Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Robert M. Maier, Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M. Ruderfer, Eli A. Stahl, Stephan Ripke, Naomi R. Wray, Jian Yang, Peter M. Visscher, Matthew R. Robinson, Andreas J. Forstner, Andrew Mcquillin, Vassily Trubetskoy, Weiqing Wang, Yunpeng Wang, Jonathan R.I. Coleman, Héléna A. Gaspar, Christiaan A. De Leeuw, Jennifer M. Whitehead PavlidesLoes M. Olde Loohuis, Tune H. Pers, Phil H. Lee, Alexander W. Charney, Amanda L. Dobbyn, Laura Huckins, James Boocock, Claudia Giambartolomei, Panos Roussos, Niamh Mullins, Swapnil Awasthi, Esben Agerbo, Thomas D. Als, Carsten Bøcker Pedersen, Jakob Grove, Ralph Kupka, Eline J. Regeer, Adebayo Anjorin, Miquel Casas, Pamela B. Mahon, Judith Allardyce, Valentina Escott-Price, Liz Forty, Christine Fraser, Manolis Kogevinas, Josef Frank, Fabian Streit, Jana Strohmaier, Jens Treutlein, Stephanie H. Witt, James L. Kennedy, John S. Strauss, Julie Garnham, Claire O'donovan, Claire Slaney, Stacy Steinberg, Thorgeir E. Thorgeirsson, Martin Hautzinger, Michael Steffens, Roy H. Perlis, Cristina Sánchez-Mora, Maria Hipolito, William B. Lawson, Evaristus A. Nwulia, Shawn E. Levy, Tatiana M. Foroud, Stéphane Jamain, Allan H. Young, James D. Mckay, Diego Albani, Peter Zandi, James B. Potash, Peng Zhang, J. Raymond Depaulo, Sarah E. Bergen, Anders Juréus, Robert Karlsson, Radhika Kandaswamy, Peter Mcguffin, Margarita Rivera, Jolanta Lissowska, Cristiana Cruceanu, Susanne Lucae, Pablo Cervantes, Monika Budde, Katrin Gade, Urs Heilbronner, Marianne Giørtz Pedersen, Derek W. Morris, Cynthia Shannon Weickert, Thomas W. Weickert, Donald J. Macintyre, Jacob Lawrence, Torbjørn Elvsåshagen, Olav B. Smeland, Srdjan Djurovic, Simon Xi, Elaine K. Green, Piotr M. Czerski, Joanna Hauser

Research output: Contribution to journalArticleResearch

40 Citations (Scopus)

Abstract

© 2018 The Author(s). Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
Original languageEnglish
Article number989
JournalNature Communications
Volume9
DOIs
Publication statusPublished - 1 Dec 2018

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