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

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journal contribution
posted on 30.07.2019, 13:20 by Paul J. Newcombe, Christopher P. Nelson, Nilesh J. Samani, Frank Dudbridge
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.

Funding

Paul J. Newcombe was funded by the UK Medical Research Council programme number MC_UU_00002/9 and also acknowledges support from the NIHR Cambridge BRC. Nilesh J. Samani is funded from BHF and is an NIHR senior investigator. Christopher P. Nelson is funded from British Heart Foundation (BHF). The authors also acknowledge use of DNA from the UK Blood Services collection of Common Controls (UKBS collection), funded by the Wellcome Trust Grant 076113/C/04/Z, the Wellcome Trust/JDRF Grant 061858, and the National Institutes of Health Research of England. The collection was established as part of the Wellcome Trust Case‐Control Consortium (funding for the project was provided by the Wellcome Trust under award 076113).

History

Citation

Genetic Epidemiology, 2019

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

VoR (Version of Record)

Published in

Genetic Epidemiology

Publisher

Wiley for International Genetic Epidemiology Society (IGES)

eissn

1098-2272

Acceptance date

30/05/2019

Copyright date

2019

Available date

30/07/2019

Publisher version

https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.22245

Language

en