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Abstract
Background—Omega6 (n6) polyunsaturated fatty acids (PUFAs) and their metabolites are involved in cell signaling, inflammation, clot formation, and other crucial biological processes. Genetic components, such as variants of fatty acid desaturase (FADS) genes, determine the composition of n6 PUFAs.
Methods and Results—To elucidate undiscovered biological pathways that may influence n6 PUFA composition, we conducted genome-wide association studies and meta-analyses of associations of common genetic variants with 6 plasma n6 PUFAs in 8631 white adults (55% women) across 5 prospective studies. Plasma phospholipid or total plasma fatty acids were analyzed by similar gas chromatography techniques. The n6 fatty acids linoleic acid (LA), γ-linolenic acid (GLA), dihomo-GLA, arachidonic acid, and adrenic acid were expressed as percentage of total fatty acids. We performed linear regression with robust SEs to test for single-nucleotide polymorphism–fatty acid associations, with pooling using inverse-variance–weighted meta-analysis. Novel regions were identified on chromosome 10 associated with LA (rs10740118; P=8.1×10−9; near NRBF2), on chromosome 16 with LA, GLA, dihomo-GLA, and arachidonic acid (rs16966952; P=1.2×10−15, 5.0×10−11, 7.6×10−65, and 2.4×10−10, respectively; NTAN1), and on chromosome 6 with adrenic acid after adjustment for arachidonic acid (rs3134950; P=2.1×10−10; AGPAT1). We confirmed previous findings of the FADS cluster on chromosome 11 with LA and arachidonic acid, and further observed novel genome-wide significant association of this cluster with GLA, dihomo-GLA, and adrenic acid (P=2.3×10−72, 2.6×10−151, and 6.3×10−140, respectively).
Conclusions—Our findings suggest that along with the FADS gene cluster, additional genes may influence n6 PUFA composition.
Introduction
It is well documented that certain long-chain polyunsaturated fatty acids (PUFAs), such as the omega-3s (n3) in fatty fish, are beneficial with respect to cardiovascular health. More recently, it has been proposed that the omega6 (n6) PUFAs may also have health benefits1–4 although opposing findings have also been reported.5–7 N6 PUFAs metabolize into the powerful bioactive eicosanoids, such as leukotrienes, thromboxanes, and lipoxins, that influence biological processes that relate to health, such as inflammation and platelet aggregation. N6 PUFAs have been differentially associated with inflammatory cytokines, clotting factors, and endothelial dysfunction markers, but only for certain n6 PUFAs.8–10 Because plasma and cell membranes may be composed of different n6 PUFAs in variable concentrations, it is important to characterize the determinants of plasma and cell membrane n6 PUFA composition. Dietary intake, lifestyle, and demographic characteristics11–15 are well known to influence n6 levels; however, recent findings from genome-wide association studies (GWAS) and the Kibbutzim Family Study indicate a strong genetic component in determining plasma and erythrocyte fatty acid composition.16–18
Clinical Perspective on p 331
To date, the best characterized genes shown to affect plasma and membrane PUFA composition are the fatty acid desaturase (FADS) genes, FADS1 and FADS2. These biologically relevant candidate genes encode the δ-5 and δ-6 desaturases, which participate in the metabolic conversion of the essential fatty acid linoleic acid (LA) to longer chain n6 PUFAs (Figure 1). Candidate gene studies have demonstrated significant associations of the minor alleles in the FADS cluster with multiple n6 PUFAs, including arachidonic acid (AA; 20:4n6), LA (18:2n6), γ-LA (GLA; 18:3n6), dihomo-γ-LA (DGLA; 20:3n6), and adrenic acid (AdrA; 22:4n6).19–22 A recent GWAS of fatty acids confirmed the association of genetic variants in FADS1, FADS2, and FADS3 with LA and AA18; however, it remains unknown whether other loci beyond FADS influence LA and AA composition, and whether any genetic loci influence levels of the other n6 fatty acids, including GLA, DGLA, and AdrA.
