Dataset Information

Longitudinal data methods for evaluating genome-by-epigenome interactions in families.

ABSTRACT: BACKGROUND:Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data. RESULTS:Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score. CONCLUSIONS:Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models.

SUBMITTER: Strickland JC

PROVIDER: S-EPMC6156905 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Longitudinal data methods for evaluating genome-by-epigenome interactions in families.

Strickland Justin C JC Chen I-Chen IC Wang Chanung C Fardo David W DW

BMC genetics 20180917 Suppl 1

<h4>Background</h4>Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.<h4>Results</h4>Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to t ...[more]

PMID: 30255767

Similar Datasets

Project description:There is growing interest in the hypertriglyceridemic waist (HTGW) phenotype, defined as high waist circumference (?95 cm in males and ?80 cm in females) combined with high serum triglyceride concentration (?2.0 mmol/L in males and ?1.5 mmol/L in females) as a marker of type 2 diabetes (T2D) and cardiovascular disease. However, the prevalence of this phenotype in high-risk populations, its association with T2D, and the genetic or epigenetic influences on HTGW are not well explored. Using data from large, extended families of Mexican Americans (a high-risk minority population in the USA) we aimed to: (1) estimate the prevalence of this phenotype, (2) test its association with T2D and related traits, and (3) dissect out the genetic and epigenetic associations with this phenotype using genome-wide and epigenome-wide studies, respectively.Data for this study was from 850 Mexican American participants (representing 39 families) recruited under the ongoing San Antonio Family Heart Study, 26 % of these individuals had HTGW. This phenotype was significantly heritable (h (2) r?=?0.52, p?=?1.1?×?10(-5)) and independently associated with T2D as well as fasting glucose levels and insulin resistance. We conducted genome-wide association analyses using 759,809 single nucleotide polymorphisms (SNPs) and epigenome-wide association analyses using 457,331 CpG sites. There was no evidence of any SNP associated with HTGW at the genome-wide level but two CpG sites (cg00574958 and cg17058475) in CPT1A and one CpG site (cg06500161) in ABCG1 were significantly associated with HTGW and remained significant after adjusting for the closely related components of metabolic syndrome. CPT1A holds a cardinal position in the metabolism of long-chain fatty acids while ABCG1 plays a role in triglyceride metabolism.Our results reemphasize the value of HTGW as a marker of T2D. This phenotype shows association with DNA methylation within CPT1A and ABCG1, genes involved in fatty acid and triglyceride metabolism. Our results underscore the importance of epigenetics in a clinically informative phenotype.

Dataset Information

Longitudinal data methods for evaluating genome-by-epigenome interactions in families.

Publications

Longitudinal data methods for evaluating genome-by-epigenome interactions in families.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets