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Detecting responses to treatment with fenofibrate in pedigrees

BACKGROUND: Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors...

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Autores principales: Cherlin, Svetlana, Wang, Maggie Haitian, Bickeböller, Heike, Cantor, Rita M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156837/
https://www.ncbi.nlm.nih.gov/pubmed/30255820
http://dx.doi.org/10.1186/s12863-018-0652-5
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author Cherlin, Svetlana
Wang, Maggie Haitian
Bickeböller, Heike
Cantor, Rita M.
author_facet Cherlin, Svetlana
Wang, Maggie Haitian
Bickeböller, Heike
Cantor, Rita M.
author_sort Cherlin, Svetlana
collection PubMed
description BACKGROUND: Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees. RESULTS: The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks. CONCLUSIONS: Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb; (c) common and rare variants are associated with TG responses to Fb; (d) the interactions of common variants and CpG ML should be included in models predicting the TG response; and (e) sample size is a critical factor in the successful construction of predictive models representing the response to Fb.
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spelling pubmed-61568372018-09-27 Detecting responses to treatment with fenofibrate in pedigrees Cherlin, Svetlana Wang, Maggie Haitian Bickeböller, Heike Cantor, Rita M. BMC Genet Methodology BACKGROUND: Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees. RESULTS: The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks. CONCLUSIONS: Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb; (c) common and rare variants are associated with TG responses to Fb; (d) the interactions of common variants and CpG ML should be included in models predicting the TG response; and (e) sample size is a critical factor in the successful construction of predictive models representing the response to Fb. BioMed Central 2018-09-17 /pmc/articles/PMC6156837/ /pubmed/30255820 http://dx.doi.org/10.1186/s12863-018-0652-5 Text en © The Author(s). 2018 Open AccessThis 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.
spellingShingle Methodology
Cherlin, Svetlana
Wang, Maggie Haitian
Bickeböller, Heike
Cantor, Rita M.
Detecting responses to treatment with fenofibrate in pedigrees
title Detecting responses to treatment with fenofibrate in pedigrees
title_full Detecting responses to treatment with fenofibrate in pedigrees
title_fullStr Detecting responses to treatment with fenofibrate in pedigrees
title_full_unstemmed Detecting responses to treatment with fenofibrate in pedigrees
title_short Detecting responses to treatment with fenofibrate in pedigrees
title_sort detecting responses to treatment with fenofibrate in pedigrees
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156837/
https://www.ncbi.nlm.nih.gov/pubmed/30255820
http://dx.doi.org/10.1186/s12863-018-0652-5
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