The pathogenicity of variants of uncertain significance in the LRRK2 gene remains underexplored. Investigating the LRRK2 variant spectrum in a large Chinese population cohort can provide deeper insights into its pathogenic mechanisms. This study examined the LRRK2 gene variants in 20,519 Chinese individuals, including 7,562 Parkinson’s disease (PD) patients, 3,077 Essential tremor (ET) patients, and 9880 healthy controls. We conducted a genetic analysis of low-frequency and common non-synonymous variants in the LRRK2 gene across the cohorts. A total of 287 low-frequency non-synonymous LRRK2 variants were identified in the PD and control cohorts. Among these, six reported pathogenic variants (p.R1325Q, p.R1441C, p.R1441H, p.V1447M, p.G2019S, p.I2020T) and three reported likely pathogenic variants (p.R1067Q, p.N1437D, p.R1728H) were enriched in PD cases, with a frequency of 0.71%. In contrast, only one pathogenic variant (p.R1325Q) and one likely pathogenic variant (p.R1067Q) were observed in healthy controls (0.11%), and the ET cohort exhibited similar variant distribution to controls (0.19%). Burden analysis and association analysis revealed novel likely pathogenic variants, including p.A312V, p.M968K, and p.R1320S as candidates. These novel variants were significantly more frequent in PD patients (0.79%) compared to healthy controls (0.20%) or ET patients (0.42%). Additionally, seven common missense variants of LRRK2 were identified, and significant associations with PD for p.A419V, p.R1628P, and p.G2385R were confirmed, but no common variants were linked to ET. This study provides the first comprehensive characterization of the LRRK2 variant spectrum in a large Chinese population, underscoring the pivotal role of LRRK2 in PD pathogenesis but not in ET. These findings advance the understanding of LRRK2 in neurodegenerative disorders and lay a foundation for personalized therapeutic strategies based on genetic profiling.

Many mental illnesses share behavioral, genetic and imaging features. However, common resting-state functional abnormalities across diagnoses remain unclear. This study aimed to investigate shared spontaneous functional alterations in neural circuitry across psychiatric disorders through a transdiagnostic quantitative meta-analysis of published neuroimaging data. A voxel-wise meta-analysis was conducted to investigate amplitude of low-frequency fluctuation (ALFF) differences between patients with major psychiatric disorders (including bipolar disorders, major depressive disorders, schizophrenia, obsessive-compulsive disorder, posttraumatic stress disorder, and anxiety disorders) and healthy controls (HCs). Further analyses explored transcriptional profiles, neurotransmitter systems, and cognitive functions associated with ALFF alterations to uncover potential molecular mechanisms underlying spontaneous neural activity signatures. A total of 254 experiments from 210 ALFF studies (10456 patients; 11014 HCs) were included. We found increased ALFF mainly in the bilateral inferior frontal gyrus (IFG), insula, anterior cingulate gyrus/medial prefrontal cortex (ACC/mPFC), amygdala, striatum, right orbitofrontal cortex, and decreased ALFF in the right precentral gyrus and postcentral gyrus in patients across major psychiatric disorders. The abnormal pattern of ALFF across psychiatric disorders was spatially associated with transmembrane transport and ion channel, and dopaminergic, serotonergic, noradrenaline, opioid, and acetylcholine neurotransmission, as well as cognitive terms mainly involved rewards, mood, and fear. Common alteration of spontaneous brain activity across major psychiatric disorders are underpinned by disrupted gene expression, neurotransmitter activity, and cognitive functions. These findings highlight transdiagnostic neurobehavioral phenotypes that extend beyond discrete diagnostic categories, offering insights into shared pathophysiological mechanisms in psychiatric disorders.

Lipid droplet (LD)-associated metabolic reprogramming plays a critical role in breast cancer progression and immune modulation, yet robust prognostic biomarkers and their functional mechanisms remain incompletely understood. This study aimed to identify LD-associated biomarkers with prognostic and therapeutic relevance through multi-omics integration and functional validation. Bulk transcriptomic, single-cell RNA sequencing, and spatial transcriptomic data were integrated using machine learning to construct a prognostic model in the TCGA-BRCA cohort, validated in merged GEO datasets (GSE24450 and GSE42568). Functional enrichment, immune infiltration analyses, and in vitro/in vivo experiments—including 3T3-L1 adipogenesis, co-culture, orthotopic tumor models, and clinical adipose tissue validation were performed to characterize candidate genes. The StepCox[both]+plsRcox algorithm generated an optimal prognostic model that independently stratified patients across molecular subtypes, outperforming ER/PR/HER2 status. High-risk patients exhibited reduced immune infiltration and T-cell dysfunction. SQLE and SOCS3 emerged as key LD-associated genes with opposing expression patterns: SQLE enriched in tumor-associated adipocytes and SOCS3 in immune cells. Functional assays confirmed SQLE promoted while SOCS3 inhibited adipogenesis. Modulating these genes in adipocytes suppressed tumor growth and EMT and polarized macrophages toward an M1-dominant phenotype. SQLE and SOCS3 serve as functionally significant LD-associated prognostic biomarkers and represent promising therapeutic targets in breast cancer, revealing novel mechanisms in tumor–adipocyte crosstalk.

Objective:Lymphovascular invasion (LVI) is a crucial step in metastasis and is closely associated with poor prognosis in patients with breast cancer. However, its clinical and molecular characteristics remain insufficiently defined. We aimed to identify molecular targets for LVI-positive (LVI+) breast cancer and predict patient prognosis via the analysis of genomic variations using targeted sequencing. Methods:We established a large-scale targeted sequencing cohort of 4,079 breast cancer samples, which included 3,159 early-stage and locally advanced patients with available LVI statuses. Comparisons of somatic mutation frequencies and germline pathogenic/likely pathogenic (P/LP) mutation frequencies, mutational signature analyses, and mutual exclusivity and co-occurrence analyses were performed to identify key genomic features involved in LVI+ patients. Additionally, Kaplan-Meier survival analysis was conducted to further explore the prognostic value of co-mutations in LVI+ cases. Results:We observed that LVI+ patients with the hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) and triple-negative breast cancer (TNBC) subtypes exhibited worse disease-free survival. Notably, HR+/HER2- and HER2+ breast cancer patients with LVI displayed distinct genomic features compared with LVI- tumors. Specifically, LVI+ HR+/HER2- tumors exhibited greater frequencies of somatic mutations in TP53 and ESR1, germline BRCA2 P/LP variations, and an enrichment of clock-like single-base substitution (SBS)1 mutational signatures. In contrast, LVI+ HER2+ tumors demonstrated a higher incidence of somatic PIK3CA mutations and increased activity of the apolipoprotein B mRNA editing enzyme catalytic polypeptide (APOBEC)-associated SBS2 signature. Furthermore, we revealed that the co-mutation of TP53 and NF1 could serve as a potential prognostic marker for LVI+ HR+/HER2- patients. Conclusions:Our findings provide a comprehensive overview of the genomic characteristics of LVI in breast cancer, thereby offering insights that may help in refining precision treatment strategies for LVI+ breast cancer patients.

Genetic variants influencing gene expression have been extensively studied at the transcriptional level. How these variants affect downstream processes remains unclear. We quantitated ribosome occupancy in prefrontal cortex samples from the BrainGVEX cohort and integrated these data with transcriptomic and proteomic profiles from the same individuals. Through cis-QTL mapping, we identified genetic variants associated with transcript level (eQTLs), ribosome occupancy (rQTLs), and protein level (pQTLs). Notably, only 34% of eQTLs have their effects propagated to the protein levels, suggesting widespread post-transcriptional attenuation. Using both a gene-based approach and a variant-based approach we identified omics-specific QTLs that associated with brain disorder GWAS signals and found the majority of them to be driven predominantly by transcriptional regulation. Consistently, using a TWAS approach, we identified 74 SCZ risk genes across the three omics layers, 52 were discovered using transcriptome with 68% showing limited impact on protein expression. Our findings indicated that many disease-associated variants act through regulatory mechanisms that do not lead to an observable impact on the protein level.

