Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study

Kim V. E. Braun; Klodian Dhana; Paul S. de Vries; Trudy Voortman; Joyce B. J. van Meurs; Andre G. Uitterlinden; Albert Hofman; Frank B. Hu; Oscar H. Franco; Abbas Dehghan

doi:10.1186/s13148-016-0304-4

Research
Open Access
Published: 07 February 2017

Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study

Clinical Epigeneticsvolume 9, Article number: 15 (2017) | Download Citation

Abstract

Background

DNA methylation is a key epigenetic mechanism that is suggested to be associated with blood lipid levels. We aimed to identify CpG sites at which DNA methylation levels are associated with blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol in 725 participants of the Rotterdam Study, a population-based cohort study. Subsequently, we sought replication in a non-overlapping set of 760 participants.

Results

Genome-wide methylation levels were measured in whole blood using the Illumina Methylation 450 array. Associations between lipid levels and DNA methylation beta values were examined using linear mixed-effect models. All models were adjusted for sex, age, smoking, white blood cell proportions, array number, and position on array. A Bonferroni-corrected p value lower than 1.08 × 10⁻⁷ was considered statistically significant. Five CpG sites annotated to genes including DHCR24, CPT1A, ABCG1, and SREBF1 were identified and replicated. Four CpG sites were associated with triglycerides, including CpG sites annotated to CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), and SREBF1 (cg11024682). Two CpG sites were associated with HDL-C, including ABCG1 (cg06500161) and DHCR24 (cg17901584). No significant associations were observed with LDL-C or total cholesterol.

Conclusions

We report an association of HDL-C levels with methylation of a CpG site near DHCR24, a protein-coding gene involved in cholesterol biosynthesis, which has previously been reported to be associated with other metabolic traits. Furthermore, we confirmed previously reported associations of methylation of CpG sites within CPT1A, ABCG1, and SREBF1 and lipids. These results provide insight in the mechanisms that are involved in lipid metabolism.

Background

Genetics is an important determinant of lipid levels which may affect them by changing the expression levels of the genes [1]. Gene expression levels, however, are also regulated by DNA methylation, which is one of the most studied mechanisms in the field of epigenetics and may therefore have an effect on lipid levels [2]. In contrast to the DNA sequence, DNA methylation is dynamic over time and responsive to the environment; therefore, DNA methylation could also change in response to blood lipid levels [3].

A few studies using candidate gene approaches have reported that DNA methylation at several loci, such as APOE and ABCA1, are associated with lipid levels [4, 5]. In addition, epigenome-wide association studies (EWAS) have recently become available, providing the possibility to identify associations between blood lipid levels and DNA methylation at novel loci [3]. To date, EWAS have identified associations between differentially methylated genes at a few novel loci, such as TNNT1, CPT1A, and ABCG1, and blood lipid levels [6–8]. However, so far, most studies investigating the association between DNA methylation and lipids have been performed in patient populations, while only one study has been performed within a population-based study. As DNA methylation may vary across different states of health, further population-based studies are needed to explore these associations in the general population.

In this study, we aimed to investigate the association between blood DNA methylation levels and blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol in of 725 participants of the Rotterdam Study, a population-based cohort study. Subsequently, we sought replication in a non-overlapping set of 760 participants.

Results

Participant characteristics

Participant characteristics of the discovery cohort (n = 725) and the replication cohort (n = 760) are presented in Table 1. Levels of triglycerides and total cholesterol were similar in both cohorts. Mean levels of HDL were slightly lower in the discovery cohort compared to the replication cohort (1.4 vs. 1.5 mmol/L, p < 0.001). Mean levels of LDL-C were higher in the discovery cohort compared to the replication cohort (3.9 vs. 3.7 mmol/L, p = 0.02). The mean age was significantly higher (p < 0.001) in the replication cohort (67.7 ± 5.9 years) compared to the discovery cohort (59.9 ± 8.2 years). In the discovery cohort, 27% of the population was current smokers, whereas in the replication cohort, this was 10% (p < 0.001).

Table 1 Participant characteristics

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Discovery panel

The associations between DNA methylation probes and blood lipid levels are presented in Manhattan plots (Figs. 1 and 2). Table 2 shows the Bonferroni-significant CpGs. We identified five CpG sites associated with triglyceride levels. These CpG sites were annotated to CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), SREBF1 (cg11024682), and DHCR24 (cg17901584). CpG sites annotated to CPT1A and DHCR24 were negatively associated with triglycerides, whereas CpG sites annotated to ABCG1 and SREBF1 were positively associated with triglycerides. We identified three CpG sites associated with HDL-C. These CpG sites were annotated to ABCG1 (cg06500161) and DHCR24 (cg17901584), and one CpG site was not annotated to a gene (cg14816825). The CpG site annotated to DHCR24 was positively associated with HDL-C; the other two CpG sites were negatively associated with HDL-C. We did not find significantly associated CpG sites for LDL-C and total cholesterol levels in the discovery cohort.

