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

• Published in: bioRxiv
• Main Authors: Hahn, Georg, Prokopenko, Dmitry, Hecker, Julian, Lutz, Sharon M., Mullin, Kristina, Tanzi, Rudolph E., DeSantis, Stacia, Lange, Christoph
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Laboratory 22.04.2024
• Edition: 1.1
• Subjects: Bioinformatics; Penalized regression; Cox proportional hazard; Alzheimer; Survival; Lasso
• ISSN: 2692-8205
• DOI: 10.1101/2024.04.18.590111

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 the 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, ten leading principal components, and selected genomic loci. We apply the lasso for Cox’s proportional hazards to a dataset 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 dataset of AD patients, thus enabling it to handle datasets with 60, 000 to 100, 000 subjects in less than one hour.