Sci Rep
Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA.
Published: October 2019
We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.
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http://dx.doi.org/10.1038/s41598-019-51258-x | DOI Listing |
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Hematology-Oncology Service, Department of Medicine, Centre hospitalier de l'Université de Montréal (CHUM), 1000, rue Saint-Denis, Montreal, QC, Canada.
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Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
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School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
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View Article and Find Full Text PDFCRISPR J
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