Mohammed A. Farahat

BACKGROUND: Hypertension and preeclampsia are clinically distinct, yet biologically related conditions characterized by vascular dysfunction and elevated cardiovascular risk. Although genome-wide association studies have identified loci associated with blood pressure traits and preeclampsia, the functional mechanisms linking shared variants to gene regulation and clinical phenotypes remain unclear. METHODS: We integrated genome-wide association study summary statistics for hypertension, systolic blood pressure, diastolic blood pressure, and preeclampsia to identify shared variants ( P ≤1×10 ⁻⁴ ). Cis -expression quantitative trait loci analyses were performed in whole blood using RNA sequencing data from 180 Black women. Significant associations (false discovery rate ≤0.05) were evaluated for replication across vascular, metabolic, and endocrine tissues in the Genotype-Tissue Expression project. Associations between gene expression and blood pressure traits were also assessed. RESULTS: We identified 4792 shared genome-wide association study variants, of which 4663 were tested in expression quantitative trait loci analyses, yielding 1837 significant variant-gene associations across 78 genes. Replication in the Genotype-Tissue Expression project confirmed 645 associations involving 24 genes, many showing cross-tissue regulatory effects. Three genes ( C4B , HLA-C , and HLA-DQB1 ) demonstrated convergent evidence across genome-wide association study, gene regulation, and expression-trait analyses. C4B expression was positively associated with hypertension and systolic blood pressure, while HLA-C showed negative associations with hypertension, systolic blood pressure, and diastolic blood pressure. HLA-DQB1 expression was specifically associated with diastolic blood pressure, suggesting trait-specific effects. CONCLUSIONS: These findings highlight immune-related pathways as key mediators linking hypertension and preeclampsia. Integrating genetic, transcriptomic, and phenotypic data provides a framework for identifying functionally relevant loci and advancing mechanistic insights into cardiometabolic and pregnancy-related disorders.

Background: Preeclampsia (PE) is a complex hypertensive disorder of pregnancy characterized by endothelial dysfunction, immune dysregulation, and systemic vascular injury. Multiple genome-wide association studies (GWAS) have revealed genetic signals shared with hypertension and blood pressure traits, potentially obscuring biological mechanisms that are more specific to PE pathogenesis. Furthermore, the functional consequences of most PE-associated variants remain poorly understood. In addition, GWAS relies on short-read sequencing and array-based analyses, limiting the ability to identify insertions, deletions, and other structural variants that may contribute to disease-associated regulatory mechanisms. In this study, we investigated the regulatory architecture of PE-specific genetic variants and evaluated their potential linkage disequilibrium (LD) with structural variants. Methods: We integrated GWAS, transcriptomic, and long-read sequencing data to investigate the regulatory architecture of PE-specific genetic variants. Summary statistics for PE, hypertension, systolic and diastolic blood pressure were obtained from the GWAS Catalog, and variants uniquely associated with PE (P ≤ 1×10 ^-4 ) were prioritized. Cis-expression quantitative trait locus (cis-eQTL) analyses were performed in whole-blood RNA-sequencing data from 180 African American women. Significant associations were replicated in biologically relevant tissues from the GTEx Project, including vascular, renal, and immune-related tissues. Long-read sequencing-derived structural variants (SVs) were subsequently evaluated for LD with replicated eQTL loci. Results: A total of 10,843 PE-specific variants, present in whole-genome sequencing data of the 180 women, were evaluated. Cis-eQTL analyses identified 480 significant eQTL-gene associations involving 277 unique variants and 192 genes (FDR ≤ 0.05). Replication analyses supported 69 eQTL-gene associations across five GTEx tissues, involving 35 variants and 14 genes. Replicated signals were enriched in vascular tissues, particularly artery tibial and artery aorta. Several prioritized genes converged on immune and vascular pathways, including MICA, HLA-DPB1, SEMA4D, JUP, ZFP57, and TMEM204. Integration of GWAS and eQTL effects demonstrated consistent regulatory shifts associated with PE-risk alleles, including downregulation of immune-related loci and upregulation of select vascular-associated genes. Long-read sequencing analyses identified 66 high-LD (r ² ≥ 0.80) SNP-SV-gene associations, including 12 replicated eQTL variants, 8 candidate SVs, and 3 replicated genes, suggesting that structurally complex genomic regions may contribute to the observed regulatory signals. Conclusions: The tissues enriched in the regulatory signal highlight the importance of systemic endothelial biology in PE susceptibility. The findings of this study support a model in which PE-specific genetic susceptibility converges predominantly on interconnected immune and vascular regulatory mechanisms. The integration of eQTL analyses with long-read structural variant discovery provides additional insight into the complex genomic architecture underlying PE and highlights candidate regulatory loci that may not be adequately captured through conventional GWAS approaches alone. The study also emphasizes the importance of conducting functional genomic analyses in diverse populations to improve understanding of disease biology and advance precision medicine efforts. GRAPHICAL Regulatory Genomics of Preeclampsia-Specific Risk Variants Highlights Immune and Endothelial Mechanisms. GWAS summary statistics for preeclampsia, hypertension, SBP, and DBP were integrated to identify 10,843 preeclampsia-specific variants that were subsequently evaluated in cis-eQTL analyses using whole-blood RNA-sequencing data from 180 African American women (left). Cis-eQTL analyses identified 480 significant associations involving 277 variants and 192 genes (FDR ≤ 0.05), of which 69 eQTL-gene associations involving 35 variants and 14 genes replicated across five GTEx tissues, with strongest enrichment observed in vascular tissues, particularly artery tibial and artery aorta (center). Prioritized genes, including MICA, HLA-DPB1, SEMA4D, JUP, ZFP57, and TMEM204, converged on interconnected immune and endothelial pathways associated with systemic vascular dysfunction, impaired placentation, and inflammatory dysregulation in preeclampsia. Integration of long-read sequencing data further identified 66 high-LD SNP-SV-gene associations involving 12 replicated eQTL variants, 8 candidate structural variants, and 3 replicated genes, suggesting that structurally complex genomic regions may contribute to regulatory mechanisms not fully captured through conventional GWAS approaches alone. eQTL indicates expression quantitative trait locus; FDR, false discovery rate; GTEx, Genotype-Tissue Expression project; SBP, systolic blood pressure; DBP, diastolic blood pressure; LD, linkage disequilibrium; SV, structural variant.

