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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.690926/full |
| _version_ | 1818651917633454080 |
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| author | Cheng Gao Hairong Wei Kui Zhang |
| author_facet | Cheng Gao Hairong Wei Kui Zhang |
| author_sort | Cheng Gao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T02:13:44Z |
| format | Article |
| id | doaj.art-b652412a4fe44266b4538b580c5ae2ab |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T02:13:44Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-b652412a4fe44266b4538b580c5ae2ab2022-12-21T22:07:29ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-11-011210.3389/fgene.2021.690926690926LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized RegressionCheng Gao0Hairong Wei1Kui Zhang2Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United StatesCollege of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United StatesDepartment of Mathematical Sciences, Michigan Technological University, Houghton, MI, United StatesCharacterization 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.https://www.frontiersin.org/articles/10.3389/fgene.2021.690926/fulleQTL mappingproximal gradient methodcis-eQTLtrans-eQTLpenalized regression |
| spellingShingle | Cheng Gao Hairong Wei Kui Zhang LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression Frontiers in Genetics eQTL mapping proximal gradient method cis-eQTL trans-eQTL penalized regression |
| title | 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_short | LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression |
| title_sort | lorsen fast and efficient eqtl mapping with low rank penalized regression |
| topic | eQTL mapping proximal gradient method cis-eQTL trans-eQTL penalized regression |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.690926/full |
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