Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies

被引：268

作者：

Chatterjee, Nilanjan ^{[1
]}

Wheeler, Bill ^{[2
]}

Sampson, Joshua ^{[1
]}

Hartge, Patricia ^{[1
]}

Chanock, Stephen J. ^{[1
]}

Park, Ju-Hyun ^{[1
,3
]}

机构：

[1] NCI, Div Canc Epidemiol & Genet, NIH, US Dept HHS, Rockville, MD USA

[2] Informat Management Syst, Rockville, MD USA

[3] Dongguk Univ Seoul, Dept Stat, Seoul, South Korea

来源：

NATURE GENETICS | 2013年 / 45卷 / 04期

关键词：

GENETIC RISK; SUSCEPTIBILITY LOCI; MISSING HERITABILITY; BREAST-CANCER; VARIANTS; DISEASE; IDENTIFICATION; PROPORTION; REGRESSION; SELECTION;

D O I：

10.1038/ng.2579

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

We report a new method to estimate the predictive performance of polygenic models for risk prediction and assess predictive performance for ten complex traits or common diseases. Using estimates of effect-size distribution and heritability derived from current studies, we project that although 45% of the variance of height has been attributed to SNPs, a model trained on one million people may only explain 33.4% of variance of the trait. Models based on current studies allow for identification of 3.0%, 1.1% and 7.0% of the populations at twofold or higher than average risk for type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate these percentages to 18.8%, 6.1% and 12.2%, respectively. The utility of polygenic models for risk prediction will depend on achievable sample sizes for the training data set, the underlying genetic architecture and the inclusion of information on other risk factors, including family history.

引用

页码：400 / 405

页数：6

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