LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms...

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Autores principales:	Cheng Gao, Hairong Wei, Kui Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	eQTL mapping proximal gradient method cis-eQTL trans-eQTL penalized regression Genetics QH426-470
Acceso en línea:	https://doaj.org/article/b652412a4fe44266b4538b580c5ae2ab
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spelling	oai:doaj.org-article:b652412a4fe44266b4538b580c5ae2ab2021-11-17T17:00:17ZLORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression1664-802110.3389/fgene.2021.690926https://doaj.org/article/b652412a4fe44266b4538b580c5ae2ab2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.690926/fullhttps://doaj.org/toc/1664-8021Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.Cheng GaoHairong WeiKui ZhangFrontiers Media S.A.articleeQTL mappingproximal gradient methodcis-eQTLtrans-eQTLpenalized regressionGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	eQTL mapping proximal gradient method cis-eQTL trans-eQTL penalized regression Genetics QH426-470
spellingShingle	eQTL mapping proximal gradient method cis-eQTL trans-eQTL penalized regression Genetics QH426-470 Cheng Gao Hairong Wei Kui Zhang LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
description	Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.
format	article
author	Cheng Gao Hairong Wei Kui Zhang
author_facet	Cheng Gao Hairong Wei Kui Zhang
author_sort	Cheng Gao
title	LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
title_short	LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
title_full	LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
title_fullStr	LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
title_full_unstemmed	LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression
title_sort	lorsen: fast and efficient eqtl mapping with low rank penalized regression
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b652412a4fe44266b4538b580c5ae2ab
work_keys_str_mv	AT chenggao lorsenfastandefficienteqtlmappingwithlowrankpenalizedregression AT hairongwei lorsenfastandefficienteqtlmappingwithlowrankpenalizedregression AT kuizhang lorsenfastandefficienteqtlmappingwithlowrankpenalizedregression
_version_	1718425440202260480

LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

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