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Abstract
The main goal of this paper is to estimate the effect of triglyceride levels on methylation of cytosine-phosphate-guanine (CpG) sites in multiple-case families. These families are selected because they have 2 or more cases of metabolic syndrome (primary phenotype). The methylations at the CpG sites are the secondary phenotypes. Ascertainment corrections are needed when there is an association between the primary and secondary phenotype. We will apply the newly developed secondary phenotype analysis for multiple-case family studies to identify CpG sites where methylations are influenced by triglyceride levels. Our second goal is to compare the performance of the naïve approach, which ignores the sampling of the families, SOLAR (Sequential Oligogenic Linkage Analysis Routines), which adjusts for ascertainment via probands, and the secondary phenotype approach. The analysis of possible CpG sites associated with triglyceride levels shows results consistent with the literature when using the secondary phenotype approach. Overall, the secondary phenotype approach performed well, but the comparison of the different approaches does not show significant differences between them. However, for genome-wide applications, we recommend using the secondary phenotype approach when there is an association between primary and secondary phenotypes, and to use the naïve approach otherwise.
Highlights
The multiple-case family design oversamples families with cases of metabolic syndrome
For these CpG sites, the secondary phenotype approach was used to assess the effect of TG level on methylation
We studied the performance of the secondary phenotype approach and SOLAR with 2 proband definitions
Summary
The multiple-case family design oversamples families with cases of metabolic syndrome (primary phenotype). To obtain unbiased estimates of the model parameters, adjustments for this oversampling are required. Some work has been done when the primary phenotype is modeled [1]. It is often overlooked that ascertainment corrections are needed when a secondary phenotype is modeled and the primary and secondary phenotype are correlated. A combination of the retrospective likelihood and a joint model for the primary and secondary phenotypes appears to provide unbiased estimates of the model parameters [2, 3]. For family-based data sets, the within-family correlation must be modeled, which yields integration over a
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