Introduction Polyunsaturated fatty acids (PUFA) refer to the class of fatty acids with multiple desaturations in the aliphatic tail. Short chain PUFA (up to 16 carbons) are synthesized endogenously by fatty acid synthase. Long chain PUFA are fatty acids of 18 carbons or more in length with two or more double bonds. Depending on the position of the first double bond proximate to the methyl end, PUFA are classified as n-6 or n-3. Long chain PUFA are either directly absorbed from food or synthesized from the two essential fatty acids linoleic acid (LA; 18:2n-6) and alpha-linolenic acid (ALA; 18:3n-3) through a series of desaturation and elongation processes [1]. The initial step in PUFA biosynthesis is the desaturation of ALA and LA by the enzyme d6-desaturase (FADS2; GeneID 9415) (Figure 1). PUFA modulate inflammatory response through a number of different mechanisms including modulation of cyclooxygenase and lipoxigenase activity [2]. Cyclooxygenase and lipoxigenase are essential for production of eicosanoids and resolvins [2]–[4]. Since n-3 and n-6 fatty acids compete for the same metabolic pathway and produce eicosanoids with differing effects, it has been theorized that the balance of the two classes of PUFA may be important in the pathogenesis of inflammatory diseases. 10.1371/journal.pgen.1000338.g001 Figure 1 The metabolic pathway of n-3 and n-6 fatty acids. The fatty acids examined in the study are indicated in bold. The dashed arrows indicate pathways absent in mammals. Epidemiological studies have shown that fatty acid consumption and plasma levels, in particular of the n-3 family, are associated with reduced risk of cardiovascular disease [5]–[7], diabetes [8]–[10], depression [11],[12], and dementia [13]. However, not all studies show significant associations and there has been inconsistencies in the direction of the associations especially for the n-6 acids [14],[15]. The different methods (dietary questionnaire or biomarkers) for accessing PUFA status may contribute to discrepant results [16]–[18]. The disadvantage of using dietary PUFA intake is the evidence of inaccuracies intrinsic in any reporting methods of dietary intake that plasma levels would circumvent. In addition, direct measures of PUFA reflect the cumulative effects of intake and endogenous metabolism. Dietary fatty acids can be converted into longer chain PUFA or stored for energy thus another reason for inconsistent results may be due the general lack of control for individual differences in metabolism once fatty acids are consumed. Previous studies have examined the association of genetic variants, especially polymorphisms in the FADS genes, on fatty acid concentrations in plasma and erythrocyte membranes [19]–[21]. There are 3 FADS (FADS1 [GeneID 3992] ,FADS2, and FADS3 [GeneID 3992]) clustered on chromosome 11. Variants in FADS1 and FADS2 have been consistently shown to be associated with PUFA concentrations. It is unknown whether other loci also determine fatty acid concentrations. To address this question, we conducted a genome-wide association study of plasma fatty acid concentration in participants in the InCHIANTI study. Results Linoleic acid (LA) constituted the highest proportion of total fatty acids followed by arachidonic acid (AA) (Table 1) The narrow heritability was highest for AA (37.7%) followed by LA (35.9%), eicosadienoic acid (EDA, 33.3%), alpha-linolenic acid (ALA, 28.1%), eicosapentanoic acid (EPA, 24.4%), and docosahexanoic acid (DHA,12.0%). For EDA, AA, and EPA, genome-wide significant signals fell in the FADS1/FADS2/FADS3 region on chromosome 11 (Figure 2, Figure 3, Table S1). Of these, the most significant SNP was rs174537 for AA (P = 5.95×10−46), where the variant explained 18.6% of the additive variance of AA concentrations. This SNP was significantly associated with EDA (P = 6.78×10−9), and EPA (P = 1.04×10−14). The association with LA (P = 5.58×10−7) and ALA (P = 2.76×10−5) did not reach genome-wide significance, and there was no association with DHA (P = 0.3188). Presence of the minor allele (T) was associated with lower concentrations