The role of gut microbiota in chronic restraint stress-induced cognitive deficits in mice.

BACKGROUND: Chronic stress induces cognitive deficits. There is a well-established connection between the enteric and central nervous systems through the microbiota-gut-brain (MGB) axis. However, the effects of the gut microbiota on cognitive deficits remain unclear. The present study aimed to elucidate the microbiota composition in cognitive deficits and explore its potential in predicting chronic stress-induced cognitive deficits. METHODS: Mice were randomly divided into control and chronic restraint stress (CRS) groups. The mice subjected to CRS were further divided into cognitive deficit (CRS-CD) and non-cognitive deficit (CRS-NCD) groups using hierarchical cluster analysis of novel object recognition test results. The composition and diversity of the gut microbiota were analyzed. RESULTS: After being subjected to chronic restraint distress, the CRS-CD mice travelled shorter movement distances (p = 0.034 vs. CRS-NCD; p < 0.001 vs. control) and had a lower recognition index than the CRS-NCD (p < 0.0001 vs. CRS-NCD; p < 0.0001 vs. control) and control mice. The results revealed that 5 gut bacteria at genus levels were significantly different in the fecal samples of mice in the three groups. Further analyses demonstrated that Muricomes were not only significantly enriched in the CRS-CD group but also correlated with a decreased cognitive index. The area under the receiver operating curve of Muricomes for CRS-induced cognitive deficits was 0.96. CONCLUSIONS: Our study indicates that the composition of the gut microbiota is involved in the development of cognitive deficits induced by chronic restraint stress. Further analysis revealed that Muricomes have the potential to predict the development of chronic stress-induced cognitive deficits in mice.

Epidemiologic and genetic associations between primary biliary cholangitis and extrahepatic rheumatic diseases.

Patients with primary biliary cholangitis (PBC) commonly experience extrahepatic rheumatic diseases. However, the epidemiologic and genetic associations as well as causal relationship between PBC and these extrahepatic conditions remain undetermined. In this study, we first conducted systematic review and meta-analyses by analyzing 73 studies comprising 334,963 participants across 17 countries and found strong phenotypic associations between PBC and rheumatic diseases. Next, we utilized large-scale genome-wide association study summary data to define the shared genetic architecture between PBC and rheumatic diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and Sjögren's syndrome (SS). We observed significant genetic correlations between PBC and each of the four rheumatic diseases. Pleiotropy and heritability enrichment analysis suggested the involvement of humoral immunity and interferon-associated processes for the comorbidity. Of note, we identified four variants shared between PBC and RA (rs80200208), SLE (rs9843053), and SSc (rs27524, rs3873182) using cross-trait meta-analysis. Additionally, several pleotropic loci for PBC and rheumatic diseases were found to share causal variants with gut microbes possessing immunoregulatory functions. Finally, Mendelian randomization revealed consistent evidence for a causal effect of PBC on RA, SLE, SSc, and SS, but no or inconsistent evidence for a causal effect of extrahepatic rheumatic diseases on PBC. Our study reveals a profound genetic overlap and causal relationships between PBC and extrahepatic rheumatic diseases, thus providing insights into shared biological mechanisms and novel therapeutic interventions.

A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features.

Facial morphology, a complex trait influenced by genetics, holds great significance in evolutionary research. However, due to limited fossil evidence, the facial characteristics of Neanderthals and Denisovans have remained largely unknown. In this study, we conducted a large-scale multi-ethnic meta-analysis of Genome-Wide Association Study (GWAS), including 9674 East Asians and 10,115 Europeans, quantitatively assessing 78 facial traits using 3D facial images. We identified 71 genomic loci associated with facial features, including 21 novel loci. We developed a facial polygenic score (FPS) that enables the prediction of facial features based on genetic information. Interestingly, the distribution of FPSs among populations from diverse continental groups exhibited significant correlations with observed facial features. Furthermore, we applied the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA, and aligned predictions with the fossil records. Our results suggested that Neanderthals and Denisovans likely shared similar facial features, such as a wider but shorter nose and a wider endocanthion distance. The decreased mouth width was characterized specifically in Denisovan. The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.

The Contribution of Mosaic Chromosomal Alterations to Schizophrenia.

BACKGROUND: Mosaic chromosomal alterations (mCAs) are implicated in neuropsychiatric disorders, yet the contribution to schizophrenia (SCZ) risk for somatic copy number variations (sCNVs) emerging in early developmental stages is not fully established. METHODS: We analyzed blood-derived genotype arrays from 9,715 SCZ patients and 28,822 controls of Chinese descent using a computational tool (MoChA) based on long-range chromosomal information to detect mCAs. We focused on probable early developmental sCNVs through stringent filtering. We assessed the sCNVs' burden across varying cell fraction (CF) cutoffs, as well as the frequency with which genes were involved in sCNVs. We integrated this data with the Psychiatric Genomics Consortium (PGC) dataset, which comprises 12,834 SCZ cases and 11,648 controls of European descent, and complemented it with genotyping data from postmortem brain tissue of 936 subjects (449 cases and 487 controls). RESULTS: Patients with SCZ had a significantly higher somatic losses detection rate than control subjects (1.00% vs 0.52%; odds ratio (OR) = 1.91; 95% CI, 1.47-2.49; two-sided Fisher's exact test, p=1.49×10-6). Further analysis indicated that the ORs escalated proportionately (from 1.91 to 2.78) with the increment in CF cutoffs. Recurrent sCNVs associated with SCZ (OR>8; Fisher's exact test, p<0.05) were identified, including notable regions at 10q21.1 (ZWINT), 3q26.1 (SLITRK3), 1q31.1 (BRINP3) and 12q21.31-21.32 (MGAT4C and NTS) in the Chinese cohort, some regions validated with PGC data. Cross-tissue validation pinpointed somatic losses at loci like 1p35.3-35.2 and 19p13.3-13.2. CONCLUSIONS: The study highlights mCAs' significant impact on SCZ, suggesting their pivotal role in the disorder's genetic etiology.

Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders.

BACKGROUND: There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated. METHODS: Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways. RESULTS: LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships. CONCLUSIONS: This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

Comparative genomics of macaques and integrated insights into genetic variation and population history.

The crab-eating macaques ( Macaca fascicularis ) and rhesus macaques ( M. mulatta ) are widely studied nonhuman primates in biomedical and evolutionary research. Despite their significance, the current understanding of the complex genomic structure in macaques and the differences between species requires substantial improvement. Here, we present a complete genome assembly of a crab-eating macaque and 20 haplotype-resolved macaque assemblies to investigate the complex regions and major genomic differences between species. Segmental duplication in macaques is ∼42% lower, while centromeres are ∼3.7 times longer than those in humans. The characterization of ∼2 Mbp fixed genetic variants and ∼240 Mbp complex loci highlights potential associations with metabolic differences between the two macaque species (e.g., CYP2C76 and EHBP1L1 ). Additionally, hundreds of alternative splicing differences show post-transcriptional regulation divergence between these two species (e.g., PNPO ). We also characterize 91 large-scale genomic differences between macaques and humans at a single-base-pair resolution and highlight their impact on gene regulation in primate evolution (e.g., FOLH1 and PIEZO2 ). Finally, population genetics recapitulates macaque speciation and selective sweeps, highlighting potential genetic basis of reproduction and tail phenotype differences (e.g., STAB1 , SEMA3F , and HOXD13 ). In summary, the integrated analysis of genetic variation and population genetics in macaques greatly enhances our comprehension of lineage-specific phenotypes, adaptation, and primate evolution, thereby improving their biomedical applications in human diseases.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits.

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Scattered throughout the human genome are variations in the genetic code that make individuals more or less likely to develop certain traits. To identify these variants, scientists carry out Genome-wide association studies (GWAS) which compare the DNA variants of large groups of people with and without the trait of interest. This method has been able to find the underlying genes for many human diseases, but it has limitations. For instance, some variations are linked together due to where they are positioned within DNA, which can result in GWAS falsely reporting associations between genetic variants and traits. This phenomenon, known as linkage equilibrium, can be avoided by analyzing functional genomics which looks at the multiple ways a gene's activity can be influenced by a variation. For instance, how the gene is copied and decoded in to proteins and RNA molecules, and the rate at which these products are generated. Researchers can now use an artificial intelligence technique called deep learning to generate functional genomic data from a particular DNA sequence. Here, Song et al. used one of these deep learning models to calculate the functional genomics of haplotypes, groups of genetic variants inherited from one parent. The approach was applied to DNA samples from over 350 thousand individuals included in the UK BioBank. An activity score, defined as the haplotype function score (or HFS for short), was calculated for at least two haplotypes per individual, and then compared to various complex traits like height or bone density. Song et al. found that the HFS framework was better at finding links between genes and specific traits than existing methods. It also provided more information on the biology that may be underpinning these outcomes. Although more work is needed to reduce the computer processing times required to calculate the HFS, Song et al. believe that their new method has the potential to improve the way researchers identify links between genes and human traits.

