suggesting that this gene may contribute to the anorexic phenotype in the chronic food restriction mouse model as well as in patients with AN. We believe that this report highlights promising candidate genes and gene pathways for AN, and although we did not obtain a significant result in the replication cohort, it identifies Vnn1 as a potential biomarker that may be used as a molecular target to predict and/or to understand AN.

Public stigma and prejudice toward people with psychiatric conditions is highly prevalent and damaging. Explanations for the origins of mental illness can influence attitudes toward people with these conditions. To date, studies exploring the effects of explanations for the origins of mental illness have focused on genetic or environmental explanations, and the impact of evidence-based multifactorial explanations for psychiatric illness on public attitudes remains unknown. Participants were recruited through Amazon Mechanical Turk to watch a 4-min video about the "mental illness jar model"-an evidence-based analogy that explains the complex interactions between genes and environment in the development of mental illness. Participants provided demographic information and completed questions regarding knowledge about the causes of mental illness, and the Prejudice towards People with Mental Illness (PPMI) scale both before and after watching the video. A total of 106 eligible participants completed the study. Watching the video had no significant effect on participants' knowledge about the causes of mental illness (p = 0.06), but there was a significant decrease in prejudicial attitudes toward mental illness (p = 0.0003), the effect size was small (-0.15). The use of this brief video (available at cogastudy.org) is a promising tool to decrease prejudicial attitudes toward mental illness that warrants further study.

Duplications of the Xq28,distal locus have been described in male and female patients with schizophrenia (SCZ) or intellectual disability. The Xq28,distal locus spans eight protein-coding genes (F8, CMC4, MTCP1, BRCC3, VBP1, FUNDC2, CLIC2, and RAB39B) and is flanked by recurrent genomic breakpoints. Thus, the issue of which gene/s at this locus is/are relevant in terms of SCZ pathogenesis remains unclear. The aim of this study was to investigate the contribution of rare and potentially functionally relevant sequence variants within the Xq28,distal locus to SCZ risk using the single-molecule molecular inversion probes (smMIP) method. Targeted sequencing was performed in a cohort of 1935 patients with SCZ and 1905 controls of European ancestry. The consecutive statistical analysis addressed two main areas. On the level of the individual variants, allele counts in the patient and control cohort were systematically compared with a Fisher's exact test: (i) for the entire present study cohort; (ii) for patients and controls separated by sex; and (iii) in combination with data published by the Schizophrenia Exome Meta-Analysis (SCHEMA) consortium. On the gene-wise level, a burden analysis was performed using the X-chromosomal model of the Optimal Unified Sequence Kernel Association Test (SKAT-O), with adjustment for possible sex-specific effects. Targeted sequencing identified a total of 13 rare and potentially functional variants in four patients and 11 controls. However, neither at the level of individual rare and potentially functional variants nor at the level of the eight protein-coding genes at the Xq28,distal locus was a statistically significant enrichment in patients compared to controls observed. Although inconclusive, the present findings represent a step toward improved understanding of the contribution of X-chromosomal risk factors in neuropsychiatric disorder development, which is an underrepresented aspect of genetic studies in this field.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. Participants (n = 714) from the Genome-Based Therapeutic Drugs for Depression study received escitalopram or nortriptyline over 12 weeks. Remission was defined as scoring ≤ 7 on the Hamilton Rating Scale. Predictors included demographic, clinical, and genetic variables (at 0 weeks) and measures of symptom severity (at 0, 2, 4, and 6 weeks). Longitudinal descriptors extracted with growth curves and topological data analysis were used to inform prediction of remission. Repeated assessments produced gradual and drug-specific improvements in predictive performance. By Week 4, models' discrimination in all samples reached levels that might usefully inform treatment decisions (area under the receiver operating curve (AUC) = 0.777 for nortriptyline; AUC = 0.807 for escitalopram; AUC = 0.794 for combined sample). Decisions around switching or modifying treatments for depression can be informed by repeated symptom assessments collected during treatment, but not until 4 weeks after the start of treatment.