Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20
Abstract Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based t...
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BMC
2018-09-01
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| Series: | BMC Proceedings |
| Online Access: | http://link.springer.com/article/10.1186/s12919-018-0124-y |
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doaj-1308ad9a08e04f55abc640b9977e90912020-11-25T02:01:05ZengBMCBMC Proceedings1753-65612018-09-0112S9737710.1186/s12919-018-0124-yEvaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20Jason Vander Woude0Jordan Huisman1Lucas Vander Berg2Jenna Veenstra3Abbey Bos4Anya Kalsbeek5Karissa Koster6Nathan Ryder7Nathan L. Tintle8Department of Mathematics and Statistics, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeDepartment of Biology, Dordt CollegeDepartment of Biology, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeDepartment of Mathematics and Statistics, Dordt CollegeAbstract Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum p value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set.http://link.springer.com/article/10.1186/s12919-018-0124-y |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Jason Vander Woude Jordan Huisman Lucas Vander Berg Jenna Veenstra Abbey Bos Anya Kalsbeek Karissa Koster Nathan Ryder Nathan L. Tintle |
| spellingShingle |
Jason Vander Woude Jordan Huisman Lucas Vander Berg Jenna Veenstra Abbey Bos Anya Kalsbeek Karissa Koster Nathan Ryder Nathan L. Tintle Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 BMC Proceedings |
| author_facet |
Jason Vander Woude Jordan Huisman Lucas Vander Berg Jenna Veenstra Abbey Bos Anya Kalsbeek Karissa Koster Nathan Ryder Nathan L. Tintle |
| author_sort |
Jason Vander Woude |
| title |
Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 |
| title_short |
Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 |
| title_full |
Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 |
| title_fullStr |
Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 |
| title_full_unstemmed |
Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20 |
| title_sort |
evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at gaw20 |
| publisher |
BMC |
| series |
BMC Proceedings |
| issn |
1753-6561 |
| publishDate |
2018-09-01 |
| description |
Abstract Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum p value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set. |
| url |
http://link.springer.com/article/10.1186/s12919-018-0124-y |
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