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A flexible and parallelizable approach to genome-wide polygenic risk scores.

Authors
  • Newcombe, Paul J1
  • Nelson, Christopher P2, 3
  • Samani, Nilesh J2, 3
  • Dudbridge, Frank4
  • 1 MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK.
  • 2 Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield Hospital, University of Leicester, Leicester, UK.
  • 3 NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK.
  • 4 Department of Health Sciences, Centre for Medicine, University of Leicester, Leicester, UK.
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Oct 01, 2019
Volume
43
Issue
7
Pages
730–741
Identifiers
DOI: 10.1002/gepi.22245
PMID: 31328830
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes. © 2019 The Authors. Genetic Epidemiology Published by Wiley Periodicals, Inc.

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