Table 2 Epigenome-wide associations between genome-wide DNA methylation and lipid levels

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Replication panel

Of the five CpG sites significantly associated with triglycerides in the discovery cohort, four replicated in the replication cohort, including CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), and SREBF1 (cg11024682). Of the three CpG sites significantly associated with HDL-C in the discovery cohort, two replicated in the replication cohort, including ABCG1 (cg06500161) and DHCR24 (cg17901584). Results from the discovery and replication cohorts were combined using fixed-effect meta-analyses.

Meta-analyses

In order to test potential confounding, additional models with adjustment for lipid-lowering medication, waist circumference, and other lipids were performed in the combined analyses. Some of the effect estimates decreased in strength, but overall, the results remained similar to those of model 1 (Table 3). Furthermore, results from meta-analyses revealed seven new CpG sites associated with triglycerides, including TXNIP, TMEM49, SLC7A11, and KCNA3. An additional 55 CpG sites were associated with HDL-C. Methylation of four CpG sites were associated with total cholesterol, including CpGs annotated to IFFO1, ABCG1, and DHCR24 (Additional file 1: Table S1).

Table 3 Associations between genome-wide DNA methylation and lipid levels (meta-analyses)

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Additional analyses

Since lipid levels might be affected by dietary fat intake, we explored whether intake of total fat, poly-unsaturated fatty acids (PUFA), mono-unsaturated fatty acids (MUFA), and saturated fatty acids (SFA) were associated with DNA methylation of significantly replicated CpG sites, using linear regression models. From these models, we observed no significant association between fat intake and methylation of DHCR24, SREBF1, ABCG1, or CPT1A (Additional file 2: Table S2). To test if there was an interaction between lipid-lowering medication or fat intake and the CpG site located in the DHCR24 gene on blood lipid levels, interaction terms were added to the regression model. However, none of these interaction terms were significant. Sensitivity analyses in which we replaced beta values with M values showed similar results (Additional file 3: Table S3).

Methylation risk scores

The methylation risk score was calculated using eight CpG sites for triglycerides and seven CpG sites for HDL-C, based on current and previously reported findings [7, 8]. The correlation coefficients of these CpG sites are presented in Additional file 4: Table S4 and Additional file 5: Table S5. For triglycerides, 9% of the variance was explained by the methylation risk score. For HDL-C, 5% of the variance was explained by the methylation risk score (Additional file 6: Table S6). To test whether the association between methylation risk scores and lipids differed by lipid-lowering medication use, interaction terms were tested. For both triglycerides as HDL-C, none of the interaction terms were significant. The difference in levels of triglycerides and HDL-C per quartile of methylation risk score are presented in Figs. 3 and 4. HDL-C levels decrease as quartiles of methylation risk score increase. Triglyceride levels increased from the first quartile to the second quartile but remained similar for the third quartile and the fourth quartile.

Expression levels

The associations between DNA methylation of ABCG1 and CPT1A and their expression levels are presented in Additional file 7: Table S7. Of the four genes of which the significantly associated CpG sites were annotated, only data on expression levels for CPT1A and ABCG1 passed quality control. For CPT1A, the expression (ILMN_1696316) was negatively associated with the methylation of CPT1A at both identified CpG sites (cg00574958: p = 7.6 × 10⁻⁴, cg17058475: p = 5.1 × 10⁻³). Expression of ABCG1 at two of the five transcripts (ILMN_1794782 and ILMN_2329927) were negatively associated with methylation of the ABCG1 CpG site (p = 6.6 × 10⁻¹⁴ and 3.7 × 10⁻¹¹).

Discussion

The current study reports the results of an epigenome-wide association study of fasting triglycerides, HDL-C, LDL-C, and total cholesterol blood levels. We identified and replicated a CpG site near DHCR24 to be related with HDL-cholesterol. Moreover, we confirmed former reports that methylation of CpG sites within ABCG1 are associated with HDL-C, and methylation of CpG sites within CPT1A, ABCG1, and SREBF1 were associated with triglycerides. The majority of these associations were independent of several potentially confounding factors, such as use of lipid-lowering medication, waist circumference, or other lipid traits. The differentially methylated CpG sites combined in a methylation risk score explained up to 9% of the variance in triglycerides and 5% of the variance in HDL-C.

