Expressive

rs10744777 - ALDH2

Magnitude 2.2 · 5 studies on file

Reported associations

  • The Genetic Determinants of Aortic Distention. - Journal of the American College of Cardiology (2023) · Pirruccello JP, Rämö JT, Choi SH, Chaffin MD, Kany S, Nekoui M, Chou EL, Jurgens SJ, Friedman SF, Juric D, Stone JR, Batra P, Ng K, Philippakis AA, Lindsay ME, Ellinor PT · PubMed 37019578

    As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous peripheral blood delivery. Systolic distention and diastolic recoil conserve energy and are enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and vascular disease. In this study, we sought to discover epidemiologic correlates and genetic determinants of aortic distensibility and strain. We trained a deep learning model to quantify thoracic aortic area throughout the cardiac cycle from cardiac magnetic resonance images and calculated aortic distensibility and strain in 42,342 UK Biobank participants. Descending aortic distensibility was inversely associated with future incidence o

  • The NINDS Stroke Genetics Network: a genome-wide association study of ischemic stroke and its subtypes - Unknown journal (n.d.) · Unknown authors · PubMed 26708676

    ABSTRACT: Summary Introduction The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading approach to the identification of novel biological pathways for human disease. To date, GWAS have had been limited by relatively small sample sizes and yielded relatively few loci associated with ischemic stroke The National Institute of Neurological Disorders Stroke Genetics Network (NINDS-SiGN) is an international consortium that has taken a systematic approach to phenotyping and produced the largest ischemic stroke GWAS to date. Methods In order to identify genetic loci associated with ischemic stroke, we performed a two-stage genome-wide association study. The first stage consisted of 16,851 cases with state-of-the-art phenotyping and 32,473 stroke-free

  • Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture - Unknown journal (n.d.) · Unknown authors · PubMed 41629584

    ABSTRACT: Background Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into disease etiology and risk. Methods In this study, we segment the heart, aorta, and vena cava from abdominal magnetic resonance imaging (MRI) scans using deep learning. We generate six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants, and explore their associations with disease outcomes, as well as genetic and environmental factors. Results Here we show concordance between our IDPs and related IDPs from cardiac magnetic resonance imaging, the current gold standard, and replicate previous findings related to sex differences and age-related changes in

  • Deep learning enables genetic analysis of the human thoracic aorta - Unknown journal (n.d.) · Unknown authors · PubMed 34837083

    ABSTRACT: Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests, and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank p

  • Multi-trait GWAS for diverse ancestries: mapping the knowledge gap - Unknown journal (n.d.) · Unknown authors · PubMed 38627641

    ABSTRACT: Background Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. Methods Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits acro

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.