N6 polyunsaturated fatty acid metabolic pathway and summary of genome-wide significant associations. The associations of loci with each fatty acid are shown with dashed arrows. + and – signs indicate the direction of the associations. AA indicates arachidonic acid; AdrA, adrenic acid; DGLA, dihomo-γ-linoleic acid; GLA, γ-linoleic acid; and LA, linoleic acid.
Given the gaps in our current knowledge of genetic determinants of n6 PUFA composition, we performed a large-scale meta-analysis of GWAS from 5 participating cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium23 to identify common genetic variants associated with plasma n6 fatty acid phenotypes, including LA, GLA, DGLA, AA, and AdrA.
Materials and Methods
Ethics Statement
Informed consent forms were signed by participants, and each local institutional review board of the participating cohort studies approved the study protocols.
Study Population
Study participants in the current GWAS were of European ancestry, had available plasma n6 PUFA and genetic data, and were enrolled in 1 of 5 cohorts, including the Atherosclerosis Risk in Communities (ARIC) study (n=3269), Coronary Artery Risk Development in Young Adults (CARDIA) study (n=1507), Cardiovascular Health Study (CHS; n=2404), Invecchiare in Chianti (InCHIANTI) study (n=1075), and an ancillary study to the Multi-Ethnic Study of Atherosclerosis (MESA; n=707). Descriptions of each of these studies have been previously published.24–28
Measurement of Plasma Phospholipid or Total Plasma Fatty acids
Details of plasma fatty acid measurement have been described previously (Data Supplement). In the ARIC, CARDIA, and MESA cohorts, phospholipid fatty acids were analyzed according to Cao et al.29 First, total lipids were extracted, and phospholipid fraction was isolated by thin layer chromatography. Isolated phospholipids were then converted to fatty acid methyl esters for further separation by gas chromatography. CHS used a similar method (Data Supplement). In the InCHIANTI study, total plasma fatty acids were directly measured by gas chromatography.30 AdrA was measured in the ARIC and CHS cohorts only. N6 fatty acids in all studies were expressed as percentage of total fatty acids.
Imputation and Statistical Analysis
Genotyping was done in each cohort separately using high-density single-nucleotide polymorphism (SNP) marker platforms (ARIC, CARDIA, and MESA: Affymetrix 6.0; CHS: Illumina 370; InCHIANTI: Illumina 550). Samples with call rates below 95% (ARIC, CARDIA, and MESA) or 97% (CHS and InCHIANTI) at genotyped markers were excluded. Genotypes were imputed to ≈2.5 million HapMap SNPs using MACH31 (ARIC and InCHIANTI), BIMBAM32 (CHS), BEAGLE33 (CARDIA), or IMPUTE2.1.034 (MESA). SNPs for which testing Hardy–Weinberg equilibrium resulted in P<10–5 were excluded from imputation. SNPs with minor allele frequency <1% or imputation quality score (estimated r2) <0.3 were excluded from the meta-analyses. Additional details on genotyping and imputation per cohort are provided in Table I in the Data Supplement.
The main analysis was linear regression of each fatty acid on single-SNP allele dosage from imputation, including covariates to account for age, sex, site of recruitment when appropriate (InCHIANTI, CARDIA, CHS, and MESA), as well as the top 2 (MESA) or top 10 (CARDIA and CHS) principal components to adjust for potential population structure. To reduce the complexity of analysis by each cohort, we chose a conservative model without adjusting for diet and other lifestyle variables. In all cohorts, we used a robust Huber–White sandwich variance estimator, which provides protection against miss-specified mean models, as well as nonconstant variance (heteroskedasticity).35–37 The association results in each cohort were corrected by genomic control method,38 which provides additional protection against spurious findings because of population stratification; the results were then combined using inverse-variance–weighted meta-analysis in METAL (www.sph.umich.edu/csg/abecasis/metal). Cochran Q test was used to assess potential heterogeneity among results from multiple cohorts.39 As the Cochran Q test P value for each meta-analysis in our study was ≥0.05, we chose the fixed effect meta-analysis to pool results across the cohorts. We declared a fatty acid–SNP association genome-wide significant if the nominal P value for the SNP was <5×10–8. For the significantly associated SNPs, we calculated the proportion of variation explained by a particular variant in each cohort using an approximation: (β2×2×minor allele frequency ×(1−minor allele frequency))/Var(Y), where β is the regression coefficient for 1 copy of the allele and Var(Y) is the variance of the fatty acid in the corresponding cohort.
To explore additional independent susceptibility variants at the loci identified in the main analysis, we repeated the GWAS and meta-analysis conditioning on the most significant SNPs in each loci, specifically rs10740118 (chromosome 10), rs174547 (chromosome 11), and rs16966952 (chromosome 16).
We also performed GWAS and meta-analysis in which each SNP was tested for association with n6 fatty acid levels, adjusting for levels of the preceding fatty acid in the biological pathway (Figure 1). For example, to identify additional SNPs associated with GLA (18:3n6), we conducted a GWAS of GLA with adjustment for LA (18:2n6).
Results
The 5 cohort studies included 8631 adults (55% women) of European ancestry, who were average age 60 years (Table 1). The mean proportion of LA in plasma phospholipids was ≈20% of total phospholipid fatty acids, ranging from 19.96% in CHS to 21.98% in CARDIA. The mean proportion of AA ranged from 10.87% in CHS to 12.1% in MESA. InCHIANTI, total plasma LA was slightly higher (24.78%) and AA was lower (8.00%), relative to the phospholipid fatty acid fraction in other cohorts.8 GLA and DGLA were present in substantially smaller amounts across studies (range, 0.09%–0.12% and 3.13%–3.33%, respectively). In ARIC and CHS, plasma phospholipid fatty acids were analyzed for AdrA and the proportions were similar in the 2 studies.
Characteristics of Participants Included in the n6 GWAS Meta-Analysis, n=8631
Meta-Analysis of GWAS of n6 Fatty Acids
Figure 2A to 2E shows the Manhattan plots for the meta-analysis of the GWAS results for LA, AA, GLA, DGLA, and AdrA. The genomic inflation factors are 1.02, 0.99, 1.01, 1.02, and 1.02 for LA, GLA, DGLA, AA, and AdrA, respectively. For the primary analysis, adjusting for only age and sex (and other covariates where applicable), genome-wide significant signals were identified on chromosomes 10, 11, and 16 for LA, GLA, DLGA, AA, and AdrA (Table 2).
Genetic Loci Where Common Polymorphisms Are Associated With Plasma FA (Percentage of Total FAs) With P<5×10–8
LA was associated with multiple SNPs on chromosome 10 in a region that included nuclear receptor-binding factor 2 (NRBF2), jumonji domain containing 1C (JMJD1c), and receptor accessory protein 3 (REEP3; Figure 3A). The most significant SNP was rs10740118 (P=8.1×10–9). There was no association of SNPs in this region with the other n6 fatty acids. We found the most significant associations of SNP rs174547 in FADS1 on chromosome 11 with all 5 n6 fatty acids. Several other SNPs were also genome-wide significant, falling within the FADS2 and FADS3 regions. Four n6 fatty acids (LA, GLA, DGLA, and AA) were associated with SNPs in a region of chromosome 16 that included pyridoxal-dependent decarboxylase domain containing 1 (PDXDC1), N-terminal asparagine amidase (NTAN1), and RNA polymerase I–specific transcription initiation factor (RRN3). Using LA as an example, Figure 3 shows regional association plots for the 3 identified regions.
A–E, Meta-analysis of genome-wide associations with n6 polyunsaturated fatty acids: A, Linoleic acid (18:2n6), (B), γ-linolenic acid (18:3n6), (C) dihomo-γ-linolenic acid (20:3n6), (D) arachidonic acid (20:4n6), and (E) adrenic acid (22:4n6). Associations were graphed by chromosome position and –log10 (P value) up to P values of 10−10. Δ, additional single-nucleotide polymorphisms (SNPs) with P values < 10-10. Genes of interest within the significant SNPs are indicated.
