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Table 1 Primary aims and statistical modeling methods

From: Detecting responses to treatment with fenofibrate in pedigrees

First Named Author

Aims of the Analysis

Analytic Methods

Cantor

Filter CpG sites for those exhibiting genetic contributions to ML; targeted meQTL studies

Concordance of familiality and variability of CpG distributional outliers, LMM

Cherlin

Predicting TG response to Fb with SNPs

LASSO penalized regression

Hsu

Evaluating adjustments for family structure

LMM

Wu

Genome-wide cis-meQTL studies

LMM

Xia

Evaluate ML in predicting TG response to Fb

ANN, GEE, and LMM

Xu

Predicting TG response to Fb with SNPs

LMM and KST

Yang

Association between homozygosity intensity and TG response to Fb

GEE

Yasmeen

Predicting TG response to Fb with SNPs and CpG ML

KST and linear regression

  1. ANN Artificial neural networks, CpG Cytosine-phosphate-guanine, Fb Fenofibrate, GEE Generalized estimating equations, KST Kernel score test, LASSO Least absolute shrinkage and selection operator, LMM Linear mixed models, meQTL Methylation quantitative trait locus, ML Methylation level, SNPs Single nucleotide polymorphisms, TG Triglyceride levels