Evaluation of the lasso and the elastic net in genome-wide association studies

The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have c...

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Main Authors:	Waldmann, Patrik, Mészáros, Gábor, Gredler, Birgit, Fuerst, Christian, Sölkner, Johann
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850240/

id	pubmed-3850240
recordtype	oai_dc
spelling	pubmed-38502402013-12-20 Evaluation of the lasso and the elastic net in genome-wide association studies Waldmann, Patrik Mészáros, Gábor Gredler, Birgit Fuerst, Christian Sölkner, Johann Genetics The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have compared the statistical performance of two methods (the least absolute shrinkage and selection operator—lasso and the elastic net) on two simulated data sets and one real data set from a 50 K genome-wide single nucleotide polymorphism (SNP) panel of 5570 Fleckvieh bulls. The first simulated data set displays moderate to high linkage disequilibrium between SNPs, whereas the second simulated data set from the QTLMAS 2010 workshop is biologically more complex. We used cross-validation to find the optimal value of regularization parameter λ with both minimum MSE and minimum MSE + 1SE of minimum MSE. The optimal λ values were used for variable selection. Based on the first simulated data, we found that the minMSE in general picked up too many SNPs. At minMSE + 1SE, the lasso didn't acquire any false positives, but selected too few correct SNPs. The elastic net provided the best compromise between few false positives and many correct selections when the penalty weight α was around 0.1. However, in our simulation setting, this α value didn't result in the lowest minMSE + 1SE. The number of selected SNPs from the QTLMAS 2010 data was after correction for population structure 82 and 161 for the lasso and the elastic net, respectively. In the Fleckvieh data set after population structure correction lasso and the elastic net identified from 1291 to 1966 important SNPs for milk fat content, with major peaks on chromosomes 5, 14, 15, and 20. Hence, we can conclude that it is important to analyze GWAS data with both the lasso and the elastic net and an alternative tuning criterion to minimum MSE is needed for variable selection. Frontiers Media S.A. 2013-12-04 /pmc/articles/PMC3850240/ /pubmed/24363662 http://dx.doi.org/10.3389/fgene.2013.00270 Text en Copyright © 2013 Waldmann, Mészáros, Gredler, Fuerst and Sölkner. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Waldmann, Patrik Mészáros, Gábor Gredler, Birgit Fuerst, Christian Sölkner, Johann
spellingShingle	Waldmann, Patrik Mészáros, Gábor Gredler, Birgit Fuerst, Christian Sölkner, Johann Evaluation of the lasso and the elastic net in genome-wide association studies
author_facet	Waldmann, Patrik Mészáros, Gábor Gredler, Birgit Fuerst, Christian Sölkner, Johann
author_sort	Waldmann, Patrik
title	Evaluation of the lasso and the elastic net in genome-wide association studies
title_short	Evaluation of the lasso and the elastic net in genome-wide association studies
title_full	Evaluation of the lasso and the elastic net in genome-wide association studies
title_fullStr	Evaluation of the lasso and the elastic net in genome-wide association studies
title_full_unstemmed	Evaluation of the lasso and the elastic net in genome-wide association studies
title_sort	evaluation of the lasso and the elastic net in genome-wide association studies
description	The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have compared the statistical performance of two methods (the least absolute shrinkage and selection operator—lasso and the elastic net) on two simulated data sets and one real data set from a 50 K genome-wide single nucleotide polymorphism (SNP) panel of 5570 Fleckvieh bulls. The first simulated data set displays moderate to high linkage disequilibrium between SNPs, whereas the second simulated data set from the QTLMAS 2010 workshop is biologically more complex. We used cross-validation to find the optimal value of regularization parameter λ with both minimum MSE and minimum MSE + 1SE of minimum MSE. The optimal λ values were used for variable selection. Based on the first simulated data, we found that the minMSE in general picked up too many SNPs. At minMSE + 1SE, the lasso didn't acquire any false positives, but selected too few correct SNPs. The elastic net provided the best compromise between few false positives and many correct selections when the penalty weight α was around 0.1. However, in our simulation setting, this α value didn't result in the lowest minMSE + 1SE. The number of selected SNPs from the QTLMAS 2010 data was after correction for population structure 82 and 161 for the lasso and the elastic net, respectively. In the Fleckvieh data set after population structure correction lasso and the elastic net identified from 1291 to 1966 important SNPs for milk fat content, with major peaks on chromosomes 5, 14, 15, and 20. Hence, we can conclude that it is important to analyze GWAS data with both the lasso and the elastic net and an alternative tuning criterion to minimum MSE is needed for variable selection.
publisher	Frontiers Media S.A.
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850240/
_version_	1612034096298459136

Evaluation of the lasso and the elastic net in genome-wide association studies

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