We observed that methylation of a CpG site (cg17901584) annotated to the DHCR24 gene was associated with HDL-C. This CpG site is located within 1 Kb upstream of the DHCR24 gene (Fig. 5). The DHCR24 gene encodes for the cholesterol biosynthesis enzyme 3-hydroxysterol-24 reductase, which catalyzes the conversion of desmosterol to cholesterol [9, 10]. Mutations in the DHCR24 gene may cause desmosterolosis, an autosomal recessive disease characterized by high levels of desmosterol [11]. Since DHCR24 is involved in the cholesterol metabolism, an association between methylation in this gene and HDL-C is plausible. However, to date, it is not clear how methylation of DHCR24 is involved in sterol regulation. It has been suggested that regulation of DHCR24 expression is mediated by sterol regulatory element-binding proteins (SREBP) in response to cholesterol availability [12]. We observed in meta-analysis of the discovery and replication cohort that methylation of a CpG site in the SREBF1 gene, which encodes for SREBP, was associated with HDL-C levels. These findings suggest that mechanisms in which these two genes are involved might interact. Unfortunately, as the probes for these genes did not pass quality control, we did not have data on the expression levels of these genes to further investigate this hypothesis. Moreover, the DHCR24 gene is located near PCSK9, which is associated with cholesterol levels [1]. Therefore, the association we observed between DHCR24 methylation and HDL-C might be due to SNP variation in PCSK9. However, when we adjusted our models on HDL-C for the top SNP from genome-wide association studies (GWAS), the association remained similar (data not shown). To our knowledge, an association between methylation of cg17901584 (DHCR24) and HDL-C has not been previously reported. However, an EWAS performed by Dekkers et al. showed that another CpG site located in the DHCR24 gene, cg2716885, was associated with LDL-C levels [13]. Although there are no previous studies that report an association between methylation of cg17901584 (DHCR24) and HDL-C, previous studies reported associations between methylation of this CpG site with waist circumference and phosphatidylcholine (PC ae C36:5) [14, 15]. In additional models, we adjusted for waist circumference, where we indeed observed a decrease in strength compared to the main model. However, the association stayed significant. Two scenarios may explain these results. A higher waist circumference may affect lipid levels and consequently modify DNA methylation. Alternatively, waist circumference could be a confounding factor since metabolic traits are highly correlated. Due to the cross-sectional design of our study, it is difficult to make strong conclusions whether waist circumference is a confounder or a precursor in this association.

In addition to our novel finding, we also replicated findings previously reported by the GOLDN study [7]. In line with findings from GOLDN, we observed a significant negative association between two CpG sites (cg00574958 and cg17058475) located in the CPT1A gene and triglycerides. Carnitine palmitoyltransferase-1, which is encoded by the CPT1A gene, has an important role of transporting long-chain fatty acids in mitochondria. In order to obtain more knowledge on the epigenetic mechanisms of CPT1A, we explored expression levels of this gene in relation to methylation. Similar to findings from the GOLDN study, we observed a negative association between DNA methylation and expression levels of one probe, suggesting that increased methylation at CPT1A decreases expression of this gene [7].

Furthermore, some of the findings previously reported by KORA were also replicated [8]. In line with findings from KORA, we also found a significant negative association between a CpG site (cg06500161), located in the ABCG1 gene and HDL-C. We also found a significant positive association between CpG sites located in ABCG1 (cg06500161) and SREBF1 (cg11024682) and triglycerides. Although these findings regarding triglycerides correspond to findings from KORA, the CpG sites in ABCG1 and SREBF1 differed from the CpG sites found in our study. To further explore the epigenetic mechanisms of ABCG1, we investigated whether DNA methylation of the CpG sites at ABCG1 was associated with gene expression. Similar to KORA, we observed a negative correlation between DNA methylation at CpG sites annotated to ABCG1 with two mRNA expression probes [8]. In our study, ILMN_1794782 showed the strongest negative correlation (r = −0.30) with methylation of ABCG1 (cg06500161), which was also reported by the authors from KORA as one of the most strongly related transcripts to ABCG1 methylation. These results are in agreement with a mediatory role of gene ABCG1 expression in the association between DNA methylation and lipid levels. In addition to lipid levels, other studies have also demonstrated an association between methylation levels at CpG sites located in CPT1A, ABCG1, and SREBF1 with other cardiometabolic traits, such as myocardial infarction, body mass index (BMI), waist circumference, insulin, and several metabolomic traits [8, 14–16]. These previously reported findings and our current results suggest that methylation of these genes are involved in metabolic mechanisms and may be potential therapeutic targets for cardiovascular- and metabolic-related diseases.

