An atlas of genetic scores to predict multi-omic traits

Yu Xu (), Scott C. Ritchie, Yujian Liang, Paul R. H. J. Timmers, Maik Pietzner, Loïc Lannelongue, Samuel A. Lambert, Usman A. Tahir, Sebastian May-Wilson, Carles Foguet, Åsa Johansson, Praveen Surendran, Artika P. Nath, Elodie Persyn, James E. Peters, Clare Oliver-Williams, Shuliang Deng, Bram Prins, Jian’an Luan, Lorenzo Bomba, Nicole Soranzo, Emanuele Angelantonio, Nicola Pirastu, E. Shyong Tai, Rob M. Dam, Helen Parkinson, Emma E. Davenport, Dirk S. Paul, Christopher Yau, Robert E. Gerszten, Anders Mälarstig, John Danesh, Xueling Sim, Claudia Langenberg, James F. Wilson, Adam S. Butterworth and Michael Inouye ()
Additional contact information
Yu Xu: University of Cambridge
Scott C. Ritchie: University of Cambridge
Yujian Liang: National University of Singapore and National University Health System
Paul R. H. J. Timmers: University of Edinburgh
Maik Pietzner: University of Cambridge School of Clinical Medicine
Loïc Lannelongue: University of Cambridge
Samuel A. Lambert: University of Cambridge
Usman A. Tahir: Beth Israel Deaconess Medical Center
Sebastian May-Wilson: University of Edinburgh
Carles Foguet: University of Cambridge
Åsa Johansson: Uppsala University
Praveen Surendran: University of Cambridge
Artika P. Nath: University of Cambridge
Elodie Persyn: University of Cambridge
James E. Peters: Imperial College London
Clare Oliver-Williams: University of Cambridge
Shuliang Deng: Beth Israel Deaconess Medical Center
Bram Prins: University of Cambridge
Jian’an Luan: University of Cambridge School of Clinical Medicine
Lorenzo Bomba: Wellcome Genome Campus
Nicole Soranzo: University of Cambridge
Emanuele Angelantonio: University of Cambridge
Nicola Pirastu: University of Edinburgh
E. Shyong Tai: National University of Singapore and National University Health System
Rob M. Dam: National University of Singapore and National University Health System
Helen Parkinson: European Bioinformatics Institute, Wellcome Genome Campus
Emma E. Davenport: Wellcome Genome Campus
Dirk S. Paul: University of Cambridge
Christopher Yau: University of Oxford
Robert E. Gerszten: Beth Israel Deaconess Medical Center
Anders Mälarstig: Karolinska Institutet
John Danesh: University of Cambridge
Xueling Sim: National University of Singapore and National University Health System
Claudia Langenberg: University of Cambridge School of Clinical Medicine
James F. Wilson: University of Edinburgh
Adam S. Butterworth: University of Cambridge
Michael Inouye: University of Cambridge

Nature, 2023, vol. 616, issue 7955, 123-131

Abstract: Abstract The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; 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), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show 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 the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK–STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.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.

Date: 2023
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DOI: 10.1038/s41586-023-05844-9

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