rs11968293 - SLC35F1

Magnitude 2.0 · 2 studies on file

Reported associations

• Genomic and transcriptomic analyses of aortic stenosis enhance therapeutic target discovery and disease prediction - Nature genetics (2026) · Small AM, Yang TY, Itoh S, Thériault S, Dufresne L, Kurosawa R, Komuro I, Matsuda K, Vy HMT, Farber-Eger EH, Shaffer LL, Boulier KM, Corey KM, Ramaker ME, Laporte F, Schott JJ, Le Scouarnec S, Singh SA, Sonawane AR, Smith HA, Rafaels N, Ghouse J, Raja AA, Ostrowski SR, Sørensen E, Mikkelsen C, Pedersen OB, Erikstrup C, Ullum H, Sveinbjornsson G, Gudbjartsson DF, Abner E, Lee J, Ganna A, Nowak-Göttl U, Finer S, Schumacher J, Maj C, Al-Kassou B, Nickenig G, Trenkwalder T, Dreβen M, Krane M, Nöthen MM, Moksnes MR, Brumpton BM, Knight S, Knowlton KU, Nadauld L, Debiec R, Musameh MD, Braund PS, Nelson CP, Czuba T, Melander O, Selvaraj MS, Koyama S, Bhukar R, Ruan Y, Ljungberg J, Damrauer SM, Levin MG, Franke A, Berger K, Ruff CT, Melloni GEM, Kamanu FK, Ito K, Do R, Loos RJF, Schunkert H, Wells QS, Shah SH, Le Tourneau T, Messika-Zeitoun D, Gignoux C, Bundgaard H, Larsson SC, Michaëlsson K, Holm H, Helgadottir A, Esko T, van Heel DA, Mathieu P, Samani NJ, Smith JG, Söderberg S, Rader DJ, Marston NA, Sabatine MS, Pasaniuc B, Cho K, Wilson PWF, O'Donnell CJ, Stefansson K, Bossé Y, Aikawa E, Engert JC, Peloso GM, Natarajan P, Thanassoulis G · PubMed 41419686

  ABSTRACT: Aortic stenosis (AS) is a common valvular heart disease and has no pharmacological therapies. We performed a multi-ancestry genome-wide association meta-analysis of 86,864 AS cases among 2,853,408 individuals, discovering 241 autosomal independent risk loci and 3 X chromosome risk loci. We additionally performed sex-stratified and ancestry-stratified genome-wide association studies (GWASs), identifying an additional 5 sex-specific risk loci, 11 risk loci in European ancestry individuals and 1 risk locus in African ancestry individuals. We also performed a transcriptome-wide association study using expression quantitative trait loci from human aortic valves, discovering 54 new genes for which genetically predicted expression influences the risk of AS. We then generated a new polyg

• Cross-modal autoencoder framework learns holistic representations of cardiovascular state - Nature communications (2023) · Radhakrishnan A, Friedman SF, Khurshid S, Ng K, Batra P, Lubitz SA, Philippakis AA, Uhler C · PubMed 37105979

  ABSTRACT: A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop

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