Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20

• Published in: BMC genetics Vol. 19; no. Suppl 1; p. 73
• Main Authors: Ghosh, Saurabh, Fardo, David W.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.09.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Analysis; Animal Genetics and Genomics; Biomedical and Life Sciences; data collection; Datasets; Epigenome-wide association; equations; Fenofibrate; Genetic aspects; Genetics and Population Dynamics; Genome-wide association; Genome-wide association studies; Genome-Wide Association Study; Genomes; genotype; Genotype & phenotype; Genotypes; high density lipoprotein; High density lipoproteins; Humans; Hypertriglyceridemia - drug therapy; Hypertriglyceridemia - genetics; Hypoglycemic Agents - therapeutic use; Life Sciences; Likelihood Functions; Linear mixed models; Linear Models; Lipoproteins; Lipoproteins, HDL - blood; Longitudinal data; Longitudinal Studies; Measurement; Methylation; Microbial Genetics and Genomics; Mimicry; Multivariate analysis; Multivariate phenotypes; Phenotype; Phenotypes; Physiological aspects; Plant Genetics and Genomics; Polymorphism, Single Nucleotide; Power; Principal components analysis; Proceedings; Quasi-likelihood; Regression analysis; Statistical analysis; Statistics; Studies; triacylglycerols; Triglycerides; Triglycerides - blood; Twins; Within-subjects design; United States; Linear mixed models; Quasi-likelihood; Longitudinal data; Genome-wide association; Epigenome-wide association; Multivariate phenotypes
• ISSN: 1471-2156; 1471-2156
• DOI: 10.1186/s12863-018-0651-6

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.