Structural and functional alterations in MRI-negative drug-resistant epilepsy and associated gene expression features.

Neuroimaging techniques have been widely used in the study of epilepsy. However, structural and functional changes in the MRI-negative drug-resistant epilepsy (DRE) and the genetic mechanisms behind the structural alterations remain poorly understood. Using structural and functional MRI, we analyzed gray matter volume (GMV) and regional homogeneity (ReHo) in DRE, drug-sensitive epilepsy (DSE) and healthy controls. Gene expression data from Allen human brain atlas and GMV/ReHo were evaluated to obtain drug resistance-related and epilepsy-associated gene expression and compared with real transcriptional data in blood. We found structural and functional alterations in the cerebellum of DRE patients, which may be related to the mechanisms of drug resistance in DRE. Our study confirms that changes in brain morphology and regional activity in DRE patients may be associated with abnormal gene expression related to nervous system development. And SP1, as an important transcription factor, plays an important role in the mechanism of drug resistance.

The study of Alzheimer's disease risk diagnosis based on natural killer cell marker genes in the multi-omics data.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder, currently lacking effective early diagnostic methods. However, natural killer (NK) cells may play a potential role in AD pathogenesis. OBJECTIVE: This study aims to identify AD-related feature genes from NK cell markers to construct a diagnostic model and explore their potential biological mechanisms in AD. METHODS: Single-cell RNA sequencing data was used to identify NK cell markers. A novel feature selection algorithm, adaptive dynamic graph convolutional network (ADGCN), was proposed to extract AD-related feature genes and construct a diagnostic model. Differential, correlation and enrichment analyses were performed to understand the biological mechanisms of these genes. Immune infiltration analysis compared the immune microenvironment between AD and controls. Two regulatory networks explored interactions between feature genes, transcription factors and microRNAs. The association between SNPs and feature genes' expression was examined through expression quantitative trait loci analysis. Differential CpG sites were identified to analyze their association with the NK cell markers' expression. RESULTS: We developed an optimal diagnostic model (ADGCN-XGBoost) with 17 feature genes, demonstrating high diagnostic effectiveness across datasets. These genes were primarily related to macromolecule biosynthesis, cytoplasmic translation biological processes and ribosome pathway, and potentially modulated immune infiltration of AD patients. We predicted 27 target miRNAs and 21 transcription factors influencing these genes. Multimodal analysis identified 57 significant SNP-gene associations and seven CpG-gene pairs. CONCLUSIONS: This study proposed a novel feature selection algorithm and developed a diagnostic model based on 17 feature genes, providing new potential biomarkers for AD diagnosis.

The impact of genetic similarity and environment on the flavonoids variation pattern of Cyclocarya paliurus.

The leaves of Cyclocarya paliurus (Batalin) Iljinskaja, an endemic tree with a scattered distribution in subtropical China, are rich in flavonoids with beneficial, health-promoting properties. To understand the impact of environment and genetic similarity on the variation pattern of flavonoids in this species, we analyzed C. paliurus germplasm resources from 26 different populations previously sampled from the main distribution area. Environmental, genetic and biochemical data was associated by genetic structure analysis, non-parametric tests, correlation analysis and principal component analysis. We found that populations with higher flavonoid contents were distributed at higher elevations and latitudes and fell into two groups with similar genetic diversities. Significant accumulations of isoquercitrin and kaempferol 3-O-glucoside were detected in the higher flavonoid-content resources. In addition, the genetic clusters with higher flavonoid contents exhibited broader environmental-adaptive capacities. Even in the presence of environmental factors promoting C. paliurus flavonoid accumulation, only those populations having a specific level of genetic similarity were able to exploit such environments.

Multimorbidity in neurodegenerative diseases: a network analysis.

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.

Rice homolog of Arabidopsis Xylem NAC domain 1 (OsXND1), a NAC transcription factor regulates drought stress responsive root system architecture in indica rice.