Arginine methylation, catalyzed by protein arginine methyltransferases (PRMTs), is a regulatory key mechanism involved in various cellular processes such as gene expression, RNA processing, DNA damage repair. Increasing evidence highlights the crucial role of PRMTs in human diseases, including cancer, cardiovascular and metabolic diseases. Here, this review focuses on the latest findings regarding PRMTs in the central nervous system (CNS), emphasizing their regulatory roles in neural stem cells, neurons, and glial cells. Additionally, we examine the connection between PRMTs dysregulation and neurological diseases affecting the CNS, including brain tumors, neurodegenerative diseases, and neurodevelopmental disorders. Therefore, this review aims to deepen our understanding of PRMTs-mediated arginine methylation in CNS and open avenues for developing novel therapeutic strategies for neurological diseases. PRMTs’ role in neural cells and neurological diseases in CNS. PRMTs-mediated arginine methylation plays a significant role in multiple biological processes of neural cells via regulating protein–protein and protein-RNA interactions, such as NSC proliferation and differentiation, neuron morphogenesis and activity, glial cells development and function. Its dysregulation is closely linked to neurological diseases in CNS including brain tumors, neurodegenerative diseases, and neurodevelopmental disorders.

Backgrounds: Evidence from animal and population studies has consistently revealed that microRNA 218 (MIR218) is involved in susceptibility to depression and cognitive functions. Nevertheless, few studies have evaluated the association between MIR218 and clinical features in patients with depressed bipolar disorder (BD). Methods: A total of 66 patients with depressed BD and 49 healthy controls (HCs) were recruited for this study. MIR218 polygenic risk score (PRS) was used to assess the addictive effects of the MIR218 regulated genes. We compared the MIR218 PRS between patients with depressed BD and HCs to investigate whether it can be used to predict the risk of BD, and further explored the association between MIR218 PRS and cognitive performance as well as neurochemical metabolites among depressed BD. Results: We found that there was a significant difference in MIR218 PRS between patients with depressed BD and HCs. The correlation analysis indicated that MIR218 PRS was negative associated with the number of disease onset (r = -0.311, P = 0.033) and choline (Cho)/creatine (Cr) in right thalamus (r = -0.285, P = 0.021). Additionally, as supported by previous findings, patients with lower MIR218 PRS presented more domains of impaired cognitive function than those with higher scores. Conclusion: These findings suggested MIR218 PRS might be useful in differentiating patients with depressed BD from HCs. Moreover, depressed BD with lower MIR218 PRS showed more pronounced cognitive impairment than those with higher scores, which may be associated with disease recurrence and Cho metabolism in right thalamus.

Background RNA editing in the human brain exhibits a highly dynamic and complex regulatory landscape. With the rapid advancement of high-throughput sequencing technologies, many studies have focused on using edQTLs to explain GWAS signals. However, current studies are largely centered on European populations and primarily investigate genetic regulation at the level of individual editing sites. This approach overlooks the population-specific nature of RNA editing. Moreover, multiple studies have shown that RNA editing sites often occur in clusters and may be subject to coordinated regulation. To address those gaps, we aimed to construct the first large-scale RNA editing regulatory map in East Asians and proposed a novel concept of regional editing QTLs (redQTLs), treating regional average editing levels as an endophenotype for QTL analysis. Methods We conducted whole-genome and transcriptome sequencing on 546 samples from the DLPFC of East Asian individuals. For cross-population comparison, we also included 429 matched DNA and RNA sequencing samples from the European BrainGVEx cohort. RNA editing sites were identified by integrating novel candidates from REDItools2 with known sites from public databases. We then performed edQTL mapping to assess the genetic regulation of RNA editing and compared the results with those from the European cohort. Co-localization analyses were conducted to fine-map SCZ GWAS signals. Finally, based on the average RNA editing level within each region, we performed regional RNA editing quantitative trait locus (redQTL) mapping to explore their coordinated genetic regulation, which reflects the biologically clustered nature of RNA editing. Results (1) We constructed the first large-scale map of genetic regulation of RNA editing in the East Asian population. 18,648 high-confidence RNA editing sites were identified in this study, and 504,500 edQTLs were identified by QTL analysis, of which 473,637 were East Asian–specific.(2) The edQTL repetition rate within the same population was higher than that between different populations(π1(EUR-EAS) = 0.73 and π1(EAS-EAS) = 0.86). Population-specific edQTL differences were mainly driven by allele frequency variation. Shared signals showed consistent regulatory directions.(3) Colocalization analysis identified 20 schizophrenia-associated editing sites in East Asians, including four novel candidate genes (MPHOSPH9, CSMD1, MFSD13A, KCNIP4), and the sites chr7_137067935 and chr7_137067938 were shared risk sites for both populations, with DGKI and UBE2D3 being the shared risk genes.(4) A total of 3,096 RNA editing regions and 136,286 redQTL were identified in the East Asian population by combining editing correlation, spatial proximity, and other factors.(5) redQTL showed a higher cross-population sharing rate compared to edQTL (from 12.8% to 39.7%). Notably, in the European population, we identified a novel SCZ-colocalized editing region (chr7_38764259_ld) that did not show significant signals in the edQTL analysis. Discussion This study constructed the first large-scale brain edQTL map in East Asian populations. It advances our understanding of population-specific mechanisms underlying RNA editing. Through colocalization analysis with neuropsychiatric disorders such as schizophrenia (SCZ), we identified four East Asian–specific SCZ-colocalized genes that have not been reported in previous studies. Additionally, our redQTL analysis further explores the coordinated genetic regulation of RNA editing.

