Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

Abstract 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 ar...

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Autores principales:	Louis Lello, Timothy G. Raben, Soke Yuen Yong, Laurent C. A. M. Tellier, Stephen D. H. Hsu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d0bbe05be1ad42e9b5b0912d11a4bc34
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spelling	oai:doaj.org-article:d0bbe05be1ad42e9b5b0912d11a4bc342021-12-02T15:09:33ZGenomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer10.1038/s41598-019-51258-x2045-2322https://doaj.org/article/d0bbe05be1ad42e9b5b0912d11a4bc342019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-51258-xhttps://doaj.org/toc/2045-2322Abstract 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.Louis LelloTimothy G. RabenSoke Yuen YongLaurent C. A. M. TellierStephen D. H. HsuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-16 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Louis Lello Timothy G. Raben Soke Yuen Yong Laurent C. A. M. Tellier Stephen D. H. Hsu Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
description	Abstract 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.
format	article
author	Louis Lello Timothy G. Raben Soke Yuen Yong Laurent C. A. M. Tellier Stephen D. H. Hsu
author_facet	Louis Lello Timothy G. Raben Soke Yuen Yong Laurent C. A. M. Tellier Stephen D. H. Hsu
author_sort	Louis Lello
title	Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
title_short	Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
title_full	Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
title_fullStr	Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
title_full_unstemmed	Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer
title_sort	genomic prediction of 16 complex disease risks including heart attack, diabetes, breast and prostate cancer
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/d0bbe05be1ad42e9b5b0912d11a4bc34
work_keys_str_mv	AT louislello genomicpredictionof16complexdiseaserisksincludingheartattackdiabetesbreastandprostatecancer AT timothygraben genomicpredictionof16complexdiseaserisksincludingheartattackdiabetesbreastandprostatecancer AT sokeyuenyong genomicpredictionof16complexdiseaserisksincludingheartattackdiabetesbreastandprostatecancer AT laurentcamtellier genomicpredictionof16complexdiseaserisksincludingheartattackdiabetesbreastandprostatecancer AT stephendhhsu genomicpredictionof16complexdiseaserisksincludingheartattackdiabetesbreastandprostatecancer
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Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

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