Associations of genetically predicted fatty acid levels across the phenome: A mendelian randomisation study

Abstract

Background: Fatty acids are important dietary factors that have been extensively studied for their implication in health and disease. Evidence from epidemiological studies and randomised controlled trials on their role in cardiovascular, inflammatory, and other diseases remains inconsistent. The objective of this study was to assess whether genetically predicted fatty acid concentrations affect the risk of disease across a wide variety of clinical health outcomes.

Methods and findings: The UK Biobank (UKB) is a large study involving over 500,000 participants aged 40 to 69 years at recruitment from 2006 to 2010. We used summary-level data for 117,143 UKB samples (base dataset), to extract genetic associations of fatty acids, and individual-level data for 322,232 UKB participants (target dataset) to conduct our discovery analysis. We studied potentially causal relationships of circulating fatty acids with 845 clinical diagnoses, using mendelian randomisation (MR) approach, within a phenome-wide association study (PheWAS) framework. Regression models in PheWAS were adjusted for sex, age, and the first 10 genetic principal components. External summary statistics were used for replication. When several fatty acids were associated with a health outcome, multivariable MR and MR-Bayesian method averaging (MR-BMA) was applied to disentangle their causal role. Genetic predisposition to higher docosahexaenoic acid (DHA) was associated with cholelithiasis and cholecystitis (odds ratio per mmol/L: 0.76, 95% confidence interval: 0.66 to 0.87). This was supported in replication analysis (FinnGen study) and by the genetically predicted omega-3 fatty acids analyses. Genetically predicted linoleic acid (LA), omega-6, polyunsaturated fatty acids (PUFAs), and total fatty acids (total FAs) showed positive associations with cardiovascular outcomes with support from replication analysis. Finally, higher genetically predicted levels of DHA (0.83, 0.73 to 0.95) and omega-3 (0.83, 0.75 to 0.92) were found to have a protective effect on obesity, which was supported using body mass index (BMI) in the GIANT consortium as replication analysis. Multivariable MR analysis suggested a direct detrimental effect of LA (1.64, 1.07 to 2.50) and omega-6 fatty acids (1.81, 1.06 to 3.09) on coronary heart disease (CHD). MR-BMA prioritised LA and omega-6 fatty acids as the top risk factors for CHD. Although we present a range of sensitivity analyses to the address MR assumptions, horizontal pleiotropy may still bias the reported associations and further evaluation in clinical trials is needed.

Conclusions: Our study suggests potentially protective effects of circulating DHA and omega-3 concentrations on cholelithiasis and cholecystitis and on obesity, highlighting the need to further assess them as prevention treatments in clinical trials. Moreover, our findings do not support the supplementation of unsaturated fatty acids for cardiovascular disease prevention.

Fig 1. Overview of the study.

BMI, body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; DHA, docosahexaenoic acid; FDR, false discovery rate; GRS, genetic risk score; GWAS, genome-wide association study; HbA1c, haemoglobin A1c; HES, Hospital Episode Statistics; HWE p, Hardy–Weinberg equilibrium p-value; IVW, inverse-variance weighted; LA, linoleic acid; MAF, minor allele frequency; MR-PheWAS, mendelian randomisation–phenome-wide association study; MUFA, monounsaturated fatty acid; NMR, nuclear magnetic resonance; P, p-value; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; total FA, total fatty acid; T2D, type 2 diabetes.

