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Sci Rep. 2017 Oct 4;7(1):12665. doi: 10.1038/s41598-017-13056-1.

A machine-learning heuristic to improve gene score prediction of polygenic traits.

Author information

1
Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada. pareg@mcmaster.ca.
2
Population Genomics Program, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada. pareg@mcmaster.ca.
3
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada. pareg@mcmaster.ca.
4
Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.
5
Department of Statistical Sciences, University of Toronto, Toronto, Canada.

Abstract

Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R 2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10-16) and BMI (p < 1.57 × 10-4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

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