Genome-wide association studies (GWASs) have identified numerous genomic loci linked to schizophrenia (SCZ), while their pathogenic mechanisms largely remain unclear. This study demonstrated protein arginine methyltransferase 7 (PRMT7) as a key target of SCZ risk SNPs with allele-specific enhancer activity at 16q22.1. Downregulating PRMT7 in neural progenitor cells (NPCs) decreased proliferation, increased neuronal differentiation, and also led to longer neurites in these neurons. Conversely, overexpressing PRMT7 enhanced NPC proliferation and reduced neuronal differentiation. In three-dimensional (3D) cerebral organoids, similar NPC phenotypic changes were noted following PRMT7 depletion. Mechanistically, PRMT7 regulates the expression of genes related to the cell cycle and neuronal functions, such as CDKN2A and SYP, via symmetrical di-methylation at arginine 3 of histone 4 (H4R3me2s) modification in their promoters. Notably, these genes have a stronger association with SCZ compared to other mental disorders. Together, the results of this study reveal that PRMT7 is a functional gene at 16q22.1, contributing to the etiology of SCZ by modulating NPC proliferation and differentiation as an epigenetic regulator.

Neuropsychiatric disorders arise from complex interactions between genetic and environmental factors. DNA methylation, a reversible and environmentally responsive epigenetic regulatory mechanism, serves as a crucial bridge linking environmental exposure, gene expression regulation, and neurobehavioral outcomes. During long-duration deep-space missions, astronauts face multiple stressors-including microgravity, cosmic radiation, circadian rhythm disruption, and social isolation, which can induce alterations in DNA methylation and increase the risk of neuropsychiatric disorders. Genome-wide DNA methylation research can be divided into 3 major methodological stages: Study design, sample preparation and detection, and data analysis, each of which can be applied to astronaut neuropsychiatric health monitoring. Systematic comparison of the Illumina MethylationEPIC array and whole-genome bisulfite sequencing reveals their complementary strengths in terms of genomic coverage, resolution, cost, and application scenarios: the array method is cost-effective and suitable for large-scale population studies and longitudinal monitoring, whereas sequencing provides higher resolution and coverage and is more suitable for constructing detailed methylation maps and characterizing individual variation. Furthermore, emerging technologies such as single-cell methylation sequencing, nanopore long-read sequencing, and machine-learning-based multi-omics integration are expected to greatly enhance the precision and interpretability of epigenetic studies. These methodological advances provide key support for establishing DNA-methylation-based monitoring systems for neuropsychiatric risk in astronauts and lay an epigenetic foundation for safeguarding neuropsychiatric health during future long-term deep-space missions.