A–C, Regional association plots in the genome-wide association of linoleic acid (18:2n6). The color scheme is red for strong linkage disequilibrium (LD) and fading color for lower LD. A, Regional association plot for rs10740118 on chromosome 10. B, Regional association plot for rs174547 on chromosome 11. C, Regional association plot for rs16966952 on chromosome 16.
Notably, in the 5 cohorts, the top SNP rs174547 on chromosome 11 independently explained a relatively large proportion of variation in certain n6 PUFA, for example, 8.7% to 11.1% for DGLA across the 5 cohorts, and >20% for AA in 4 of the 5 cohorts. rs16966952 on chromosome 16 independently explained 0.1% to 0.6% to 2.0% to 4.5% of total variation in AA and DGLA, respectively; and rs10740118 on chromosome 10 independently explained 0.2 to 0.7% of variation in LA (Table 2). These 3 SNPs were genotyped in 4 of the 5 cohorts except CHS, in which the imputation R2 was 0.80 for rs10740118, and >0.98 for the other 2 SNPs. Forest plots (Figure I; Table II in the Data Supplement) were shown for associations between each SNP and LA, and the plots for other n6 fatty acids were similar.
Large numbers of SNPs reached genome-wide significance in each of the 3 identified regions. To identify potential secondary signals within these regions, we conducted conditional analysis for each of the 5 n6 PUFA by adjusting for the top SNP in addition to the covariates included in the main analysis (Table 2; Tables III–VII in the Data Supplement). For LA, no other significant association on chromosome 10 was evident after adjustment for the top SNP (rs10740118). Similarly, after adjustment for rs174547, no additional significant association was observed for GLA or AdrA in the region of chromosome 11. Interestingly, after adjusting for rs174547, additional significant associations were identified for LA (rs2727270; P=2.6×10−21), DGLA (rs968567; P=1.3×10−42), and AA (rs102275; P=6.6×10−147). In the region of chromosome 16, we observed no additional significant associations with GLA or AA after adjusting for the top SNP rs16966952. However, in analyses adjusted for rs16966952, another SNP (rs228018) was identified that was significantly associated with LA and DGLA (P=3.6×10−14 and 4.5×10−25).
Circulating levels of the 5 n6 PUFA were correlated with each other, with correlation coefficients ranging from −0.63 to 0.49 (Table VIII in the Data Supplement). In analyses of GLA adjusted for its precursor LA, estimated effect sizes of the most significant SNPs (rs174547 and rs16966952) in the main unadjusted analysis decreased by 47% and 31%, respectively (Table 3); rs174547 was still genome-wide significant (P=1.5×10−20), but rs16966952 failed to reach genome-wide significance (P=3.1×10−6; Table 3). This observation is consistent with the finding that SNP–LA associations partially explained SNP–GLA associations. Similarly, in analyses of AdrA adjusted for AA, no genome-wide significant signal was observed on chromosome 11 for AdrA (P=2.0×10−2 for rs174547).
Estimated Effects of Top SNPs in Main Analysis and Secondary Analysis Adjusting for Preceding Fatty Acid in Metabolic Pathway
Another motivation for the exploratory analyses was the possibility of discovering new associations: When 2 fatty acids are positively correlated, there is greater statistical power to identify SNP–fatty acid associations that are in the opposite directions for the fatty acid and its precursor. Indeed, in analyses of DGLA adjusted for its precursor GLA, and in analyses of AA adjusted for its precursor DGLA, the rs174547-DGLA and rs16966952-AA associations became more significant (with greater effect sizes; Table 3). Furthermore, in analyses of AdrA adjusted for AA, a novel region on chromosome 6 was found. The most significant SNP, rs3134950 (P=2.1×10−10), was positively associated with AA (coefficient=0.085; P=6.6×10−3) but inversely associated with AdrA (coefficient=−0.0097; P=5.7×10−6) in the main analyses. Multiple genes were near the association signals on chromosome 6, including 1-acylglycerol-3-phosphate O-acyltransferase 1 (AGPAT1; Figure 4).
Regional association plot on chromosome 6 in the secondary analyses, adjusting arachidonic acid for adrenic acid. The color scheme is red for strong linkage disequilibrium (LD) and fading color for lower LD.
Each of the identified significant SNPs was either directly genotyped or imputed with high-quality scores in the 5 cohorts, with the mean imputation quality score (r2) between 0.93 and 1.00. We further examined the potential effect modification of obesity on fatty acid metabolism in the ARIC cohort, the study with the largest sample (n=3269), using body mass index (BMI) information categorized into normal weight (BMI<25), overweight (25<BMI<30), and obese (BMI>30). There was no significant interaction observed between the BMI categories and any of our top SNPs (all P>0.10). Finally, to investigate the potential effect of BMI, physical activity, and dietary intakes of total calories and LA on the study results, we conducted additional analyses using ARIC data. There was little change in the estimated coefficients and P values with or without these covariates in the models and no new signal reached genome-wide significance in the 5 GWASs. We included the estimated effect sizes of the top 3 SNPs in the main analysis in Table IX in the Data Supplement.
Discussion
With ≥8900 adults of European ancestry across 5 prospective cohorts, the present analysis represents the largest GWAS of circulating n6 fatty acids to date. We confirmed previous findings that the FADS cluster on chromosome 11 associates with LA and AA,18–22 and further extended these findings to additional n6 PUFAs. In addition, we identified multiple novel regions on chromosomes 6, 10, and 16 with multiple n6 PUFAs.
FADS1 and FADS2 genetic variants play a clear role in regulating n6 PUFAs. In particular, the C allele of rs174547 in FADS1 was associated with a higher proportion of LA and lower GLA (consistent with lower δ-6 desaturase activity) and a higher proportion of DGLA and lower AA (consistent with lower δ-5 desaturase activity). This polymorphism has previously been recognized for its association with n6 PUFAs and desaturase activities,22 and our findings build on and extend these previous observations by documenting additional inverse associations with GLA and DGLA. Conditional analysis identified additional SNPs in FADS2 associated with higher LA and DGLA and a site upstream from FADS1 associated with lower AA. Together, the data suggest that FADS gene minor allele polymorphisms in FADS1 and FADS2 may suppress δ-5 and δ-6 desaturase expression and activity resulting in less flux through the pathway and lower rates of AA and AdrA syntheses. It remains unclear whether such FADS polymorphisms have biological effects on plasma FA composition,40–43 and our findings highlight the need for additional study of potential interaction among FADS variation, n6 PUFAs, and metabolic biomarkers and health outcomes.
Furthermore, rs174547 was highly correlated with rs174538 (r2=0.83) in the current study, which was the SNP most significantly associated with n3 FA in our previous GWAS.17 The 18-carbon N3 fatty acid α-linolenic acid is converted to the 20- and 22-carbon N3 fatty acids (eg, eicosapentaenoic acid and docosahexaenoic acid) via the same enzymatic pathway that converts the 18-carbon LA to the longer chain N6 fatty acids. Therefore, our identification of highly correlated SNPs in the FADS1/2 cluster that relate to both N3 and N6 fatty acids is consistent with existing biochemical knowledge and highlight the important inter-related nature of N3/N6 PUFA metabolism.
Apart from our novel findings for the FADS gene cluster, additional novel associations were observed among multiple n6 PUFAs and gene variants of NRBF2, PDXDC1, AGPAT1, and NTAN1. Notably, all genes except PDXDC1 encode proteins shown to be involved in fatty acid metabolism, which may account for the associations found here. For example, NRBF2 encodes nuclear receptor-binding factor 2 that interacts with peroxisome proliferator activated receptor-α44—a transcription factor that upregulates lipoprotein lipase activity and fatty acid oxidation. Although the specific mechanism that accounts for the association between the rs10740118 NRBF2 variant and LA is not established, we hypothesize that variants of nuclear receptor-binding factor 2 may differentially bind peroxisome proliferator activated receptor-α, thus affecting fatty acid bioavailability. Our novel findings indicate the need for additional studies of how NRBF2 influences fatty acid biology and LA in particular.
