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Title: Gene-SCOUT: identifying genes with similar continuous trait fingerprints from phenome-wide association analyses.
Austin Authors: Middleton, Lawrence;Harper, Andrew R;Nag, Abhishek;Wang, Quanli;Reznichenko, Anna;Vitsios, Dimitrios;Petrovski, Slavé
Affiliation: Medicine (University of Melbourne)
Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA..
Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden..
Issue Date: 26-Apr-2022
Date: 2022
Publication information: Nucleic Acids Research 2022; 50(8):4289-4301
Abstract: Large-scale phenome-wide association studies performed using densely-phenotyped cohorts such as the UK Biobank (UKB), reveal many statistically robust gene-phenotype relationships for both clinical and continuous traits. Here, we present Gene-SCOUT, a tool used to identify genes with similar continuous trait fingerprints to a gene of interest. A fingerprint reflects the continuous traits identified to be statistically associated with a gene of interest based on multiple underlying rare variant genetic architectures. Similarities between genes are evaluated by the cosine similarity measure, to capture concordant effect directionality, elucidating clusters of genes in a high dimensional space. The underlying gene-biomarker population-scale association statistics were obtained from a gene-level rare variant collapsing analysis performed on over 1500 continuous traits using 394 692 UKB participant exomes, with additional metabolomic trait associations provided through Nightingale Health's recent study of 121 394 of these participants. We demonstrate that gene similarity estimates from Gene-SCOUT provide stronger enrichments for clinical traits compared to existing methods. Furthermore, we provide a fully interactive web-resource ( to explore the pre-calculated exome-wide similarities. This resource enables a user to examine the biological relevance of the most similar genes for Gene Ontology (GO) enrichment and UKB clinical trait enrichment statistics, as well as a detailed breakdown of the traits underpinning a given fingerprint.
DOI: 10.1093/nar/gkac274
ORCID: 0000-0002-8939-5445
Journal: Nucleic Acids Research
PubMed URL: 35474393
PubMed URL:
Type: Journal Article
Appears in Collections:Journal articles

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