of longer chain fatty acids (EDA, AA, EPA), but with higher concentrations of LA and ALA (Table 2). With the exception of DHA, the SNPs exhibiting the strongest evidence of association with the fatty acids examined in this study mapped to the FADS1, FADS2, and FADS3 cluster. The most significant SNP for DHA was on chromosome 12 within the SLC26A10 gene (GeneID 65012, rs2277324; PDHA = 2.65×10−9). In all cases, inclusion of the most significant SNP as a covariate in the model resulted in attenuation of the effect of the other SNPs in the region (Figure S1). Accordingly, associated SNPs in this region were in significant linkage disequilibrium with each other in the InCHIANTI sample (Figure S2). 10.1371/journal.pgen.1000338.g002 Figure 2 Genome-wide scans of omega-6 fatty acid profiles in InCHIANTI study. Genome-wide associations of plasma linoleic acid (A), eicasadienoic acids (B) and arachidonic acid (C) with 495,343 autosomal and X chromosome SNPs that passed quality control graphed by chromosome position and −log10 p-value. The most significant variant was within the FAD1/FAD2/FAD3 cluster on chromosome 11. The genes nearby or within the SNPs that were selected for replication in GOLDN are indicated. 10.1371/journal.pgen.1000338.g003 Figure 3 Genome-wide scans of omega-3 fatty acid profiles in InCHIANTI study. Genome-wide associations of plasma alpha linolenic acid (A), eicosapentanoic acid (B) and docasahexanoic acid (C) with 495,343 autosomal and X chromosome SNPs that passed quality control graphed by chromosome position and −log10 p-value. The most significant variant was within the FAD1/FAD2/FAD3 cluster on chromosome 11. The genes nearby or within the SNPs that were selected for replication in GOLDN are indicated. 10.1371/journal.pgen.1000338.t001 Table 1 Descriptive Characteristics of InCHIANTI and GOLDN study. Trait INCHIANTI GOLDN N (m/f) 1075 (485/590) 1076 (519/557) Age (years) 68.37 (15.5) 48.4 (16.4) BMI (kg/m2) 27.12 (4.1) 28.3 (5.6) Total Cholesterol (mg/dL) 213.62 (40.7) 190.1 (38.9) HDL Cholesterol (mg/dL) 55.98 (15.1) 47 (13.1) LDL Cholesterol (mg/dL) 133.08 (35.3) 121 (31.3) Triglyceride (mg/dL) 122.79 (65.1) 139.2 (117.3) Glucose (mg/dl) 94.23 (26.2) 101.6 (19.0) Linoleic Acida 24.8 (4.0) 12.9 (1.4) Linolenic Acida 0.4 (0.3) 0.1 (0.0) Eicosadienoic Acida 0.1 (0.1) N/A Arachidonic Acida 8.0 (1.9) 13.6 (1.2) Eicosapentanoic Acida 0.61 (0.2) 0.5 (0.3) Docosahexanoic Acida 2.29 (0.8) 3.0 (0.9) Total energy , kal/day 2000 (596) 2122 (1190) Dietary fat, % energy 30.9 (5.1) 35.4 (6.9) Values represent mean (SD). a Fatty acids are plasma concentrations (% total fatty acids) for InCHIANTI and erythrocytes concentration for GOLDN. 10.1371/journal.pgen.1000338.t002 Table 2 Associations of fatty acids and plasma lipids by rs174537 (FADS1) and rs953413 (ELOVL2) in InCHIANTI and GOLDN study. InCHIANTI GOLDN FADS: rs174537 G/G (n = 569) T/G (n = 414) T/T (n = 92) P G/G (n = 433) T/G (n = 495) T/T (n = 139) P Linoleic acid 24.27 (3.99) 25.24 (3.98) 25.88 (3.69) 5500 kcal in men and 4500kcal in women. Genotyping InCHIANTI: Genome-wide genotyping was performed using the Illumina Infinium HumanHap550 genotyping chip (chip version 1 and 3) as previously described [50]. The SNP quality control was assessed using GAINQC. The exclusion criteria for SNPs were minor allele frequency <1% (n = 25,422), genotyping completeness <99% (n = 23,610) and Hardy Weinberg-equilibrium (HWE) <0.0001 (n = 517). GOLDN: Five SNPs were selected for replication in the GOLDN study: rs953413, rs2277324, rs16940765, rs17718324 and rs174537. One of these, rs2277324, failed genotyping and therefore another SNP in high LD, rs923838 (r2 = 0.89 in hapmap), was used as a proxy for this SNP. DNA was extracted from blood samples and purified using commercial Puregene reagents (Gentra System, Inc.) following manufacturer’s instructions. SNPs were genotyped using the 5’nuclease allelic discrimination Taqman assay with allelic specific probes on the ABI Prism 7900HT Sequence Detection System (Applies Biosystems, Foster City, Calif, USA) according to standard laboratory