Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model

被引:2
|
作者
Hahn, Georg [1 ]
Prokopenko, Dmitry [2 ]
Hecker, Julian [3 ,4 ]
Lutz, Sharon M. [1 ]
Mullin, Kristina [2 ]
Tanzi, Rudolph E. [2 ]
Desantis, Stacia [5 ]
Lange, Christoph [1 ]
机构
[1] Harvard TH Chan Sch Publ Hlth, 677 Huntington Ave, Boston, MA 02115 USA
[2] Massachusetts Gen Hosp, McCance Ctr Brain Hlth, Dept Neurol, Genet & Aging Res Unit, Boston, MA USA
[3] Brigham & Womens Hosp, Channing Div Network Med, Boston, MA USA
[4] Harvard Med Sch, Boston, MA USA
[5] Univ Texas Hlth Sci Ctr, Houston, TX USA
基金
加拿大健康研究院; 美国国家卫生研究院; 美国国家科学基金会;
关键词
Alzheimer; Cox proportional hazard; lasso; penalized regression; survival; ADAPTIVE LASSO; RISK; REGRESSION; DISEASE; SELECTION;
D O I
10.1002/gepi.22581
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.
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页数:11
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