Structurally divergent and recurrently mutated regions of primate genomes.

We sequenced and assembled using multiple long-read sequencing technologies the genomes of chimpanzee, bonobo, gorilla, orangutan, gibbon, macaque, owl monkey, and marmoset. We identified 1,338,997 lineage-specific fixed structural variants (SVs) disrupting 1,561 protein-coding genes and 136,932 regulatory elements, including the most complete set of human-specific fixed differences. We estimate that 819.47 Mbp or ∼27% of the genome has been affected by SVs across primate evolution. We identify 1,607 structurally divergent regions wherein recurrent structural variation contributes to creating SV hotspots where genes are recurrently lost (e.g., CARD, C4, and OLAH gene families) and additional lineage-specific genes are generated (e.g., CKAP2, VPS36, ACBD7, and NEK5 paralogs), becoming targets of rapid chromosomal diversification and positive selection (e.g., RGPD gene family). High-fidelity long-read sequencing has made these dynamic regions of the genome accessible for sequence-level analyses within and between primate species.

Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ. STUDY DESIGN: We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis. STUDY RESULTS: We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor. CONCLUSIONS: This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.

Involvement of iNOS-induced reactive enteric glia cells in gastrointestinal motility disorders of postoperative Ileus mice.

Postoperative ileus (POI) is the cessation or reduction of gastrointestinal (GI) motility after surgery. Reactive enteric glial cells (EGCs) are critical for maintaining bowel function. However, the triggering mechanisms and downstream effects of reactive EGCs in POI were poorly understood. The goal of this current study was to investigate whether the inducible nitric oxide synthase (iNOS)-driven reactive EGCs participated in GI motility disorders and mechanisms underlying altered GI motility in POI. Intestinal manipulation (IM)-induced POI mice and iNOS-/- mice were used in the study. Longitudinal muscle and myenteric plexuses (LMMPs) from the distal small intestine were stained by immunofluorescence. Our results found that the GI motility disorders occurred in the IM-induced POI mice, and reactive EGCs were observed in LMMPs. Glial metabolic inhibitor gliotoxin fluorocitrate (FC) treatment or iNOS gene knockout attenuated GI motility dysfunction. In addition, we also found that FC treatment or iNOS gene knockout significantly inhibited the fluorescence intensity macrophage colony-stimulating factor (M-CSF), which reduced M2 phenotype macrophages activation in LMMPs of IM-induced POI mice. Our findings demonstrated that iNOS-driven reactive EGCs played a key role and were tightly linked to the MMs homeostasis in the POI mice. EGCs are emerging as a new frontier in neurogastroenterology and a potential therapeutic target.

Chimeric vaccine design against the epidemic Langya Henipavirus using immunoinformatics and validation via immune simulation approaches.

In July 2022, a new virus called Langya virus (LayV) was discovered in China in patients who had a fever. This virus is a type of Henipavirus (HNV) and is considered a potential threat as it could spread from animals to humans. It causes respiratory disease with symptoms including fever, coughing, and fatigue and is closely linked to two other henipaviruses that are known to infect humans, namely Hendra and Nipah viruses. These viruses may cause fatal respiratory illnesses. Investigators believe that the LayV is spread by shrews, and may have infected humans directly or via an intermediary species. Thus, the use of vaccines or immunizations against LayV is an alternate strategy for disease prevention. In this study, we employed various immunoinformatics methods to predict B cell, HTL and T cell epitopes from the LayV proteome in order to find the most promising candidate for a LayV vaccine. The most potent epitopes that are immunogenic and non-allergenic were joined with each other through suitable linkers. Human ß-defensin 2 was employed as an adjuvant to increase the immunogenicity of the vaccine construct. The final sequence of a multi-epitope vaccine construct was modelled for docking with TLRs. Concisely, our results suggest that the docked complexes of vaccine-TLRs seemed to be stable. Additionally, in silico cloning was done using E. coli as the host in order to validate the expression of our designed vaccine construct. The GC content of 54.39% and CAI value of 0.94 revealed that the vaccine component expresses efficiently in the host. This study presents the novel vaccine construct for LayV which will be essential for further experimental validations to confirm the immunogenicity and safety of the proposed vaccine structure, and eventually to treat HNV-related diseases.

Peripheral Nerve Block and Peri-operative Neurocognitive Disorders in Older Patients With Hip Fractures: A Systematic Review With Meta-analysis.

