A flexible and parallelizable approach to genome-wide polygenic risk scores.
John Wiley & Sons Inc.
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Newcombe, P., Nelson, C. P., Samani, N. J., & Dudbridge, F. (2019). A flexible and parallelizable approach to genome-wide polygenic risk scores.. Genetic epidemiology, 43 (7), 730-741. https://doi.org/10.1002/gepi.22245
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 parallelisable since block-wise analyses in step 1 can be distributed across a high performance computing cluster, and flexible, since sparsity and heritability is 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 7 traits in the WTCCC as well as meta-GWAS summaries for Type 1 Diabetes, Coronary Artery Disease, and Schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for Type 1 Diabetes, 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.
Humans, Diabetes Mellitus, Type 1, Genetic Predisposition to Disease, Area Under Curve, Risk Factors, Case-Control Studies, ROC Curve, Schizophrenia, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Models, Genetic, Coronary Artery Disease, Genome-Wide Association Study
Wellcome Trust (076113/C/04/Z)
Wellcome Trust (061858/Z/00/E)
External DOI: https://doi.org/10.1002/gepi.22245
This record's URL: https://www.repository.cam.ac.uk/handle/1810/294707
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/