BACKGROUND:Schizophrenia is a severe psychiatric disorder with a complex etiology involving genetic and environmental factors. Despite its profound impact, the molecular mechanisms underlying schizophrenia remain elusive. Emerging evidence suggests that DNA methylation, palmitoylation, and immune cell activity play critical roles in its pathogenesis. This study employs a multi-omics approach to investigate the causal relationships among these factors and schizophrenia. METHODS:We utilized Mendelian randomization (MR), integrating expression, protein, and methylation quantitative trait loci (eQTL, pQTL, mQTL) to explore causal pathways in schizophrenia. Our approach involved (1) identifying palmitoylation-related genes and assessing their causal effects on schizophrenia via two-sample MR, validated by summary-data-based MR (SMR); (2) conducting mediation MR to examine upstream DNA methylation's role in gene regulation and its impact on schizophrenia; and (3) investigating downstream immune cell traits as mediators of gene effects on schizophrenia risk. Sensitivity analyses ensured robustness. RESULTS:We found a significant causal association between increased ZDHHC20 expression, a palmitoyltransferase, and elevated schizophrenia risk (p < 0.05), confirmed by SMR. Upstream, DNA methylation at cg18095732 regulates ZDHHC20, mediating 59.31% of its effect on schizophrenia (p < 0.05). Downstream, CCR7 expression on naive CD8+ T cells mediates 33.35% of ZDHHC20's influence on schizophrenia risk (p < 0.05). CONCLUSIONS:This study reveals a novel mechanistic axis in schizophrenia: DNA methylation-ZDHHC20-immune regulation, linking epigenetic modifications and palmitoylation to neuroimmune dysregulation. ZDHHC20 emerges as a potential therapeutic target, highlighting the value of multi-omics in psychiatric disease research and suggesting avenues for targeted interventions addressing both neuronal and immune contributions.

BACKGROUND:Both genetic and environmental factors can influence brain function in individuals with bipolar disorder (BD). This study aimed to investigate the aberrant functional connectivity (FC) of the striatum and its relationship with the polygenic risk score (PRS) of MicroRNA-124 (MIR124) and experiences of childhood trauma. METHODS:The study recruited 80 patients with BD and 54 healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from all participants. Six pairs of striatal subregions were selected as seeds for the FC analysis. Additionally, MIR124 PRS was calculated, and childhood trauma was assessed. Partial correlation and mediation analyses were conducted to explore these associations. RESULTS:BD exhibited higher incidence of childhood trauma and increased MIR124 PRS compared to HCs. When compared to HCs, BD showed decreased FC between the left dorsal caudate (dCa) and right thalamus, the right dCa and left medial superior frontal gyrus, and the right dlPu and right median cingulate/paracingulate gyri. Conversely, increased FC was observed between the right dorsolateral putamen (dlPu) and left precentral gyrus, the right globus pallidus (GP) and precentral gyrus. Notably, mediation analyses revealed that scores for emotional neglect (EN) and physical neglect (PN) from the Childhood Trauma Questionnaire (CTQ) mediated the relationship between MIR124 PRS and FC between the right GP and left precentral gyrus. CONCLUSIONS:These findings suggest that an elevated MIR124 PRS and experiences of childhood trauma may be associated with altered FC within corticostriatal circuits in BD, offering potential new insights into the underlying mechanisms of BD.