Background: Poor pain control and opioid use are risk factors for perioperative neurocognitive disorders (PND). The peripheral nerve block (PNB) can reduce pain and opioid consumption. This systematic review aimed to investigate the effects of PNB on PND in older patients with hip fractures. Methods: The PubMed, Cochrane Central Registers of Controlled Trial, Embase and ClinicalTrials.gov databases were searched from inception until November 19, 2021 for all randomized controlled trials (RCTs) comparing PNB with analgesics. The quality of the selected studies was assessed according to Version 2 of the Cochrane tool for assessing the risk of bias in RCTs. The primary outcome was the incidence of PND. Secondary outcomes included pain intensity and the incidence of postoperative nausea and vomiting. Subgroup analyses were based on population characteristics, type and infusion method of local anesthetics, and type of PNB. Results: Eight RCTs comprising 1015 older patients with hip fractures were included. Compared with analgesics, PNB did not reduce the incidence of PND in the elderly hip fracture population comprising patients with intact cognition and those with pre-existing dementia or cognitive impairment (risk ratio [RR] = .67; 95% confidence interval [CI] = .42 to 1.08; P = .10; I2 = 64%). However, PNB reduced the incidence of PND in older patients with intact cognition (RR = .61; 95% CI = .41 to .91; P = .02; I2 = 0%). Fascia iliaca compartment block, bupivacaine, and continuous infusion of local anesthetics were found to reduce the incidence of PND. Conclusions: PNB effectively reduced PND in older patients with hip fractures and intact cognition. When the study population included patients with intact cognition and those with pre-existing dementia or cognitive impairment, PNB showed no reduction in the incidence of PND. These conclusions should be confirmed with larger, higher-quality RCTs.

Population genetics of marmosets in Asian primate research centers and loci associated with epileptic risk revealed by whole-genome sequencing.

The common marmoset ( Callithrix jacchus) has emerged as a valuable nonhuman primate model in biomedical research with the recent release of high-quality reference genome assemblies. Epileptic marmosets have been independently reported in two Asian primate research centers. Nevertheless, the population genetics within these primate centers and the specific genetic variants associated with epilepsy in marmosets have not yet been elucidated. Here, we characterized the genetic relationships and risk variants for epilepsy in 41 samples from two epileptic marmoset pedigrees using whole-genome sequencing. We identified 14 558 184 single nucleotide polymorphisms (SNPs) from the 41 samples and found higher chimerism levels in blood samples than in fingernail samples. Genetic analysis showed fourth-degree of relatedness among marmosets at the primate centers. In addition, SNP and copy number variation (CNV) analyses suggested that the WW domain-containing oxidoreductase ( WWOX) and Tyrosine-protein phosphatase nonreceptor type 21 ( PTPN21) genes may be associated with epilepsy in marmosets. Notably, KCTD18-like gene deletion was more common in epileptic marmosets than control marmosets. This study provides valuable population genomic resources for marmosets in two Asian primate centers. Genetic analyses identified a reasonable breeding strategy for genetic diversity maintenance in the two centers, while the case-control study revealed potential risk genes/variants associated with epilepsy in marmosets.

Effects of Propofol on Perioperative Sleep Quality in Patients Undergoing Gastrointestinal Endoscopy: A Prospective Cohort Study.

PURPOSE: Some patients experience sleep disturbances after endoscopy performed under sedation. This study aimed to evaluate the effects of propofol on sleep quality after gastrointestinal endoscopy (GE). DESIGN: This study was a prospective cohort study. METHODS: This study enrolled 880 patients who underwent GE. Patients who chose to undergo GE under sedation received intravenous propofol, whereas the control group did not. The Pittsburgh Sleep Quality Index (PSQI) was measured before GE (PSQI-1) and 3 weeks (PSQI-2) after GE. The Groningen Sleep Score Scale (GSQS) was used before GE (GSQS-1) and 1 (GSQS-2) and 7 days (GSQS-3) after GE. FINDINGS: There was a significant increase in GSQS scores from baseline to days 1 and 7 after GE (GSQS-2 vs GSQS-1, P < .001, GSQS-3 vs GSQS-1, P = .008). However, no significant changes were observed in the control group (GSQS-2 vs GSQS-1, P = .38, GSQS-3 vs GSQS-1, P = .66). On day 21, there were no significant changes in the baseline PSQI scores over time in either group (sedation group, P = .96; control group, P = .95). CONCLUSIONS: GE with propofol sedation negatively affected sleep quality for 7 days after GE but not 3 weeks after GE.

Identification of Distinct Genetic Profiles of Palindromic Rheumatism Using Whole-Exome Sequencing.