Compound heterozygous SMN1 genotypes account for approximately 5% of spinal muscular atrophy (SMA) cases but are difficult to detect using conventional short-read sequencing due to near-identical homology between SMN1 and its paralog SMN2. We describe a patient with a decade-long diagnostic odyssey in whom whole exome and short-read whole genome sequencing failed to establish a diagnosis, despite a single SMN1 copy having been detected and dismissed as carrier status. The patient presented in childhood with progressive proximal limb weakness, tongue atrophy with fasciculations and electrophysiological evidence of chronic neurogenic disease. High-fidelity long-read whole genome sequencing resolved the SMN locus unambiguously, identifying a compound heterozygous genotype comprising an SMN1 gene deletion and a likely pathogenic missense variant (c.347 T>C; p.Ile116Thr) on the remaining allele, confirming SMA type 3. This case illustrates how long-read sequencing can resolve paralogous genomic regions intractable to short-read approaches, and highlights that a single detected SMN1 copy in a patient with a compatible phenotype warrants evaluation for a second sequence-level variant.

Background: Hypertension and preeclampsia are clinically distinct, yet biologically related conditions characterized by vascular dysfunction and elevated cardiovascular risk. Although genome-wide association studies (GWAS) have identified loci associated with blood pressure traits and preeclampsia, the functional mechanisms linking shared variants to gene regulation and clinical phenotypes remain unclear. Methods: We integrated GWAS summary statistics for hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), and preeclampsia to identify shared variants (p ≤ 1×10⁻⁴). Cis-expression quantitative trait loci (eQTL) analyses were performed in whole blood using RNA-seq data from 180 African American women. Significant associations (FDR ≤ 0.05) were evaluated for replication across vascular, metabolic, and endocrine tissues in the Genotype-Tissue Expression (GTEx) project. Associations between gene expression and blood pressure traits were also assessed. Results: We identified 4,792 shared GWAS variants, of which 4,663 were tested in eQTL analyses, yielding 1,837 significant variant-gene associations across 78 genes. Replication in GTEx confirmed 645 associations involving 24 genes, many showing cross-tissue regulatory effects. Three genes (C4B, HLA-C, and HLA-DQB1) demonstrated convergent evidence across GWAS, gene regulation, and expression-trait analyses. C4B expression was positively associated with hypertension and SBP, while HLA-C showed consistent negative associations with hypertension, SBP, and DBP. HLA-DQB1 expression was specifically associated with DBP, suggesting trait-specific effects. Conclusions: These findings highlight immune-related pathways as key mediators linking hypertension and preeclampsia. Integrating genetic, transcriptomic, and phenotypic data provides a framework for identifying functionally relevant loci and advancing mechanistic insights into cardiometabolic and pregnancy-related disorders. GRAPHICAL Shared genetic variants across hypertension, blood pressure traits, and preeclampsia converge on immune regulatory genes linking gene regulation to clinical phenotypes. GWAS summary statistics for hypertension, SBP, DBP, and preeclampsia were intersected to identify 4,792 shared variants, of which 4,663 were tested in cis-eQTL analyses in whole blood from 180 African American women ( left ). Shared variants regulate immune-related genes through cis-eQTL effects, yielding 1,837 associations involving 78 genes (FDR ≤ 0.05). Three convergent genes emerged: C4B (upregulated), HLA-C (downregulated), and HLA-DQB1 (upregulated), with 645 associations involving 24 genes replicated across eight tissues in GTEx ( center ). Expression-trait analyses confirmed that C4B expression was positively associated with hypertension and SBP, HLA-C expression was negatively associated with hypertension, SBP, and DBP, and HLA-DQB1 expression was specifically associated with DBP. These genes implicate complement activation, antigen presentation, and adaptive immunity as shared mechanisms contributing to vascular dysfunction in both hypertension and preeclampsia. eQTL indicates expression quantitative trait locus; FDR, false discovery rate; GTEx, Genotype-Tissue Expression project; SBP, systolic blood pressure; DBP, diastolic blood pressure; APC, antigen-presenting cell; TCR, T-cell receptor; MHC, major histocompatibility complex.

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions.

Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.