Responses to psychosocial stress influence health outcomes and are therefore crucial for understanding disease risk. Although previous research has documented physiological and psychological stress responses, comparisons of effect sizes across these responses and their interrelationships remain underexplored. In this meta-analysis, we systematically examined 171 studies employing the Trier Social Stress Test (TSST) involving 8452 healthy adults. Three-level meta-analysis (TLMA) with moderator analyses showed physiological responses (effect sizes ES = 1.25, 95 CI [1.13, 1.38], Hedges' g) to be significantly larger than psychological responses (ES = 0.93, 95 % CI [0.79, 1.09]), p < 0.001. Additional distinctions were identified within domains. Among physiological measures, the autonomic nervous system (ANS; ES = 1.38, 95 % CI [1.23, 1.54]) showed stronger responses than hypothalamic-pituitary-adrenal (HPA) axis (ES = 1.04, 95 % CI [0.86, 1.21]), p = 0.002. Among psychological measures, negative emotional responses (ES = 0.96, 95 % CI [0.79, 1.14]) were stronger than positive emotional responses (ES = 0.67, 95 % CI [0.39, 0.96]), p = 0.033. Strong correlations were observed among physiological responses within the ANS (ρ = 0.69-0.87, all p < 0.01), while salivary cortisol, an HPA axis measure, showed a weak, non-significant correlation with heart rate (ρ = -0.22, p > 0.05). Across physiological and psychological domains, correlations were generally weak and non-significant (ρ = -0.30-0.60, p > 0.05), except for heart rate and negative affect, which showed a moderate, statistically significant correlation (ρ = -0.67, p = 0.039). These results underscore the complexity of stress responses and highlight the importance of integrating multiple measures for a comprehensive assessment. Additionally, cardiovascular measures reflecting ANS activation appear promising as acute stress-related endophenotypes and offer greater utility than cortisol for studying the links among genetics, psychosocial stress, and psychiatric disorders.

The China Brain Multi-omics Atlas Project (CBMAP) aims to generate a comprehensive molecular reference map of over 1000 human brains (Phase I), spanning a broad age range and multiple regions in China, to address the underrepresentation of East Asian populations in brain research. By integrating genome, epigenome, transcriptome, proteome (including multiple post-translational modifications), and metabolome data, CBMAP is set to provide a rich and invaluable resource for investigating the molecular underpinnings of aging-related brain phenotypes and neuropsychiatric disorders. Leveraging high-throughput omics data and advanced technologies, such as spatial transcriptomics, proteomics, and single-nucleus 3D chromatin structure analysis, this atlas will serve as a crucial resource for the brain science community, illuminating disease mechanisms and enhancing the utility of data from genome-wide association studies (GWAS). CBMAP is also poised to accelerate drug discovery and precision medicine for brain disorders.

Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have become essential tools for profiling gene expression across different cell types in biomedical research. While factors like RNA integrity, cell count, and sequencing depth are known to influence data quality, quantitative benchmarks and actionable guidelines are lacking. This gap contributes to variability in study designs and inconsistencies in downstream analyses. In this study, we systematically evaluated quantitative precision and accuracy in expression measures across 23 sc/snRNA-seq datasets comprising 3,682,576 cells from 339 samples. Precision was assessed using technical replicates based on pseudo-bulks created from subsampling. Accuracy was evaluated using sample-matched scRNA-seq and pooled-cell RNA sequencing data of mononuclear phagocytes from four species. Our results show that precision and accuracy are generally low at the single-cell level, with reproducibility being strongly influenced by cell count and RNA quality. We established data-driven thresholds for optimizing study design, recommending at least 500 cells per cell type per individual to achieve reliable quantification. Furthermore, we showed that signal-to-noise ratio is a key metric for identifying reproducible differentially expressed genes. To support future research, we developed Variability In single-Cell gene Expression (VICE), a tool that evaluates sc/snRNA-seq data quality and estimates the true positive rate of differential expression results based on sample size, observed noise levels, and expected effect size. These findings provide practical, evidence-based guidelines to enhance the reliability and reproducibility of sc/snRNA-seq studies.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet, the majority of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n=158), Europeans (EUR, n=408), and East Asians (EAS, n=217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs (representing ∼17% of all eQTLs pairs) linked to 1,276 genes (about 10% of all eGenes) and 198,769 SNPs (approximately 16% of all eSNPs) were identified only in the non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare (MAF < 0.05) in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified seven new risk genes ( SFXN2 , RP11-282018.3 , CYP17A1 , VPS37B , DENR , FTCDNL1 , and NT5DC2 ), and three potential novel regulatory variants in known risk genes ( CNNM2 , C12orf65 , and MPHOSPH9 ) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of novel risk genes in SCZ.