OBJECTIVE: Previous studies have underlined the genetic susceptibility in the pathogenesis of palindromic rheumatism (PR), but the known PR loci only partially explain the disease's genetic background. We aimed to genetically identify PR by whole-exome sequencing (WES). METHODS: This multicenter prospective study was conducted in 10 Chinese specialized rheumatology centers between September 2015 and January 2020. WES was performed in 185 patients with PR and in 272 healthy controls. PR patients were divided into PR subgroups who were negative for anti-citrullinated protein antibody (ACPA-) and positive for ACPA (ACPA+) according to ACPA titer (cutoff value 20 IU/liter). We conducted whole-exome association analysis for the WES data. We used HLA imputation to type HLA genes. In addition, we used the polygenic risk score to measure the genetic correlations between PR and rheumatoid arthritis (RA) and the genetic correlations between ACPA- PR and ACPA+ PR. RESULTS: Among 185 patients with PR enrolled in our study, 50 patients (27.02%) were ACPA+ and 135 PR patients (72.98%) were ACPA-. We identified 8 novel loci (in the ACPA- PR group: ZNF503, RPS6KL1, HOMER3, HLA-DRA; in the ACPA+ PR group: RPS6KL1, TNPO2, WASH2P, FANK1) and 3 HLA alleles (in the ACPA- PR group: HLA-DRB1*0803 and HLA-DQB1; in the ACPA+ PR group: HLA-DPA1*0401) that were associated with PR and that surpassed genome-wide significance (P < 5 × 10-8 ). Furthermore, polygenic risk score analysis showed that PR and RA were not similar (R2 < 0.025), whereas ACPA+ PR and ACPA- PR showed a moderate genetic correlation (0.38 < R2 < 0.8). CONCLUSION: This study demonstrated the distinct genetic background between ACPA- and ACPA+ PR patients. Additionally, our findings strengthened that PR and RA were not genetically similar.

Integrative Genomic Analysis of m6a-SNPs Identifies Potential Functional Variants Associated with Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects 35 million people worldwide. However, no potential therapeutics currently are available for AD because of the multiple factors involved in it, such as regulatory factors with their candidate genes, factors associated with the expression levels of its corresponding genes, and many others. To date, 29 novel loci from GWAS have been reported for AD by the Psychiatric Genomics Consortium (PGC2). Nevertheless, the main challenge of the post-GWAS era, namely to detect significant variants of the target disease, has not been conducted for AD. N6-methyladenosine (m6a) is reported as the most prevalent mRNA modification that exists in eukaryotes and that influences mRNA nuclear export, translation, splicing, and the stability of mRNA. Furthermore, studies have also reported m6a's association with neurogenesis and brain development. We carried out an integrative genomic analysis of AD variants from GWAS and m6a-SNPs from m6AVAR to identify the effects of m6a-SNPs on AD and identified the significant variants using the statistically significance value (p-value <0.05). The cis-regularity variants with their corresponding genes and their influence on gene expression in the gene expression profiles of AD patients were determined, and showed 1458 potential m6a-SNPs (based on p-value <0.05) associated with AD. eQTL analysis showed that 258 m6a-SNPs had cis-eQTL signals that overlapped with six significant differentially expressed genes based on p-value <0.05 in two datasets of AD gene expression profiles. A follow-up study to elucidate the impact of our identified m6a-SNPs in the experimental study would validate our findings for AD, which would contribute to the etiology of AD.

A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression.

Genome-wide association studies have identified 19p13.3 locus associated with primary biliary cholangitis (PBC). Here we aim to identify causative variant(s) and initiate efforts to define the mechanism by which the 19p13.3 locus variant(s) contributes to the pathogenesis of PBC. A genome-wide meta-analysis of 1931 PBC subjects and 7852 controls in two Han Chinese cohorts confirms the strong association between 19p13.3 locus and PBC. By integrating functional annotations, luciferase reporter assay and allele-specific chromatin immunoprecipitation, we prioritize rs2238574, an AT-Rich Interaction Domain 3A (ARID3A) intronic variant, as a potential causal variant at 19p13.3 locus. The risk allele of rs2238574 shows higher binding affinity of transcription factors, leading to an increased enhancer activity in myeloid cells. Genome-editing demonstrates the regulatory effect of rs2238574 on ARID3A expression through allele-specific enhancer activity. Furthermore, knock-down of ARID3A inhibits myeloid differentiation and activation pathway, and overexpression of the gene has the opposite effect. Finally, we find ARID3A expression and rs2238574 genotypes linked to disease severity in PBC. Our work provides several lines of evidence that a non-coding variant regulates ARID3A expression, presenting a mechanistic basis for association of 19p13.3 locus with the susceptibility to PBC.