Fig 2

(1a) Pearson correlation coefficient heatmap between phenotypic circulating fatty acid measurements. (1b) Genetic correlation heatmap between the fatty acid measurements. (2) Additional information on the genetic instruments used in the analysis. Independent biallelic genetic variants that are associated with fatty acids below P = 5 × 10⁻⁸, have MAF > 0.1 and HWE P > 10⁻⁶ were used as instruments. Independence was assessed through clumping with r² < 0.001 and 10 million bases clumping window. Dark red horizontal bars at the left bottom corner show the total number of genetic variants used as instruments per fatty acid. Dark blue vertical bars depict the number of genetic variants within each intersection. SNP intersections are defined with the purple dots below the vertical bars, indicating SNP overlap between groups, e.g., the first bar refers to MUFA having 23 unique SNPs, the sixth bar refers to 10 common SNPs between DHA and omega-3 etc. (3) Hierarchical order of the fatty acids. DHA, docosahexaenoic acid; HWE, Hardy–Weinberg equilibrium; LA, linoleic acid; MAF, minor allele frequency; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; SNP, single nucleotide polymorphism; total FA, total fatty acid.

Fig 3. PheWAS results for the associations between 8 weighted GRS of different fatty acids and 845 clinical outcomes in UKB.

log(OR) regression estimates are plotted against −log(P value). The dashed line represents the 5% FDR threshold, which corresponds to P = 9.2 × 10⁻⁴. Due to their high P values, associations with disorders of lipid metabolism, hypercholesterolemia, and hyperlipidaemia can be seen in the top left box. DHA, docosahexaenoic acid; FDR, false discovery rate; GRS, genetic risk score; LA, linoleic acid; MUFA, monounsaturated fatty acid; PheWAS, phenome-wide association study; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; total FA, total fatty acid; UKB, UK Biobank.

Fig 4

Two-sample MR effect estimates per 1 mmol/L higher fatty acids for cholelithiasis, cholecystitis, and “other biliary tract disease” in (a) using betas from UKB and (b) using betas from Kettunen and colleagues [20] for fatty acids and FinnGen (https://r5.finngen.fi/) for cholelithiasis. Only associations between genetically predicted fatty acids and biliary tract health outcomes with statistically significant (P < 0.05) IVW MR estimates in UKB are shown. DHA, docosahexaenoic acid; IVW, inverse-variance weighted; MR, mendelian randomisation; UKB, UK Biobank; WM, weighted-median.

Fig 5

Two-sample MR effect estimates per 1 mmol/L higher fatty acids for different CVD outcomes in (a) UKB (angina pectoris, coronary atherosclerosis, ischemic heart disease) and (b) using Kettunen and colleagues’ [20] betas for fatty acids and CARDIOGRAMplusC4D [22] for ischemic heart disease. Estimates of all examined fatty acids are shown for comparison. CVD, cardiovascular disease; DHA, docosahexaenoic acid; LA, linoleic acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; total FA, total fatty acid; UKB, UK Biobank; WM, weighted-median.

Fig 6

Two-sample MR effect estimates per 1 mmol/L higher fatty acids for obesity in (a) UKB and (b) using Kettunen and colleagues’ [20] betas for fatty acids and betas from GIANT [21] for BMI. Only associations with statistically significant (P < 0.05) IVW MR estimates in the discovery phase are shown. BMI, body mass index; DHA, docosahexaenoic acid; IVW, inverse-variance weighted; MR, mendelian randomisation; MUFA, monounsaturated fatty acid; total FA, total fatty acid; UKB, UK Biobank; WM, weighted-median.

Fig 7. Two-sample multivariable MR effect estimates per 1 mmol/L higher fatty acids on CHD, using UKB betas for fatty acids and betas from CARDIOGRAMplusC4D [22] for CHD.

The last column illustrated the MIP per fatty acid (MR-BMA output) to prioritise risk factors on CHD. Five models were fitted to investigate the direct effects of fatty acids. Model 1 considered the simultaneous effects of DHA, LA, MUFA, and SFA; Model 2 the effects of omega-3, omega-6, MUFA, and SFA; Model 3 the effects of PUFA, MUFA, and SFA; Model 4: DHA and LA; and Model 5: omega-3 and omega-6. MIP in the last column. CHD, coronary heart disease; DHA, docosahexaenoic acid; LA, linoleic acid; MIP, marginal inclusion probability; MR, mendelian randomisation; MR-BMA, MR-Bayesian method averaging; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; UKB, UK Biobank.