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While the penalization parameter \u03bb 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\u2014typically comprised of outliers and high-leverage observations.<\/jats:p><jats:p>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.<\/jats:p><jats:p>Although the two methods showed controlled false-positive rates \u22642.1% 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaa230","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T11:09:30Z","timestamp":1598440170000},"source":"Crossref","is-referenced-by-count":13,"title":["Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5633-1472","authenticated-orcid":false,"given":"Heike","family":"Deutelmoser","sequence":"first","affiliation":[{"name":"Statistical Genetics Research Group, Institute of Medical Biometry and Informatics, Heidelberg University, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4430-296X","authenticated-orcid":false,"given":"Dominique","family":"Scherer","sequence":"additional","affiliation":[{"name":"Statistical Genetics Research Group, Institute of Medical Biometry and Informatics, Heidelberg University, Germany"}]},{"given":"Hermann","family":"Brenner","sequence":"additional","affiliation":[{"name":"Division of Preventive Oncology and the Division of Clinical Epidemiology and Aging Research at the German Cancer Research Center, Heidelberg, Germany"}]},{"given":"Melanie","family":"Waldenberger","sequence":"additional","affiliation":[{"name":"Research Unit Molecular Epidemiology and Institute of Epidemiology, Helmholtz Center Munich, Germany"}]},{"name":"INTERVAL study","sequence":"additional","affiliation":[]},{"given":"Karsten","family":"Suhre","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine and the Director of the Bioinformatics and Virtual Metabolomics Core at the Cornell campus in Doha, Qatar"}]},{"given":"Gabi","family":"Kastenm\u00fcller","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Center Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6568-5333","authenticated-orcid":false,"given":"Justo","family":"Lorenzo Bermejo","sequence":"additional","affiliation":[{"name":"Statistical Genetics Research Group at the Institute of Medical Biometry and Informatics, Heidelberg University, Germany"}]}],"member":"286","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"2021072112100607900_ref1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc B Methodol"},{"key":"2021072112100607900_ref2","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1093\/bioinformatics\/btaa229","article-title":"Prioritizing 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