Rice yield is greatly constrained by drought stress. In Arabidopsis, XYLEM NAC DOMAIN 1 (XND1) gene regulates the xylem formation, efficiency of water transport, and the delicate equilibrium between drought tolerance and resistance to pathogens. However, diversity and the role of rice homologs of OsXND1 is not reported so far. This study hypothesized that the rice homolog of OsXND1 also regulates drought stress tolerance through modulation of root architecture. Initially, phylogenetic analysis identified two OsXND1 homologs (Os02g0555300 and Os04g0437000) in rice. Further, 14 haplotypes were identified in the OsXND1 of which Hap1 and Hap3 were major haplotypes. The association analysis of OsXND1 with 16 different traits, including 10 root traits, showed three SNPs (Chr02:20972728-Promoter variant; Chr02:20972791-5' UTR variant, and Chr02:20973745-3' UTR variant) were significantly associated with root area, root surface area, total root length, and convex hull area only under drought stress in indica rice. Besides, the superior haplotype of OsXND1 increased the root area, root surface area, total root length, and convex hull area by 46%, 40%, 38%, and 42%, respectively, under drought stress conditions. Therefore, the identified superior haplotype of OsXND1 can be utilized in haplotype breeding programs for the improvement of drought tolerance in rice.

Utilizing non-invasive prenatal test sequencing data for human genetic investigation.

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

Genomic Correlation, Shared Loci, and Causal Relationship Between Bullous Pemphigoid and Atopic Dermatitis: A Large-Scale Genome-Wide Cross-Trait Analysis.

BACKGROUND: Bullous pemphigoid (BP) and atopic dermatitis (AD) are currently thought to be tightly related, yet studies of the mechanisms of co-morbidities are lacking. METHODS: We obtained GWAS data for BP (N = 376,274) and AD (N = 796,661) from the Finnish Genetic Research Program dataset and the UK Biobank, separately. Then, the following four analyses were performed: (1) cross-trait linkage disequilibrium score regression (LDSC) to assess the genetic correlation between BP and AD, (2) cross-phenotype association analysis (CPASSOC) to identify multiple effector loci shared by BP and AD, (3) transcriptome-wide association study (TWAS) to determine whether their cross-organizational expression patterns share genes with a common biological mechanism of relevance, and (4) bidirectional Mendelian randomization (MR) analysis to assess bidirectional causal effects of BP and AD. RESULTS: We found a positive genetic association between BP and AD (rg = 0.5476, p = 0.0495) as well as identified four pleiotropic loci and 59 common genes affecting BP and AD. Bidirectional MR analysis suggested that BP promotes the risk of AD. CONCLUSIONS: We revealed a genetic link between BP and AD, which is associated with biological pleiotropy and causality. Awareness of the association between BP and AD helps dermatologists manage patients with these illnesses.

Unraveling Shared and Unique Genetic Causal Relationship Between Gut Microbiota and Four Types of Uterine-Related Diseases: Bidirectional Mendelian Inheritance Approaches to Dissect the "Gut-Uterus Axis".

BACKGROUND: The gut microbiota has emerged as a pivotal factor in the etiology of uterine-related diseases. This study aims to elucidate the genetic causal link between gut microbiota composition and these conditions, focusing on the systemic impact and uterine pathology to better understand the "Gut-Uterus Axis." METHODS: We utilized pooled data from two different GWAS databases, including data from 209 gut microbiota traits and data from four uterus-related diseases. Bidirectional Mendelian Randomization (MR) approaches, incorporating Bayesian weighting and traditional inverse variance weighting (IVW) methods, were employed to explore causal relationships. The robustness of findings was ensured through sensitivity analyses, outlier testing, and MR-PRESSO analysis. RESULTS: Seventeen significant associations were identified between gut microbiota traits and uterine-related diseases, suggesting potential causal links. These associations were consistent across sensitivity analyses, affirming the reliability of our results. Conversely, reverse MR analyses did not reveal statistically significant associations between uterine diseases and bacterial traits, indicating a unidirectional influence of gut microbiota on uterine health. These findings highlight the complex interplay within the "Gut-Uterus Axis." CONCLUSION: This research establishes a causal relationship between gut microbiota and uterine diseases, advocating for targeted interventions to mitigate associated risks. It underscores the interconnectedness of gut and reproductive health, promoting a holistic approach to management and treatment within the "Gut-Uterus Axis".

Extreme-Phenotype Genome-Wide Association Analysis for Growth Traits in Spotted Sea Bass (Lateolabrax maculatus) Using Whole-Genome Resequencing.

Spotted sea bass (Lateolabrax maculatus) is an important marine economic fish in China, ranking third in annual production among marine fish. However, a declined growth rate caused by germplasm degradation has severely increased production costs and reduced economic benefits. There is an urgent need to develop the fast-growing varieties of L. maculatus and elucidate the genetic mechanisms underlying growth traits. Here, whole-genome resequencing technology combined with extreme phenotype genome-wide association analysis (XP-GWAS) was used to identify candidate markers and genes associated with growth traits in L. maculatus. Two groups of L. maculatus, consisting of 100 fast-growing and 100 slow-growing individuals with significant differences in body weight, body length, and carcass weight, underwent whole-genome resequencing. A total of 4,528,936 high-quality single nucleotide polymorphisms (SNPs) were used for XP-GWAS. These SNPs were evenly distributed across all chromosomes without large gaps, and the average distance between SNPs was only 175.8 bp. XP-GWAS based on the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (Blink) and Fixed and random model Circulating Probability Unification (FarmCPU) identified 50 growth-related markers, of which 17 were related to body length, 19 to body weight, and 23 to carcass weight. The highest phenotypic variance explained (PVE) reached 15.82%. Furthermore, significant differences were observed in body weight, body length, and carcass weight among individuals with different genotypes. For example, there were highly significant differences in body weight among individuals with different genotypes for four SNPs located on chromosome 16: chr16:13133726, chr16:13209537, chr16:14468078, and chr16:18537358. Additionally, 47 growth-associated genes were annotated. These genes are mainly related to the metabolism of energy, glucose, and lipids and the development of musculoskeletal and nervous systems, which may regulate the growth of L. maculatus. Our study identified growth-related markers and candidate genes, which will help to develop the fast-growing varieties of L. maculatus through marker-assisted breeding and elucidate the genetic mechanisms underlying the growth traits.

Identification of Genetic Associations of IDH2, LDHA, and LDHB Genes with Milk Yield and Compositions in Dairy Cows.

Previous study revealed that isocitrate dehydrogenase (NADP (+)) 2, mitochondrial (IDH2), lactate dehydrogenase A (LDHA), and lactate dehydrogenase B (LDHB) genes were significantly differentially expressed in liver tissues of Holstein cows among different lactation periods and associated with lipid and protein metabolism; hence, they were considered as candidates for milk production traits. Herein, the genetic effects of the three genes on milk yield, fat, and protein traits were studied by association analysis using 926 Chinese Holstein cows from 45 sire families. As a result, five single nucleotide polymorphisms (SNPs) in IDH2, one in LDHA, and three in LDHB were identified by re-sequencing, and subsequently, they were genotyped in 926 Chinese Holstein cows by genotyping by target sequencing (GBTS). With the animal model, single-locus association analysis revealed that four SNPs in IDH2 and one SNP in LDHA were significantly associated with milk, fat, and protein yields (p ≤ 0.0491), and three SNPs in LDHB were associated with milk yield, milk fat yield, and fat percentage (p ≤ 0.0285). Further, four IDH2 SNPs were found to form a haplotype block significantly associated with milk yield, fat yield, protein yield, and protein percentage (p ≤ 0.0249). In addition, functional predictions indicated that one SNP in LDHA, g.26304153G>A, may affect transcription factor binding and two SNPs, g.88544541A>G and g.88556310T>C could alter LDHB mRNA secondary structure. In summary, this study profiled the significant genetic effects of IDH2, LDHA, and LDHB on milk yield and composition traits and provided referable genetic markers for genomic selection programs in dairy cattle.

Unveiling the Molecular Mechanisms Regulating Muscle Elasticity in the Large Yellow Croaker: Insights from Transcriptomics and Metabolomics.

The large yellow croaker (Larimichthys crocea) is an important economic fish in China. However, intensive farming practices, such as high stocking densities, suboptimal water quality, and imbalanced nutrition, have led to a decline in muscle quality. Muscle elasticity is a key texture property influencing muscle quality. Herein, transcriptomic and metabolomic analyses were performed on four groups: male high muscle elasticity (MEHM), female high muscle elasticity (MEHF), male low muscle elasticity (MELM), and female low muscle elasticity (MELF), to explore the molecular regulation underlying muscle elasticity in the large yellow croaker. Transcriptomics identified 2594 differentially expressed genes (DEGs) across the four groups, while metabolomics revealed 969 differentially expressed metabolites (DEMs). Association analysis indicated that the valine, leucine, and isoleucine biosynthesis pathways were significantly enriched between the MELF and MEHF groups; 2-Oxoisovalerate and L-Valine were DEMs; and the gene encoding L-threonine ammonia-lyase was a DEG. In the MELM and MEHM groups, pathways such as arginine biosynthesis; arginine and proline metabolism; and valine, leucine, and isoleucine degradation were significantly enriched. 4-guanidinobutanoate, L-aspartate, N-acetylornithine, and L-leucine were among the DEMs, while the DEGs included glul, gls, srm, hmgcs, and aacs. These findings provide insights into the molecular mechanisms controlling muscle elasticity, representing a theoretical foundation to breed high-quality large yellow croakers.