For AGPAT1 on chromosome 6, we observed an association of the rs3134950 SNP and AdrA, after adjustment for its fatty acid precursor, AA. The AGPAT1-encoded protein, 1-acyl-sn-glycerol-3-phosphate acyltransferase α, is a critical enzyme in phospholipid and triglyceride biosynthesis, catalyzing the conversion of lysophosphatidic acid to phosphatidic acid.45 In experimental studies, the AGPAT1 protein shows a preference for LA as a fatty acid substrate.46 Although AdrA was not investigated as a possible substrate of AGPAT1, our findings suggest the possibility that variation in the AGPAT1 gene may influence the availability of LA and fatty acids downstream in the pathway.
An additional novel finding was the association of genetic variants in NTAN1 on chromosome 16 with lower proportions of all n6 fatty acids except AdrA. Notably, the most significant genetic variant in NTAN1 (rs16966952) is in linkage disequilibrium (r2=0.76) with SNP (rs4985167) of the PDXDC1 gene that we previously found to be associated with the n3 fatty acid, α-LA (18:3n3).17 Biochemically, the PDXDC1-encoded protein is a vitamin B6–dependent decarboxylase that is preferentially expressed in the intestine, but its physiological importance remains unknown. Alternatively, the associated SNPs on chromosome 16 are also proximate to the PLA2G10 gene, which encodes the secretory phospholipase group-10 enzyme (X-sPLA2). Functionally, X-sPLA2 hydrolyzes phospholipids to release free fatty acids47 and promotes the liberation and bioavailability of n6 AA from glycerophospholipids.48 Notably, the above findings in the PDXDC1 and PLA2G10 genes are in agreement with a previous report of associations of both genes with phospholipid fatty acid species.49 Overall, the mechanisms that explain the novel associations of NRBF2 rs10740118, NTAN1 rs16966952, and AGPAT1 SNP rs3134950 with these n6 PUFAs are not completely understood at present but may be because of the corresponding enzyme/protein’s role in n6 fatty acid metabolism. Additional research is warranted to explain these associations fully.
The FADS gene polymorphisms have been associated with lipid and cholesterol levels, as well as incident coronary heart disease. In a study of 4635 Swedish subjects, the rs174547 C allele was found to be associated with modestly lower low-density lipoprotein-cholesterol levels but not with high-density lipoprotein-cholesterol or triglyceride levels in individuals with relatively lower N3 dietary intake.40 In contrast, a case–control study of coronary heart disease in Chinese subjects revealed that the CC variant of rs174547 was associated with higher high-density lipoprotein-cholesterol and triglyceride levels.41 The investigators proposed that these variations in lipid levels may be partially attributable to the FADS variant, which may contribute to coronary heart disease development. Finally, a candidate gene case–control study of coronary artery disease reported that certain FADS haplotypes were associated with disease risk.42 Contrary to these studies, null findings have also been reported in the cohorts of the Nurses’ Health Study (n=1200) and the Health Professionals Follow-Up Study (n=1295).43 It remains important to identify the relationship(s) between SNPs and lipid traits, which improve our biological understanding of these pathways. Furthermore, studies are needed to investigate the association of the new genes with the intermediate end points, such as leukotrienes, thromboxanes, and lipoxins, and with disease outcomes.
The current study highlights the unique strength of nonhypothesis-driven GWAS for identifying novel common genetic polymorphisms associated with n6 PUFAs. Using samples with European ancestry from each cohort, as well as including factors for population stratification using principal components analysis, reduces the potential for confounding by population stratification. The meta-analysis approach combines results from multiple cohorts to increase the statistical power to identify genes that may not have been identified because of small effects or low frequency. Importantly, our meta-analysis results were consistent across all participating cohorts (Figure I in the Data Supplement), further increasing confidence in the validity and generalizability of the findings.
