Most genome-wide association studies have explored relationships between genetic variants and plasma phospholipid fatty acid proportions, but few have examined apparent genetic influences on the membrane fatty acid profile of red blood cells (RBC). Using RBC fatty acid data from the Framingham Offspring Study, we analyzed over 2.5 million single nucleotide polymorphisms (SNPs) for association with 14 RBC fatty acids identifying 191 different SNPs associated with at least 1 fatty acid. Significant associations (p<1×10−8) were located within five distinct 1 MB regions. Of particular interest were novel associations between (1) arachidonic acid and PCOLCE2 (regulates apoA-I maturation and modulates apoA-I levels), and (2) oleic and linoleic acid and LPCAT3 (mediates the transfer of fatty acids between glycerol-lipids). We also replicated previously identified strong associations between SNPs in the FADS (chromosome 11) and ELOVL (chromosome 6) regions. Multiple SNPs explained 8–14% of the variation in 3 high abundance (>11%) fatty acids, but only 1–3% in 4 low abundance (<3%) fatty acids, with the notable exception of dihomo-gamma linolenic acid with 53% of variance explained by SNPs. Further studies are needed to determine the extent to which variations in these genes influence tissue fatty acid content and pathways modulated by fatty acids.

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2 (Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis.

The popularization of biobanks provides an unprecedented amount of genetic and phenotypic information that can be used to research the relationship between genetics and human health. Despite the opportunities these datasets provide, they also pose many problems associated with computational time and costs, data size and transfer, and privacy and security. The publishing of summary statistics from these biobanks, and the use of them in a variety of downstream statistical analyses, alleviates many of these logistical problems. However, major questions remain about how to use summary statistics in all but the simplest downstream applications. Here, we present a novel approach to utilize basic summary statistics (estimates from single marker regressions on single phenotypes) to evaluate more complex phenotypes using multivariate methods. In particular, we present a covariate-adjusted method for conducting principal component analysis (PCA) utilizing only biobank summary statistics. We validate exact formulas for this method, as well as provide a framework of estimation when specific summary statistics are not available, through simulation. We apply our method to a real data set of fatty acid and genomic data.

Background: The role of nutritional status and risk for contracting and/or suffering adverse outcomes from COVID-19 infection are unclear. Preliminary studies suggest that higher n-3 PUFA intakes may be protective. Objectives: The purpose of this study was to compare risk for three COVID-19 outcomes (testing positive, hospitalization, and death) as a function of baseline plasma DHA levels. Methods: DHA levels (% of total fatty acids) were measured by NMR. The three outcomes and relevant covariates were available for 110,688 subjects (hospitalization and death) and for 26,620 ever-tested subjects (positive COVID-19 PCR test result) via the UKBiobank prospective cohort study. Outcome data between January 1, 2020 and March 23, 2021 were included. Estimated Omega-3 Index (red blood cell EPA+DHA%) levels across DHA% quintiles were estimated. Multi-variable Cox-proportional hazards models were constructed and linear (per 1-SD) relations with risk for each outcome were computed. Results: In the fully adjusted models, subjects in quintile 5 of DHA% were 21% less likely to test positive than those in quintile 1 (p<0.001), and the risk for a positive test was 8% lower for each 1-SD increase in plasma DHA% (p<0.001). Quintile 5 subjects were also 27% less likely to be hospitalized than those in quintile 1 (P<0.05), and risk for hospitalization was 11% lower per 1-SD increase in DHA% (p<0.001). For death with COVID-19, risk was monotonically lower through quintile 4 (p<0.05 vs quintile 1), but in quintile 5, the risk reduction was partially attenuated and became non-significant. Estimated Omega-3 Index values across DHA quintiles ranged from 3.5% (quintile 1) to 8% (quintile 5). Conclusions: These findings suggest that nutritional strategies to increase circulating n-3 PUFA levels, such as increased consumption of oily fish and/or use of n-3 fatty acid supplements, may reduce risk for adverse COVID-19 outcomes.

As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In this paper we explore the possibility of using summary statistics from simple linear models of phenotype on genotype in order to make inferences about more complex phenotypes (those that are derived from two or more simple phenotypes). We provide exact formulas for the slope, intercept, and standard error of the slope for linear regressions when combining phenotypes. Derived equations are validated via simulation and tested on a real data set exploring the genetics of fatty acids.