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.

BACKGROUND: Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions. METHODS: In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk. RESULTS: A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk. CONCLUSIONS: These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

Expression quantitative trait locus mapping of extracellular microRNAs in human plasma.

MicroRNAs, crucial in regulating protein-coding gene expression, are implicated in various diseases. We performed a genome-wide association study of plasma miRNAs (ex-miRNAs) in 3,743 Framingham Heart Study (FHS) participants and identified 1,027 cis-ex-miRNA-eQTLs (cis-exQTLs) for 37 ex-miRNAs, with 55% replication in an independent study. Colocalization analyses suggested potential genetic coregulation of ex-miRNAs with whole blood mRNAs. Mendelian randomization indicated 29 ex-miRNAs potentially influencing 35 traits. Notably, the chromosome 14q23 and 14q32 miRNA clusters emerged as the top signal, contributing over 50% of the significant cis-exQTL results, and were associated with a diverse range of traits including platelet count. Correlations of 10 ex-miRNAs (such as miR-376c-3p) in 14q32 with platelet count and volume were confirmed in FHS participants. These findings shed light on the genetic basis of ex-miRNA expression and their involvement in complex traits.

A New Method for Conditional Gene-Based Analysis Effectively Accounts for the Regional Polygenic Background.

Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs. The popular conditional analysis method, GCTA-COJO, accounts for the influence of several top independently associated SNPs outside the gene, correcting the z statistics for intragenic SNPs. We suggest a new TauCOR method for conditional gene-based analysis using summary statistics. This method accounts the influence of the full regional polygenic background, correcting the genotype correlations between intragenic SNPs. As a result, the distribution of z statistics for intragenic SNPs becomes conditionally independent of distribution for extragenic SNPs. TauCOR is compatible with any gene-based association test. TauCOR was tested on summary statistics simulated under different scenarios and on real summary statistics for a 'gold standard' gene list from the Open Targets Genetics project. TauCOR proved to be effective in all modelling scenarios and on real data. The TauCOR's strategy showed comparable sensitivity and higher specificity and accuracy than GCTA-COJO on both simulated and real data. The method can be successfully used to improve the effectiveness of gene-based association analyses.

Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. DISCUSSION: Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.

Association Analysis Provides Insights into Molecular Evolution in Salt Tolerance during Tomato Domestication.

Salt stress impairs plant growth and development, generally resulting in crop failure. Tomato domestication gave rise to a dramatic decrease in salt tolerance caused by the genetic variability of the wild ancestors. However, the nature of artificial selection in reducing tomato salt tolerance remains unclear. Here, we generated and analyzed datasets on the survival rates and sodium (Na+) and potassium (K+) concentrations of hundreds of tomato varieties from wild ancestors to contemporary breeding accessions under high salinity. Genome-wide association studies (GWAS) revealed that natural variation in the promoter region of the putative K+ channel regulatory subunit-encoding gene KSB1 (potassium channel beta subunit in Solanum lycopersicum) is associated with survival rates and root Na+/K+ ratios in tomato under salt stress. This variation is deposited in tomato domestication sweeps and contributes to modified expression of KSB1 by salt-induced transcription factor SlHY5 in response to high salinity. We further found that KSB1 interacts with the K+ channel protein KSL1 to maintain cellular Na+ and K+ homeostasis, thus enhancing salt tolerance in tomato. Our findings reveal the crucial role of the SlHY5-KSB1-KSL1 module in regulating ion homeostasis and salt tolerance during tomato domestication, elucidating that selective pressure imposed by humans on the evolutionary process provides insights into further crop improvement.

Associations between the multitrajectory neuroplasticity of neuronavigated rTMS-mediated angular gyrus networks and brain gene expression in AD spectrum patients with sleep disorders.

