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

ABSTRACT: 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 be

SUBMITTER: Deutelmoser H 

PROVIDER: S-EPMC8293825 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature