Abstract
GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication.
Document Type
Conference Proceeding
Publication Date
9-17-2018
Digital Object Identifier (DOI)
https://doi.org/10.1186/s12919-018-0113-1
Funding Information
Publication of this article was supported by NIH R01 GM031575.
Related Content
The data that support the findings of this study are available from the Genetic Analysis Workshop (GAW), but restrictions apply to the availability of these data, which were used under license for the current study. Qualified researchers may request these data directly from GAW.
Repository Citation
Tintle, Nathan L.; Fardo, David W.; de Andrade, Marzia; Aslibekyan, Stella; Bailey, Julia N.; Bermejo, Justo Lorenzo; Cantor, Rita M.; Ghosh, Saurabh; Melton, Philip; Wang, Xuexua; MacCluer, Jean W.; and Almasy, Laura, "GAW20: Methods and Strategies for the New Frontiers of Epigenetics and Pharmacogenomics" (2018). Biostatistics Faculty Publications. 40.
https://uknowledge.uky.edu/biostatistics_facpub/40
Included in
Biostatistics Commons, Genetics and Genomics Commons, Pharmacy and Pharmaceutical Sciences Commons
Notes/Citation Information
Published in BMC Proceedings, v. 12, suppl. 9, 26, p. 1-3.
© The Author(s). 2018
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.