Background: Dietary and circulating levels of polyunsaturated fatty acids (PUFAs) have been associated with mortality and risks of various diseases. The circulating levels of fatty acids (FAs) exhibit heterogeneity across individuals due to genetic and environmental influences. Although previous genome-wide association studies (GWAS) have identified genetic loci associated with FAs, the biological mechanisms of these genetic associations remain largely unexplored. Methods: Here, we conducted GWAS to identify additional genetic loci for 19 circulating fatty acid (FA) traits, quantified by nuclear magnetic resonance spectroscopy, in UK Biobank participants of European ancestry (N = 239,268) and five other ancestries (N = 508 - 4,663). We leveraged the GWAS findings to characterize genetic correlations and colocalized regions among FAs, explore sex differences, examine FA loci influenced by lipoprotein metabolism, and apply statistical fine-mapping to pinpoint putative causal variants. The integrative analysis of GWAS and multi-omics quantitative trait loci (QTL) reveals intermediate molecular phenotypes mediating the associations between the genetic loci and FA levels. Results: Altogether, we identified 215 significant loci for PUFAs-related traits in European participants, 163 loci for monounsaturated fatty acids (MUFAs), and 119 loci for saturated fatty acids (SFAs), including 70 novel loci for PUFAs, 61 for MUFAs, and 54 for SFAs. Of these significant loci, a novel locus for total FAs, omega-6 PUFAs relative to total FAs, and total MUFAs (around genes GSTT1/2/2B) overlapped with QTL signals for all six molecular phenotypes examined, including gene expression, protein abundance, DNA methylation, splicing, histone modification, and chromatin accessibility. Across FA traits, 65% of GWAS loci overlapped with QTL signals for at least one molecular phenotype. Conclusions: Our study identifies additional novel genetic loci and provides the first evidence uncovering molecular mechanisms underlying FAs-relevant variants. These findings offer insights that could facilitate patient stratification for precision nutrition.

The authors have declared no competing interest.

This work was funded by the University of Georgia Research Foundation and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM143060. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study received ethical approval from the National Health Service North West Centre for Research Ethics Committee, and all participants provided informed consent. Data from the UK Biobank were accessed under application number 48818.

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The full summary statistics for GWAS of fatty acids will be publicly available in the GWAS Catalog database (https://www.ebi.ac.uk/gwas/). The blood eQTL data of eQTLGen Consortium used in the analyses were downloaded from https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/SMR_formatted/cis-eQTL- https://zenodo.org/records/7951839/files/LBC_BSGS_meta.tar.gz?download=1SMR_20191212.tar.gz. UKBB pQTL data are available at https://metabolomips.org/ukbbpgwas/. The mQTL data are available for download from https://zenodo.org/records/7951839/files/LBC_BSGS_meta.tar.gz?download=1. Blood sQTL data can be downloaded at https://yanglab.westlake.edu.cn/data/SMR/GTEx_V8_cis_sqtl_summary/sQTL_Whole_Blood.zip. The hQTL data can be assessed from http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/qtl_as/QTL_RESULTS/. Summary statistics of caQTL can be found at https://zenodo.org/records/1405945/files/.