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Biobank-scale association studies that include Hispanic/Latino(a) (HL) and African American (AA) populations remain underrepresented, limiting the discovery of disease associated genetic factors in these groups. We present here a systematic comparison of phenome-wide admixture mapping (AM) and genome-wide association (GWAS) using data from the diverse Bio biobank in New York City. Our analysis highlights 77 genome-wide significant AM signals, 48 of which were not detected by GWAS, emphasizing the complementary nature of these two approaches. AM-tagged variants show significantly higher minor allele frequency and population differentiation (Fst) while GWAS demonstrated higher odds ratios, underscoring the distinct genetic architecture identified by each method. This study offers a comprehensive phenome-wide AM resource, demonstrating its utility in uncovering novel genetic associations in underrepresented populations, particularly for variants missed by traditional GWAS approaches.
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http://dx.doi.org/10.1101/2024.11.18.24317494 | DOI Listing |
BMJ Open
September 2025
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
Objective: This study validates the previously tested Screening for Poverty And Related social determinants to improve Knowledge of and access to resources ('SPARK Tool') against comparison questions from well-established national surveys (Post Survey Questionnaire (PSQ)) to inform the development of a standardised tool to collect patients' demographic and social needs data in healthcare.
Design: Cross-sectional study.
Setting: Pan-Canadian study of participants from four Canadian provinces (SK, MB, ON and NL).
Pediatr Exerc Sci
September 2025
Division of Movement Science and Exercise Therapy, Department of Exercise, Sport and Lifestyle Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch,South Africa.
Background: Juvenile idiopathic arthritis is an autoimmune condition of multifactorial etiology resulting in chronic inflammatory joint disease, which may be associated with systemic manifestations. Therapeutic exercise is essential to counteract physical impairments, which requires the implementation of outcome measures (OMs) in research and practice as they provide meaningful results for research efficacy, exercise program evaluation and quality, medication tolerance, and patient improvement.
Purpose: To assess the types of OMs implemented in exercise randomized controlled trials related to the juvenile idiopathic arthritis cohort and the psychometric properties and age appropriateness of the implemented OMs.
Appl Clin Inform
September 2025
Pediatric Critical Care, Stanford University School of Medicine, Stanford, United States.
Background: Time spent in the electronic health record (EHR) is an important measure of clinical activity. Vendor-derived EHR use metrics may not correspond to actual EHR experience. Raw EHR audit logs enable customized EHR use metrics, but translating discrete timestamps to time intervals is challenging.
View Article and Find Full Text PDFBiomed J
September 2025
Department of Obstetrics and Gynecology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113 8655, Japan. Electronic address:
Background: Pelvic floor disorders (PFDs) severely and negatively impact on quality of life, affecting physical, psychological, and social well-being. Historically, PFDs have been managed within single-specialty frameworks, yet the complexity of these conditions often necessitates a comprehensive, multidisciplinary team (MDT) approach. This systematic review assesses the effectiveness of MDT strategies in improving outcomes for individuals with PFDs, aiming to identify the benefits and potential advantages of integrated, multi-specialty care for these complex conditions.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.