50 research outputs found
Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke
No full textGenetic factors have been implicated in stroke risk, but few replicated associations have been reported. We conducted a genome-wide association study (GWAS) for ischemic stroke and its subtypes in 3,548 affected individuals and 5,972 controls, all of European ancestry. Replication of potential signals was performed in 5,859 affected individuals and 6,281 controls. We replicated previous associations for cardioembolic stroke near PITX2 and ZFHX3 and for large vessel stroke at a 9p21 locus. We identified a new association for large vessel stroke within HDAC9 (encoding histone deacetylase 9) on chromosome 7p21.1 (including further replication in an additional 735 affected individuals and 28,583 controls) (rs11984041; combined P = 1.87 × 10<sup>−11</sup>; odds ratio (OR) = 1.42, 95% confidence interval (CI) = 1.28–1.57). All four loci exhibited evidence for heterogeneity of effect across the stroke subtypes, with some and possibly all affecting risk for only one subtype. This suggests distinct genetic architectures for different stroke subtypes
Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions
No full textArtificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems. 2023 The Author(s)Scopu
Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index
No full textThe phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ~4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity. © 2014 Hoggart et al
Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
No full textApproaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups
Modulation of genetic associations with serum urate levels by body-mass-index in humans
No full textWe tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.</p
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
No full textObesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation
Cardiovascular Risk Factors and MRI Markers of Cerebral Small Vessel Disease
No full textBackground and objectives: Cardiovascular risk factors have been implicated in the etiology of cerebral small vessel disease (CSVD); however, whether the associations are causal remains unclear in part due to the susceptibility of observational studies to reverse causation and confounding. Here, we use mendelian randomization (MR) to determine which cardiovascular risk factors are likely to be involved in the etiology of CSVD. Methods: We used data from large-scale genome-wide association studies of European ancestry to identify genetic proxies for blood pressure, blood lipids, body mass index (BMI), type 2 diabetes, smoking initiation, cigarettes per day, and alcohol consumption. MR was performed to assess their association with 3 neuroimaging features that are altered in CSVD (white matter hyperintensities [WMH], fractional anisotropy [FA], and mean diffusivity [MD]) using genetic summary data from the UK Biobank (N = 31,855). Our primary analysis used inverse-weighted median MR, with validation using weighted median, MR-Egger, and a pleiotropy-minimizing approach. Finally, multivariable MR was performed to study the effects of multiple risk factors jointly. Results: MR analysis showed consistent associations across all methods for higher genetically proxied systolic and diastolic blood pressures with WMH, FA, and MD and for higher genetically proxied BMI with WMH. There was weaker evidence for associations between total cholesterol, low-density lipoprotein, smoking initiation, pulse pressure, and type 2 diabetes liability and at least 1 CSVD imaging feature, but these associations were not reproducible across all validation methods used. Multivariable MR analysis for blood pressure traits found that the effect was primarily through genetically proxied diastolic blood pressure across all CSVD traits. Discussion: Genetic predisposition to higher blood pressure, primarily diastolic blood pressure, and to higher BMI is associated with a higher burden of CSVD, suggesting a causal role. Improved management and treatment of these risk factors could reduce the burden of CSVD
The Impact Of Green Finance And Fintech Mechanisms On Financial Stability In Advanced And Emerging Nations
No full textThis paper examines how green finance and financial technology (FinTech) mechanisms affect financial stability across advanced and emerging economies. We develop a comprehensive theoretical framework that links environmental finance initiatives and FinTech innovations to traditional financial system stability channels — credit risk, market risk, liquidity risk, and systemic risk. Using a panel dataset covering 40 countries (20 advanced and 20 emerging) over the period 2010–2024, we propose a set of empirical strategies to identify the direct and interaction effects of green finance adoption and FinTech penetration on macro prudential indicators and bank-level stability measures. Our baseline specification uses dynamic panel methods (system-GMM) and panel fixed effects with clustered standard errors. We supplement the baseline with event-study analyses around major regulatory or policy milestones (green bond issuance frameworks, FinTech sandbox launches), bank-level microdata regressions, and instrumental variable approaches to address endogeneity. We find evidence consistent with the hypothesis that mature green finance frameworks, when coupled with robust FinTech ecosystems, enhance financial stability by diversifying funding sources, improving risk pricing, and strengthening risk management — though benefits vary by country income level and institutional strength. The paper concludes with policy recommendations for harmonizing green finance incentives and FinTech regulation to promote resilient financial systems. This study examines the multifaceted influence of green finance and Financial Technology (FinTech) on the financial stability of both advanced and emerging economies, utilizing a comprehensive panel dataset from 2005 to 2022 covering 148 countries. We develop composite indices for financial stability, FinTech, and green finance to provide a robust empirical analysis, employing a two-step system Generalized Method of Moments (GMM) and bootstrapped panel quantile regression to address potential endogeneity and sample heterogeneity. Our findings indicate that FinTech and green finance positively affect financial stability in advanced nations. However, in emerging economies, while the overall interaction of FinTech and green finance (excluding the resource dimension) enhances financial stability, the environmental dimension of green finance may present risks due to industrial carbon policies. The study also confirms a negative impact of the COVID-19 pandemic on financial stability across all regions. These results provide novel insights into the context-specific dynamics of sustainable financial development and offer valuable policy recommendations for fostering resilient and low-carbon financial systems
The Role Of Green Finance In Attaining Environmental Sustainability Within ESG Performance In EU Countries
No full textThis study investigates the role of green finance in advancing the environmental dimension of Environmental, Social and Governance (ESG) performance across European Union (EU) member states. We assemble a panel dataset of 27 EU countries over 2010–2024 and develop a multi-pronged empirical strategy to estimate the effect of green finance instruments — green bonds, green lending, and taxonomy-aligned investments — on country-level and firm-level environmental performance measures. Using fixed-effects, system-GMM, and difference-in-differences designs around major EU policy milestones (notably the EU Taxonomy and the Sustainable Finance Disclosure Regulation, SFDR), we find that greater green finance depth is associated with statistically and economically significant improvements in environmental ESG scores, reductions in carbon intensity, and higher green investment shares. Heterogeneity analysis shows stronger effects in countries with robust regulatory frameworks and higher financial market depth. The paper offers policy recommendations for scaling green finance while addressing disclosure burdens and potential greenwashing risks. This paper examines the impact of green finance on the environmental dimension of ESG performance in EU countries from 2008 to 2020, using a spatial Durbin model and entropy methods. The study reveals a significant positive relationship between green finance and improved environmental outcomes within a country's ESG performance, suggesting that green finance helps channel financial resources to environmentally friendly projects. The findings support the EU's Sustainable Finance Strategy and emphasize the importance of coordinated financial policy for achieving environmental sustainability
Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways
No full textGlycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels
