Efficient Implementation of Penalized Regression for Genetic Risk Prediction - Archive ouverte HAL Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Efficient Implementation of Penalized Regression for Genetic Risk Prediction

Florian Privé 1 Hugues Aschard 2 Michael Blum 1
1 TIMC-BCM - Biologie Computationnelle et Mathématique
TIMC - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : Polygenic Risk Scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The "Clumping+Thresholding" (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T. In this paper, we present an efficient method for the joint estimation of SNP effects using individual-level data, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. We also provide an implementation of penalized linear regression for quantitative traits. We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. Overall, we find that PLR achieves equal or higher predictive performance than C+T in most scenarios considered, while being scalable to biobank data. In particular, we find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, in simulations, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC values of 89% and of 82.5%. Applying penalized linear regression to 350,000 individuals of the UK Biobank, we predict height with a larger correlation than with the best prediction of C+T (∼65% instead of ∼55%), further demonstrating its scalability and strong predictive power, even for highly polygenic traits. Moreover, using 150,000 individuals of the UK Biobank, we are able to predict breast cancer better than C+T, fitting PLR in a few minutes only. In conclusion, this paper demonstrates the feasibility and relevance of using penalized regression for PRS computation when large individual-level datasets are available, thanks to the efficient implementation available in our R package bigstatsr.
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [39 références]  Voir  Masquer  Télécharger

Contributeur : Catherine Zoppis <>
Soumis le : mardi 10 septembre 2019 - 17:12:14
Dernière modification le : mardi 8 décembre 2020 - 10:41:57
Archivage à long terme le : : vendredi 7 février 2020 - 23:40:18


Publication financée par une institution


Distributed under a Creative Commons Paternité 4.0 International License




Florian Privé, Hugues Aschard, Michael Blum. Efficient Implementation of Penalized Regression for Genetic Risk Prediction. Genetics, Genetics Society of America, 2019, 212 (1), pp.65-74. ⟨10.1534/genetics.119.302019⟩. ⟨hal-02270812⟩



Consultations de la notice


Téléchargements de fichiers