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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