In this study we have identified three novel SNPxvitamin A and six novel SNPxvitamin E interactions. A majority of the significant interactions were associated with triglycerides (4/9) and were among non-Hispanic whites (6/9). Our most significant finding (APOB rs693xvitamin E), however, explained less than 1.3% of the variance in LDL-C among Mexican Americans, a trait that is up to 80% heritable. In comparison, the effect of age and sex together accounted for 5.9% of the variance in LDL-C among Mexican Americans.
All of the genes implicated here play key roles in lipid metabolism. The gene products of APOB
, apoB-48 and apo-100, are the main apolipoproteins of chylomicrons and LDL particles, respectively. ANGPTL3
encodes a protein which can suppress lipoprotein lipase (LPL) activity, leading to increases in plasma triglycerides and HDL-C. PCSK9
encodes protein convertase subtilisin kexin 9, a protein that binds the LDL receptor and induces its degradation. Lastly, the APOA1/C3/A4/A5
gene cluster lies within a 17kb region on chromosome 11. Proteins made by this gene cluster are major constituents of very low density lipoprotein (VLDL) and/or HDL, act to inhibit LPL activity, and influence dietary fat absorption and chylomicron synthesis [12
Both vitamin E and A are incorporated into lipoproteins and are delivered to peripheral tissues. Additionally, both are found exclusively in plasma lipoproteins (VLDL, LDL, and HDL) [5
]. The interdependence of these vitamins and lipids (as demonstrated in ) suggests that the interactions described in this study may be either just reflective of the strong correlation between vitamins and lipids or biological relevance. In support of the latter interpretation, micronutrients such as vitamin A and E have previously been implicated in affecting the gene expression of import lipid-metabolizing genes [15
]. For example, Mooradian et al demonstrated that high concentrations of vitamin E were associated with significant decreases in apoA-I expression (which is sensitive to the oxidative state of the cell) in hepatic HepG2 cells by reducing apoA-I promoter activity [27
It has been argued that gene-environment heterogeneity may be, in part, to blame for the lack of replication among GWAS studies and among different ancestral populations [23
]. In the single-SNP PAGE meta-analysis detailed in Dumitrescu et al [14
rs693 was strongly associated in European Americans (p=3.38×10−21
), marginally associated with LDL-C in African Americans (p=0.02), but not associated in Mexican Americans/Hispanics (p=0.18). However, in this analysis, which represents a subset of the PAGE study sample, the main effect of rs693 was significantly associated in Mexican Americans (p=1.17×10−6
, ) after adjusting for the interaction with vitamin E. Accounting for environmental modifiers in genetic studies of lipid levels may not only uncover new biology, it may also improve the generalizabilty of findings from genome-wide association studies.
In interpreting our findings, we should consider several aspects. First, NHANES is a cross-sectional study and, therefore, we are unable to determine the temporal sequence of our results. Second, the issue of sample size and the ‘curse of dimensionality’ [2
] is relevant to this study. As the number of factors under study increases (as with the addition of interaction terms), so do the number of strata. With a set sample size, increasing the number of terms in the model quickly increases the degrees of freedom and reduces the per-stratum sample size, thus decreasing statistical power. For this reason, even with relatively large sample sizes in NHANES, we had to restrict our analysis to SNPs with minor allele frequencies greater than 5%. To better study less-common variants, collaborative studies and/or other non-regression based approaches (such as multifactor dimensionality reduction) [34
] may be appropriate, although they are not without their own limitations. Lastly, other potential confounding environmental factors, such as physical activity and alcohol consumption, were not included in the analysis.
A major strength of the study is that NHANES systematically collects environmental exposures in a diverse population. It is important to keep in mind that, beyond sample size, the power to detect gene-environment interactions is influenced by the accuracy of the measurement of the outcome and the environmental exposure [42
]. In general, environmental variables are notoriously difficult to collect and quantify. Most environmental factors are assessed by questionnaire, which can lead to certain biases, including under-reporting of risky behaviors. Therefore, biomarkers as quantitative measures of the environmental exposures are preferred. Measures of dietary intake may be assessed by collection of daily food diaries or 24-hour dietary recalls. From these recall data, calculation of fat, vitamin, and mineral content is available in NHANES but these estimates are subject to poor recall. However, serum vitamin A and E levels are easily measured from a blood draw and may be used as a measure of dietary compliance.