**6. Genetic variants in dyslipidemia and MetSyn: Future directions**

Given the polygenic nature and multi-level complexity of Dyslipidemia and MetSyn, a better understanding of the genetic determinants of each intermediate (lower level) phenotype as well as the collective integration of these traits as unifying syndromes (higher/hierarchical level) is needed, which will require more elegant statistical modeling methods and, perhaps, a paradigm shift in the way in which we think about dissecting genetic and environmental factors in complex traits. As stated throughout this chapter, there is considerable overlap between genetic variants associated with HDL-C, LDL-C and TG levels as well MetSyn as a unifying trait. As a result, there is great need to understand not only the aggregate effects of multiple variants in each of these genes but to also understand how the effects of variation in one gene are modified in the presence of other genes.

Aggregate effects of multiple variants in genes affecting dyslipidemia and MetSyn related traits have included calculation of 'risk scores', which simply add the number of 'risk alleles' in a weighted or unweighted manner. For example, unweighted risk scores were constructed by summing the number of 'TG-raising' alleles at 32 loci and placed in 'risk bins' (categories) to show that higher risk scores were significantly associated with patients with hypertriglyceridemia (HTG) compared to controls (Johansen and Hegele, 2011; Teslovich et al., 2010). Increasing genotype risk scores comprised by summing risk alleles in 9 common SNPs were associated with decreasing HDL-C levels (Kathiresan et al., 2008a).

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We have used the multivariate statistical framework of structural equation modeling (SEM) to evaluate multiple genetic determinants of MetSyn and aggregate effects of individual genes by modeling MetSyn as a second-order factor together with multiple putative candidate genes represented by latent constructs, which we mathematically defined by multiple SNPs in each gene (Nock et al., 2009b). Using this approach with the Framingham Heart Study (Offspring Cohort, Exam 7; Affymetrix 50k Human Gene Panel) data, we found that the CETP gene had a very strong association with the Dyslipidemia factor but little effect on MetSyn directly. Furthermore, we found that the effects of the CSMD1 gene diminished when modeled simultaneously with six other candidate genes, most notably CETP and STARD13. Work to identify the genetic determinants of 'Syndrome Z', modeled as a higher-order, unifying syndrome defined by 5 first-order factors (dyslipidemia, insulin resistance, obesity, hypertension, sleep disturbance) (Nock et al., 2009a) using the latent gene construct SEM approach is underway.

The use of other forms of 'causal modeling' (edge/node; integrative genetics) has been proposed (Lusis et al., 2008), particularly, to improve our understanding of differential effects by gender as well as to better understand how maternal nutrition and epigenetics affect MetSyn. Furthermore, a complex model for the genetic determinants of MetSyn associated phenotypes was recently proposed and, using gene enrichment analysis and protein-protein interaction network approaches, the retinoid X receptor and farnesoid X receptor (FXR) were identified as key players in MetSyn given their multiple interactions with metabolism, cell proliferation and oxidative stress (Sookoian and Pirola, 2011). However, more elegant kinetic models may be required to understand the true influence of genetic variants on Dsylipidemia and MetSyn given the presence of multiple feedback loops and reversible reactions (Bakker et al., 2010; Gutierrez-Cirlos et al., 2011).
