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Deutelmoser, H.* ; Scherer, D.* ; Brenner, H.* ; Waldenberger, M. ; Suhre, K.* ; Kastenmüller, G. ; Lorenzo Bermejo, J.*

Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data.

Brief. Bioinform. 22:bbaa230 (2021)

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Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence of statistical results on a small number of individuals with phenotypes or genotypes divergent from the majority of the study population-typically comprised of outliers and high-leverage observations. Robust methods have been developed to constrain the influence of divergent observations and generate statistical results that apply to the bulk of study data, but they have rarely been applied to genetic association studies. In this article, we review, for newcomers to the field of robust statistics, a novel version of standard LASSO that utilizes the Huber loss function. We conduct comprehensive simulations and analyze real protein, metabolite, mRNA expression and genotype data to compare the stability of penalization, the cross-iteration concordance of the model, the false-positive and true-positive rates and the prediction accuracy of standard and robust Huber-LASSO. Although the two methods showed controlled false-positive rates ≤2.1% and similar true-positive rates, robust Huber-LASSO outperformed standard LASSO in the accuracy of predicted protein, metabolite and gene expression levels using individual SNP data. The conducted simulations and real-data analyses show that robust Huber-LASSO represents a valuable alternative to standard LASSO in genetic studies of molecular phenotypes.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Huber Loss Function ; Lasso ; Genetic Prediction ; Molecular Data ; Robust Statistics; Regression; Association; Selection; Atlas

ISSN (print) / ISBN 1467-5463

e-ISSN 1477-4054

Journal Briefings in Bioinformatics

Quellenangaben Volume: 22, Issue: 4, Article Number: bbaa230

Publisher Oxford University Press

Publishing Place Great Clarendon St, Oxford Ox2 6dp, England

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Epidemiology II (EPI2)
Institute of Computational Biology (ICB)

Grants National Institute on Aging
Biomedical Research Program at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation
European Union's Horizon 2020 research and innovation programme
Federal Ministry of Education and Research Germany (Bundesministerium fur Bildung und Forschung, BMBF)