Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20
Abstract Background The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides...
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12863-018-0651-6 |
| _version_ | 1811252609205403648 |
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| author | Saurabh Ghosh David W. Fardo |
| author_facet | Saurabh Ghosh David W. Fardo |
| author_sort | Saurabh Ghosh |
| collection | DOAJ |
| description | Abstract Background The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set. Results The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood–based regression models. This article discusses the varying strategies explored by the group’s investigators with the common goal of improving the power to detect association with repeated measures of a phenotype. Conclusions Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association. |
| first_indexed | 2024-04-12T16:38:19Z |
| format | Article |
| id | doaj.art-0c555113c8624e4293a736bf806bf442 |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-04-12T16:38:19Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-0c555113c8624e4293a736bf806bf4422022-12-22T03:24:54ZengBMCBMC Genetics1471-21562018-09-0119S112713110.1186/s12863-018-0651-6Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20Saurabh Ghosh0David W. Fardo1Human Genetics Unit, Indian Statistical InstituteDepartment of Biostatistics, University of KentuckyAbstract Background The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set. Results The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood–based regression models. This article discusses the varying strategies explored by the group’s investigators with the common goal of improving the power to detect association with repeated measures of a phenotype. Conclusions Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association.http://link.springer.com/article/10.1186/s12863-018-0651-6Genome-wide associationEpigenome-wide associationLongitudinal dataLinear mixed modelsQuasi-likelihoodMultivariate phenotypes |
| spellingShingle | Saurabh Ghosh David W. Fardo Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 BMC Genetics Genome-wide association Epigenome-wide association Longitudinal data Linear mixed models Quasi-likelihood Multivariate phenotypes |
| title | Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 |
| title_full | Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 |
| title_fullStr | Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 |
| title_full_unstemmed | Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 |
| title_short | Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20 |
| title_sort | association analyses of repeated measures on triglyceride and high density lipoprotein levels insights from gaw20 |
| topic | Genome-wide association Epigenome-wide association Longitudinal data Linear mixed models Quasi-likelihood Multivariate phenotypes |
| url | http://link.springer.com/article/10.1186/s12863-018-0651-6 |
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