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IntroductionThis study addresses the rising cardiovascular disease (CVD) rates in the United Arab Emirates (UAE) by investigating the occurrence and impact of genetic variants in CVD-related genes.MethodsWe collected all genes linked to heritable CVD from public and diagnostic databases and mapped them to their corresponding biological processes and molecular pathways. We then evaluated the types and burden of genetic variants within these genes in 343 individuals from the Emirati Mendelian Study Cohort and 3,007 national electronic health records.ResultsWe identified a total of 735 genes associated with heritable CVD, covering a range of cardiovascular conditions. Enrichment analysis revealed key biological processes and pathways, including Apelin, FoxO, and Ras signaling, that are implicated across all forms of heritable CVD. Analysis of a UAE cohort of 3,350 individuals showed a predominance of rare and unique CVD variants specific to the population. The study found a significant burden of pathogenic variants in families with CVD within the Emirati Mendelian cohort and re-assessed the pathogenicity of 693 variants from national health records, leading to the discovery of new CVD-causing variants.DiscussionThis study underscores the importance of continuously updating our understanding of genes and pathways related to CVD. It also highlights the significant underrepresentation of the UAE population in public databases and clinical literature on CVD genetics, offering valuable insights that can inform future research and intervention strategies.

The article discusses the MCLS (Midwest Collaborative for Library Services) Spring eResources Meeting 2024 that was held virtually on April 12, 2024. Key topics discussed included the strategic planning process of the Midwest Collaborative for Library Services (MCLS), evaluations of electronic subscription packages and updates on the MeL databases contract. Attendees included various library professionals, Stephanie Davis, Scott Garrison and Cheryl Hayley.

In statistics, normal distribution has a fundamental role. This study analyses the statistical power of eleven tests of normality: Shapiro--Wilk (SW), Shapiro--Francia (SF), Filliben (F), Kolmogorov--Smirnov (KS), Anderson--Darling (AD), Kuiper (K), Cramér--von Mises (CvM), D'Agostino--Pearson (DP), Jarque--Bera (JB), Chi-square (CS) and Vasicek (V) test. The power comparison is obtained by sampling data from 5 distribution groups with Monte Carlo simulation: support (-∞, ∞) symmetric and asymmetric, support (0, ∞), support (0, 1), and contaminated distributions. We focus on the smaller 10, 20, 30, ..., 100 sample sizes given that econometrics often model macroeconomic and financial time- series within this range of samples. Results suggest that the regression and correlation-based normality tests, such as the Shapiro-Wilk and Filliben tests perform well on distributions with support (-∞, ∞). For distributions with (0, ∞) and (0, 1) support the Shapiro-Wilk and the Vasicek tests have the highest power while on contaminated samples the Jarque--Bera test outperforms them. The results could help statisticians and econometrics to apply the best-performing normality test having a prior assumption or information on the support of the data-generating process. [ABSTRACT FROM AUTHOR]