Penalized regression approaches to testing for quantitative trait-rare variant association
In statistical data analysis, penalized regression is considered an attractive approach<br/>for its ability of simultaneous variable selection and parameter estimation. Although<br/>penalized regression methods have shown many advantages in variable selection and<br/>outcome predic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-05-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00121/full |
| _version_ | 1818969013075574784 |
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| author | Sunkyung eKim Wei ePan Xiaotong eShen |
| author_facet | Sunkyung eKim Wei ePan Xiaotong eShen |
| author_sort | Sunkyung eKim |
| collection | DOAJ |
| description | In statistical data analysis, penalized regression is considered an attractive approach<br/>for its ability of simultaneous variable selection and parameter estimation. Although<br/>penalized regression methods have shown many advantages in variable selection and<br/>outcome prediction over other approaches for high-dimensional data, there is a relative<br/>paucity of the literature on their applications to hypothesis testing, e.g. in genetic<br/>association analysis. In this study, we apply several new penalized regression methods<br/>with a novel penalty, called Truncated L1-penalty (TLP) (Shen et al. 2012), for<br/>either variable selection, or both variable selection and parameter grouping, in a dataadaptive<br/>way to test for association between a quantitative trait and a group of rare<br/>variants. The performance of the new methods are compared with some existing tests,<br/>including some recently proposed global tests and penalized regression-based methods,<br/>via simulations and an application to the real sequence data of the Genetic Analysis<br/>Workshop 17 (GAW17). Although our proposed penalized methods can improve over<br/>some existing penalized methods, often they do not outperform some existing global<br/>association tests. Some possible problems with utilizing penalized regression methods<br/>in genetic hypothesis testing are discussed. Given the capability of penalized regression<br/>in selecting causal variants and its sometimes promising performance, further studies<br/>are warranted. |
| first_indexed | 2024-12-20T14:13:50Z |
| format | Article |
| id | doaj.art-e55391866c9d481dbb6d9eb9d16383a4 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-20T14:13:50Z |
| publishDate | 2014-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-e55391866c9d481dbb6d9eb9d16383a42022-12-21T19:38:05ZengFrontiers Media S.A.Frontiers in Genetics1664-80212014-05-01510.3389/fgene.2014.0012188654Penalized regression approaches to testing for quantitative trait-rare variant associationSunkyung eKim0Wei ePan1Xiaotong eShen2University of MinnesotaUniversity of MinnesotaUniversity of MinnesotaIn statistical data analysis, penalized regression is considered an attractive approach<br/>for its ability of simultaneous variable selection and parameter estimation. Although<br/>penalized regression methods have shown many advantages in variable selection and<br/>outcome prediction over other approaches for high-dimensional data, there is a relative<br/>paucity of the literature on their applications to hypothesis testing, e.g. in genetic<br/>association analysis. In this study, we apply several new penalized regression methods<br/>with a novel penalty, called Truncated L1-penalty (TLP) (Shen et al. 2012), for<br/>either variable selection, or both variable selection and parameter grouping, in a dataadaptive<br/>way to test for association between a quantitative trait and a group of rare<br/>variants. The performance of the new methods are compared with some existing tests,<br/>including some recently proposed global tests and penalized regression-based methods,<br/>via simulations and an application to the real sequence data of the Genetic Analysis<br/>Workshop 17 (GAW17). Although our proposed penalized methods can improve over<br/>some existing penalized methods, often they do not outperform some existing global<br/>association tests. Some possible problems with utilizing penalized regression methods<br/>in genetic hypothesis testing are discussed. Given the capability of penalized regression<br/>in selecting causal variants and its sometimes promising performance, further studies<br/>are warranted.http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00121/fullGWASSSU testSSUw testSum testTLP |
| spellingShingle | Sunkyung eKim Wei ePan Xiaotong eShen Penalized regression approaches to testing for quantitative trait-rare variant association Frontiers in Genetics GWAS SSU test SSUw test Sum test TLP |
| title | Penalized regression approaches to testing for quantitative trait-rare variant association |
| title_full | Penalized regression approaches to testing for quantitative trait-rare variant association |
| title_fullStr | Penalized regression approaches to testing for quantitative trait-rare variant association |
| title_full_unstemmed | Penalized regression approaches to testing for quantitative trait-rare variant association |
| title_short | Penalized regression approaches to testing for quantitative trait-rare variant association |
| title_sort | penalized regression approaches to testing for quantitative trait rare variant association |
| topic | GWAS SSU test SSUw test Sum test TLP |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00121/full |
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