Potential limitations may be considered. First, InCHIANTI examined total plasma fatty acid composition, whereas the other cohorts examined the composition of phospholipid fatty acid fraction. Yet, findings from InCHIANTI were similar to those observed in the other cohorts. Both the magnitude and the direction of associations were consistent across all 5 cohorts (Figure I in the Data Supplement). We also performed meta-analysis excluding the InCHIANTI study. Notably, the observed associations on chromosomes 10, 11, and 16 were weaker but still consistent (data not shown), which suggests that the tissue where fatty acids were measured had minimal effects on the identified SNP–fatty acid associations. Second, it is possible that environmental factors may influence the gene–fatty acid associations. However, the fatty acid–SNP associations changed little when including BMI, physical activity, and dietary LA and energy intakes in the statistical models using ARIC data (Table IX in the Data Supplement). Third, it must be acknowledged that the present analysis is a hypothesis0generating study. Although we have speculated on potential mechanisms, additional research is required to elucidate the biological effects of the identified polymorphisms. In addition, because of the high linkage disequilibrium in identified loci, it is unclear which SNPs are causal with the associated FA, and finer mapping of these regions is needed to identify the functional SNP. Finally, although our findings passed stringent thresholds for multiple testing corrections, additional replication studies are still needed to confirm our results in other European populations and other cohorts of different racial groups.
Our study confirmed previous GWAS findings that FADS gene variants are associated with plasma and cell membrane fatty acid composition for n6 fatty acids. Notably, we identified novel associations between N6 PUFAs and SNPs in NTAN1, AGPAT1, and NRBF2 genes. Our findings provide a roadmap for further investigation of genetic and metabolic pathways that may influence N6 PUFA.
Acknowledgments
We thank the other investigators, the staff, and the participants of the Atherosclerosis Risk in Communities (ARIC) study, the Coronary Artery Risk Development in Young Adults (CARDIA) study, Cardiovascular Health Study (CHS), Multi-Ethnic Study of Atherosclerosis (MESA), and the Invecchiare in Chianti (InCHIANTI) study for their important contributions. A full list of principal CHS investigators and institutions can be found at http://www.CHS-NHLBI.org. A full list of principal CARDIA investigators and institutions can be found at http://www.cardia.dopm.uab.edu/. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. We acknowledge the essential role of Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium in development and support of this. CHARGE members include National Heart, Lung, and Blood Institute’s (NHLBI) ARIC Study, National Institute on Aging’s Iceland Age Gene/Environment Susceptibility Study, NHLBI’s CHS and Framingham Heart Study, and the Netherland’s Rotterdam Study. We acknowledge the use of the Suite of Nucleotide Analysis Programs server from the Broad Institute (http://www.broadinstitute.org/mpg/snap/) to construct regional association plots. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Sources of Funding
Infrastructure for the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium is supported, in part, by the National Heart, Lung, and Blood Institute (NHLBI) grant HL105756. The Atherosclerosis Risk in Communities (ARIC) Study is performed as a collaborative study supported by NHLBI contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute (NHGRI) contract U01HG004402; and National Institutes of Health (NIH) contract HSN268200625226C. Infrastructure was partly supported by grant number UL1RR025005, a component of the NIH Roadmap for Medical Research. The Cardiovascular Health Study (CHS) was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086 and grants HL080295, HL087652, and HL105756 from the NHLBI, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by AG023629 from the National Institute on Aging (NIA). The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The fatty acid measurements were supported by grant HL085710 from NHLBI. The Coronary Artery Risk Development in Young Adults (CARDIA) study is funded by contracts N01-HC-95095, N01-HC-48047, N01-HC-48048, N01-HC-48049, N01-HC-48050, N01-HC-45134, N01-HC-05187, N01-HC-45205, and N01-HC-45204 from the NHLBI to the CARDIA investigators. Genotyping of the CARDIA participants was supported by grants U01-HG-004729, U01-HG-004446, and U01-HG-004424 from the NHGRI and R01-HL-084099 from the NHLBI to Dr Foy. The Invecchiare in Chianti (InCHIANTI) study baseline (1998–2000) was supported as a targeted project (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the US NIA Contracts 263 MD 9164 and 263 MD 821336 and was supported, in part, by the Intramural research program of the NIA, NIH, Baltimore, MD. The Multi-Ethnic Study of Atherosclerosis (MESA) and MESA SNP Health Association Resource (SHARe) were supported by contracts N01-HC-95159 through N01-HC-95169 and RR-024156 from the NHLBI. Funding for MESA SHARe genotyping was provided by NHLBI Contract N02HL64278. Dr Nettleton was supported by a K01 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIKDD) (5K01DK082729-02).