Background Alternative polyadenylation (APA) events within 3′ untranslated regions (3’UTRs) are pivotal modulators of posttranscriptional and translational processes, significantly impacting biological functions and disease risks, particularly in brain development and neuropsychiatric disorders. Over 90% of neuropsychiatric GWAS variants occur in noncoding regions, with 3’UTRs harboring critical noncoding variants. Quantitative trait loci (QTLs) associated with 3’UTR APA phenotypes (3’aQTLs) elucidate 16.1% of human diseases and traits. However, previous research on the developing human brain has been limited in splicing (sQTLs) and gene expression (eQTLs), leaving a gap in the dynamic temporal changes of aQTLs. This study explores the genetic control of stage-dependent APA (sdaQTL), with stage defined by fetal stages to old age. Methods We conducted whole genome sequencing (WGS) and RNA sequencing (RNA-seq) on 315 human prefrontal cortex samples from East Asians (EAs). This cohort included 107 fetal brains and 208 postnatal adult brains spanning the fetal period (14 to 38 weeks post-conception, n=107), young and mid-aged (18 to 65 years, n=67), and elderly individuals (66 to 102 years, n=141). Utilizing DaPars2, we calculated the poly(A) site-usage index (PDUI) value. the R package maSigPro facilitated the identification of stage-dependent APA (sdAPA) events, defining a goodness-of-fit threshold of R² ≥ 0.3. QTLtools tested the association between normalized PDUI values and SNP genotypes, adjusting for covariates via PCAforQTL. We further compared developmental trajectories and QTL results between EA and European (EUR) populations. We collected and standardized summary statistics from 13 neuropsychiatric GWAS. The coloc R package identified GWAS signals sharing genetic effects with 3’aQTLs and eQTLs. Results We identified 32,315 APA events in the 315 fetal and adult brain RNA-seq datasets and further identified 26,359 stage-dependent APA (sdAPA) events, classified into four major clustering patterns: lengthening, shortening, lengthen-shortening and shorten-lengthening. Notably, 61.17% of sdAPA events exhibit a “lengthen-shortening” pattern during brain development and aging, exemplified by the gene APP. QTLtools identified cis SNPs within 1 Mb of a 3’UTR associated with differential sdAPA usage (aQTLs), discovering 92,727 lead aQTLs, including the ZNF211, a zinc finger protein associated with schizophrenia. Cross-population comparisons revealed similar sdAPA patterns between EA and EUR, with 17.96% and 17.71% of sdAPA genes associated with the developmental process respectively. EA-specific aQTLs were identified, with 13% altering motifs crucial for APA. Bayesian co-localization identified 27 sdAPA genes associated with neuropsychiatric disorders. Discussion Our findings provide novel insights into the genetic regulation of APA events in developing human brain. The dynamic “lengthening-shortening” pattern observed in a majority of sdAPA events underscores the complexity of post-transcriptional regulation. The co-localization of APA genes with neuropsychiatric disorder risk loci, particularly the identification of aQTLs-specific not associated with eQTLs, suggests a unique role of APA in disease pathogenesis. The population-specific aQTLs imply that genetic diversity in 3’UTR might influence disease susceptibility. These results emphasize the importance of considering developmental stages and genetic diversity in neuropsychiatric research. Disclosure Nothing to disclose.

Single-cell/nuclei RNA sequencing (sc/snRNA-seq) is widely used for profiling cell-type gene expression in brain research. An important but frequently underappreciated issue is the data quality in terms of precision and accuracy. We evaluated precision using data from 14 human brain studies with a total of 3,483,905 cells from 297 individuals, with technical replicates based on random grouping of cells of the same type from the same individual. We also evaluated accuracy with sample-matched scRNA-seq and pooled-cell RNA-seq data of cultured mononuclear phagocytes from four species. Low precision and accuracy at the single-cell level across all evaluated data were observed. Cell number was highlighted as a key factor determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-seq. A high missing rate is likely the cause of the quantification quality problem. Downstream analysis results are severely affected by the expression quality issue. Many false findings can be produced when the noises are not properly controlled. This study underscores the necessity of sequencing enough cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification. Pseudo-bulk aggregation of expression data over cells of the same type is required when the high-quality expression quantification is desired.