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Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.

Authors
  • Chun, Sung1
  • Imakaev, Maxim1
  • Hui, Daniel2
  • Patsopoulos, Nikolaos A2
  • Neale, Benjamin M3
  • Kathiresan, Sekar4
  • Stitziel, Nathan O5
  • Sunyaev, Shamil R6
  • 1 Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Altius Institute for Biomedical Sciences, Seattle, WA 98121, USA.
  • 2 Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham & Women's Hospital, Boston, MA 02115, USA.
  • 3 Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA.
  • 4 Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.
  • 5 Cardiovascular Division, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA; McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63110, USA. Electronic address: [email protected]
  • 6 Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Altius Institute for Biomedical Sciences, Seattle, WA 98121, USA. Electronic address: [email protected]
Type
Published Article
Journal
The American Journal of Human Genetics
Publisher
Elsevier
Publication Date
Jul 02, 2020
Volume
107
Issue
1
Pages
46–59
Identifiers
DOI: 10.1016/j.ajhg.2020.05.004
PMID: 32470373
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments. Copyright © 2020 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

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