Results from the discovery and replication cohorts were combined in meta-analysis, which revealed a large amount of additional differentially methylated CpG sites that are associated with HDL-C levels. These additional findings include several CpG sites annotated to genes that were previously reported in GWAS on lipid levels, such as LDLR and CMIP [1]. LDLR, low-density lipoprotein receptor, is a protein-coding gene, and mutations in this gene can cause familial hypercholesterolemia [17]. CMIP encodes a c-Maf-inducing protein, which is involved in T cell signaling pathway, and this gene is known to be associated with speech impairment [18]. However, less is known on the role of CMIP in cholesterol metabolism. Considering previously reported and current findings, there might be a genetic effect as well as an epigenetic effect of these genes on HDL-C levels. Nevertheless, these findings should be interpreted with caution, as the results of our meta-analyses are not replicated by other studies yet.

This study has several strengths and limitations that should be considered with the interpretation of the currently reported results. The strength of this study is the availability of genome-wide DNA methylation data in a large sample of adults from the general population and an internal replication set. Although we had a large sample size, more loci can be identified with even larger sample sizes. To illustrate, when the results from the discovery and replication cohorts were combined in meta-analysis, an additional set of 55 new significant associations for HDL-C were observed (Additional file 1: Table S1), which emphasizes the benefits of using a larger sample size in EWAS. A limitation of this study is the use of whole blood samples to determine DNA methylation levels, whereas DNA methylation is cell type specific. When methylation is studied as a consequence of lipid levels, then leukocytes are a relevant tissue. However, when the aim is to study methylation sites that are causal of lipid levels, leukocytes might not be a relevant tissue. Therefore, certain important CpG sites could have been overlooked in our study. Nevertheless, previous studies have demonstrated that results can be replicated across tissues, suggesting that the use of blood tissue is not necessarily a major issue [14, 19]. Moreover, associations may have been overlooked be due to the type of measurements used in our study. The levels of LDL-C in our study were calculated with use of the Friedewald formula [20], which may not have been specific enough to identify an association between genome-wide DNA methylation and LDL-C. Results from another study performed within the GOLDN study showed that methylation of two CpG sites in the CPT1A gene is associated with LDL-C and VLDL-C [21]. These results overlap with our findings of triglycerides, but we did not identify CpG sites associated with LDL. This discrepancy in results may be due to a more detailed quantification of lipids by NMR spectroscopy in the GOLDN study. In our current study, we used beta values to analyze methylation levels. Since it has been suggested that there could be heteroscedasticity for CpG sites with very low or high methylation, the use of M values is recommended. These M values represent the Logit transformation of the beta values and are considered to have a better detection rate and true positive rate [22]. In order to explore whether this could have affected our results, we performed additional analyses in which we replaced beta values with M values. However, these sensitivity analyses showed that results were similar for beta values and M values (Additional file 3: Table S3), which suggests that in our study heteroscedasticity may not be an issue. Due to the cross-sectional design, we cannot determine the temporal direction of the association between DNA methylation and blood lipids. Furthermore, the observed associations could be explained by an unidentified common factor, as residual confounding is always an issue in observational studies. Another possibility is that the results may be confounded by differences in cell type proportion. In order to avoid this source of confounding, we adjusted all analyses for measured or estimated cell type proportions [23]. Finally, our replication cohort had a 7.8 years higher mean age compared to the discovery cohort, which could have resulted in differences in association between DNA methylation and lipid levels. However, when we tested our models for interaction with age, we did not find any evidence that the strength of associations was affected by age.

Conclusion

In conclusion, we report an association of HDL-C levels with methylation of a CpG site near DHCR24, a protein-coding gene involved in cholesterol biosynthesis. This CpG site has previously been reported to be associated with other metabolic traits, such as waist circumference. In addition, we replicated associations previously reported by other studies. These results provide insight in the mechanisms that are involved in cholesterol and lipid metabolism and identify potential new therapeutic targets. Future studies should include a larger sample size and further investigate the independency and causality of the observed associations.

Methods

Design and subjects

This study was embedded within the Rotterdam Study, a population-based cohort study in Rotterdam, the Netherlands. The design of the Rotterdam Study has been previously described in detail elsewhere [24]. Briefly, residents of Ommoord, a district in Rotterdam, aged 45 years and older were invited to participate. The Rotterdam Study includes three sub-cohorts. We used data from the baseline and second visit of the third cohort (RSIII-1 and RSIII-2) and the third visit from the second cohort (RSII-3).