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An atlas of genetic scores to predict multi-omic traits

Authors
Yu Xu,Scott C Ritchie
Yujian Liang,Paul R. H. J. Timmers,Maik Pietzner,Loic Lannelongue,Samuel A. Lambert,Usman A. Tahir,Sebastian May-Wilson,&Aringsa Johansson,Praveen Surendran,Artika P Nath,Elodie Persyn,James E. Peters,Clare Oliver-Williams,Shuliang Deng,Bram Prins,Carles Foguet,Jian'an Luan,Lorenzo Bomba,Nicole Soranzo,Emanuele Di Angelantonio,Nicola Pirastu,E Shyong Tai,Rob M van Dam,Emma E Davenport,Dirk S. Paul,Christopher Yau,Robert E. Gerszten,Anders M&aumllarstig,John Danesh,Xueling Sim,Claudia Langenberg,James F Wilson,Adam S. Butterworth,Michael Inouye,E Tai,Rob Dam,Anders Mälarstig,Adam Butterworth,Emma Davenport,James Wilson,Scott Ritchie,Paul Timmers,Loïc Lannelongue,Samuel Lambert,Usman Tahir,Åsa Johansson,Artika Nath,James Peters,Clare Oliver‐Williams,Jian’an Luan,Emanuele Angelantonio,E. Tai,Dirk Paul,Robert Gerszten,Anders Hamsten
+55 authors
,Michael Weedon
Published
Apr 17, 2022
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Abstract

Abstract Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal ( OmicsPred.org ) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

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