protocols. The primers and probes were pre-designed (the assay -on -demand) by the manufacturer (Applied Biosystem) (Assay ID: FEN_rs174537: C___2269026_10, HRH4_rs16940765: C__32711739_10, SPARC_rs17718324: C__34334455_10, ELOVL2_rs953413: C___7617198_10, rs923828: C___2022671_10). Statistical Analysis InCHIANTI GWAS: Inverse normal transformation was applied to plasma fatty acid concentrations to avoid inflated type I error due to non-normality [51]. The genotypes were coded 0, 1 and 2 reflecting the number of copies of an allele being tested (additive genetic model). For X-chromosome analysis, the average phenotype of males hemizygous for a particular allele was treated assumed to match the average phenotype of females homozygous for the same allele. Association analysis was conducted by fitting simple regression test using the fastAssoc option in MERLIN [52]. Narrow heritability reflects the ratio of the trait’s additive variance to the total variance [51],[53]. In all the analyses, the models were adjusted for sex, age and age squared. The genomic control method was used to control for effects of population structure and cryptic relatedness [54]. An approximate genome-wide significance threshold of 1×10−7 (∼0.05/495343 SNPs) was used. For each fatty acid concentration, a second analysis included the most significant SNP from the first pass analysis as a covariate. Linkage disequilibrium coefficints within the region of interest were calculated using GOLD [55]. For the other phenotypes (total cholesterol, triglycerides, LDL-cholesterol, HDL-cholesterol and BMI), the traits were normalized either by natural log or square root transformation when necessary. Associations for each SNP were investigated using the general linear model (GLM) procedure in SAS. GOLDN: Inverse normal transformation was applied to erythrocyte membrane fatty acid concentration to achieve approximate normality. For the additive model, genotype coding was based on the number of variant alleles at the polymorphic site. With no significant sex modification observed, men and women were analyzed together. We used the generalized estimating equation (GEE) linear regression with exchangeable correlation structure as implemented in the GENMOD procedure in SAS (Windows version 9.0, SAS Institute, Cary, NC) to adjust for correlated observations due to familial relationships. Potential confounding factors included study center, age, sex, BMI, smoking (never, former and current smoker), alcohol consumption (non-drinker and current drinker), physical activity, drugs for lowering cholesterol, diabetes and hypertension and hormones. A two-tailed P value of <0.05 was considered to be statistically significant. Supporting Information Figure S1 Q-Q plots for (A) linolenic acid, (B) eicosadienoic acid (C) arachidonic acid, (D), alpha-linolenic acid, (E), eicsapentanoic acid, and (F) docsahexanoic acid from the first analysis (red circles) and the second analysis after including the most significant SNP (blue circles). (0.52 MB TIF) Click here for additional data file. Figure S2 The associations in the fatty acid desaturase clusters on chromosome 11 are displayed. (A) The −log10 pvalues for each fatty acid concentration within the FADS cluster on chromosome 11. The y axis is truncated at 14, the most significant SNP for arachidonic acid rs174537 at −log10 value of 45. (B) The genes that lie +/− 100kb of rs174537 and (C) pairwise LD (r2) in the region ranging from high (red), intermediate (green), to low (blue) in the InCHIANTI study. (0.76 MB TIF) Click here for additional data file. Figure S3 The associations in the elongation of very long fatty acid 2 gene are displayed. (A) The −log10 pvalues for each fatty acid concentration around the ELOVL2 gene. (B) The genes that lie +/− 100kb of rs953413 and (C) pairwise LD (r2) in the region ranging from high (red), intermediate (green), to low (blue) in the InCHIANTI study. (0.53 MB TIF) Click here for additional data file. Table S1 Top 10 non-redundant SNPs for each plasma fatty acid concentrations. (0.15 MB DOC) Click here for additional data file.