INTRODUCTION: The multifactorial influence of repetitive transcranial magnetic stimulation (rTMS) on neuroplasticity in neural networks is associated with improvements in cognitive dysfunction and sleep disorders. The mechanisms of rTMS and the transcriptional-neuronal correlation in Alzheimer's disease (AD) patients with sleep disorders have not been fully elucidated. METHODS: Forty-six elderly participants with cognitive impairment (23 patients with low sleep quality and 23 patients with high sleep quality) underwent 4-week periods of neuronavigated rTMS of the angular gyrus and neuroimaging tests, and gene expression data for six post mortem brains were collected from another database. Transcription-neuroimaging association analysis was used to evaluate the effects on cognitive dysfunction and the underlying biological mechanisms involved. RESULTS: Distinct variable neuroplasticity in the anterior and posterior angular gyrus networks was detected in the low sleep quality group. These interactions were associated with multiple gene pathways, and the comprehensive effects were associated with improvements in episodic memory. DISCUSSION: Multitrajectory neuroplasticity is associated with complex biological mechanisms in AD-spectrum patients with sleep disorders. HIGHLIGHTS: This was the first transcription-neuroimaging study to demonstrate that multitrajectory neuroplasticity in neural circuits was induced via neuronavigated rTMS, which was associated with complex gene expression in AD-spectrum patients with sleep disorders. The interactions between sleep quality and neuronavigated rTMS were coupled with multiple gene pathways and improvements in episodic memory. The present strategy for integrating neuroimaging, rTMS intervention, and genetic data provide a new approach to comprehending the biological mechanisms involved in AD.

Multi-trait analysis reveals risk loci for heart failure and the shared genetic etiology with blood lipids, blood pressure, and blood glucose.

Phenotypic associations have been reported between heart failure (HF) and blood lipids (BLs), blood pressure (BP), and blood glucose (BG). However, the shared genetic etiology underlying these associations remains incompletely understood. Conducting a large-scale multi-trait association study for HF with these traits, we discovered 143 previously unreported genomic risk loci for HF. Results showed that 46, 35, and 14 colocalized loci were shared by HF with BLs, BP, and BG, respectively. Notably, the loci shared by HF with these traits rarely overlapped, indicating distinct mechanisms. The combination of gene-mapping, gene-based, and transcriptome-wide association analyses prioritized noteworthy candidate genes (such as lipoprotein lipase [LPL], G protein-coupled receptor kinase 5 [GRK5], and troponin C1, slow skeletal and cardiac type [TNNC1]) for HF. Enrichment analysis revealed that HF exhibited comparable characteristics to cardiovascular traits and metabolic traits correlated to BLs, BP, and BG. Finally, we reported drug repurposing candidates and plasma protein targets for HF. These results provide biological insights into the pathogenesis of these comorbidities of HF.

Genome-wide association reveals a locus in neuregulin 3 associated with gabapentin efficacy in women with chronic pelvic pain.

Chronic pelvic pain (CPP) in women with no obvious pelvic pathology has few evidence-based treatment options. Our recent multicenter randomized controlled trial (GaPP2) in women with CPP and no obvious pelvic pathology showed that gabapentin did not relieve pain overall and was associated with more side effects than placebo. We conducted an exploratory genome-wide association study using eligible GaPP2 participants aiming to identify genetic variants associated with gabapentin response. One genome-wide significant association with gabapentin analgesic response was identified, rs4442490, an intron variant located in Neuregulin 3 (NRG3) (p = 2·11×10-8; OR = 18·82 (95% CI 4·86-72·83). Analysis of a large sample of UK Biobank participants demonstrated phenome-wide significant brain imaging features of rs4442490, particularly implicating the orbitofrontal cortex. NRG3 is expressed predominantly in central nervous system tissues and plays a critical role in nervous system development, maintenance, and repair, suggesting a neurobiologically plausible role in gabapentin efficacy and potential for personalized analgesic treatment.

Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analyses.

Atrial fibrillation (AF) is the most common arrhythmia in the world. Human genetics can provide strong AF therapeutic candidates, but the identification of the causal genes and their functions remains challenging. Here, we applied an AF fine-mapping strategy that leverages results from a previously published cross-ancestry genome-wide association study (GWAS), expression quantitative trait loci (eQTLs) from left atrial appendages (LAAs) obtained from two cohorts with distinct ancestry, and a paired RNA sequencing (RNA-seq) and ATAC sequencing (ATAC-seq) LAA single-nucleus assay (sn-multiome). At nine AF loci, our co-localization and fine-mapping analyses implicated 14 genes. Data integration identified several candidate causal AF variants, including rs7612445 at GNB4 and rs242557 at MAPT. Finally, we showed that the repression of the strongest AF-associated eQTL gene, LINC01629, in human embryonic stem cell-derived cardiomyocytes using CRISPR inhibition results in the dysregulation of pathways linked to genes involved in the development of atrial tissue and the cardiac conduction system.