Disclosures
None.
Appendix
From the Division of Biostatistics, School of Public Health (W.G.), Laboratory Medicine and Pathology, School of Public Medicine (B.T.S., M.Y.T.), and Division of Epidemiology and Community Health, School of Public Health (W.T., L.M.S.), University of Minnesota, Minneapolis; Cardiovascular Health Research Unit (R.N.L., B.M., B.M.P., D.S.), Department of Medicine (R.N.L., B.M.) and Departments of Medicine, Epidemiology, and Health Services (B.M.P., D.S.), University of Washington, Seattle; Department of Epidemiology and Nutrition (J.H.Y.W., D.M., L.W.) and Department of Epidemiology (D.M.), Harvard School of Public Health, Boston, MA; School of Medicine and Pharmacology, University of Western Australia, Perth, Australia (J.H.Y.W.); Clinical Research Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD (T.T., L.F.); Center for Public Health Genomics (A.M., S.S.R.) Division of Biostatistics and Epidemiology (A.M.), University of Virginia, Charlottesville; Institute of Molecular Medicine (M. Foy, X.G., M. Fornage) and Division of Epidemiology, Human Genetics, and Environmental Sciences (J.A.N., M. Fornage), University of Texas Health Science Center in Houston; Geriatrics Rehabilitation Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy (S.B.); Department of Internal Medicine, University of New Mexico, Albuquerque (I.B.K.); Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P.); Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School and Boston VA Healthcare System, MA (L.D.); Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (Y.-D.I.C.); and Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA (D.M.).
Footnotes
Guest Editor for this article was Robert A. Hegele, MD.
The Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.113.000208/-/DC1.
- Received May 20, 2013.
- Accepted April 10, 2014.
- © 2014 American Heart Association, Inc.
References
CLINICAL PERSPECTIVE
Omega6 polyunsaturated fatty acids and their metabolites are involved in cell signaling, inflammation, clot formation, and other crucial biological processes that influence health outcomes. Higher circulating omega6 fatty acid levels have been shown to be associated with health benefits further, such as lower risk of cardiovascular disease and diabetes mellitus. Although dietary intake and demographic factors influence omega6 levels, recent findings from genome-wide association studies indicate a strong genetic component in determining plasma and erythrocyte fatty acid composition. To characterize the genetic component of circulating omega6 fatty acid composition better, we conducted genome-wide association studies and meta-analyses of associations of common genetic variants with 5 plasma phospholipid omega6 fatty acids, including linoleic acid, γ-linolenic acid, dihomo-γ-linolenic acid, arachidonic acid, and adrenic acid, in 8631 white adults across 5 prospective studies in Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Novel associations were observed among multiple omega6 fatty acids and gene variants of NRBF2, PDXDC1, AGPAT1, and NTAN1. Notably, all genes except PDXDC1 encode proteins shown to be involved in fatty acid metabolism. We also confirmed previous findings that the FADS cluster on chromosome 11 associates with linoleic and arachidonic acid, and further extended these findings to the other omega6 fatty acids. These results inform our understanding of genes that influence circulating omega6 fatty acid concentrations, and additional investigations are needed to determine their potential pathogenic role in disease development.
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- Genome-Wide Association Study of Plasma N6 Polyunsaturated Fatty Acids Within the Cohorts for Heart and Aging Research in Genomic Epidemiology ConsortiumCLINICAL PERSPECTIVE
