Genome-Wide Interaction Study of Omega-3 PUFAs and Other Fatty Acids on Inflammatory Biomarkers of Cardiovascular Health in the Framingham Heart Study

A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort

BACKGROUND

Cognitive decline, and more specifically Alzheimer's disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids.

OBJECTIVES

We aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual's genetically conferred risk of cognitive decline.

METHODS

The Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness).

RESULTS

Our analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits.

CONCLUSIONS

In this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.

Role of Polyunsaturated Fat in Modifying Cardiovascular Risk Associated With Family History of Cardiovascular Disease: Pooled De Novo Results From 15 Observational Studies

BACKGROUND

It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium.

METHODS

Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction.

RESULTS

After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions.

CONCLUSIONS

A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD.

Design and quality control of large-scale two-sample Mendelian randomization studies

Background

Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors.

Methods

We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set.

Results

We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls.

Conclusions

In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Genome-wide association study of Red Blood Cell fatty acids in the Women's Health Initiative Memory Study

Despite their widespread associations with a wide variety of disease phenotypes, the genetics of red blood cell fatty acids remains understudied. We present one of the first genome-wide association studies of red blood cell fatty acid levels, using the Women's Health Initiative Memory study - a prospective cohort of N = 7,479 women aged 65-79. Approximately 9 million SNPs were measured directly or imputed and, in separate linear models adjusted for age and genetic principal components of ethnicity, SNPs were used to predict 28 different fatty acids. SNPs were considered genome-wide significant using a standard genome-wide significance level of p < 1 × 10-8. Twelve separate loci were identified, seven of which replicated results of a prior RBC-FA GWAS. Of the five novel loci, two have functional annotations directly related to fatty acids (ELOVL6 and ACSL6). While overall explained variation is low, the twelve loci identified provide strong evidence of direct relationships between these genes and fatty acid levels. Further studies are needed to establish and confirm the biological mechanisms by which these genes may directly contribute to fatty acid levels.

Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population

Background

Oxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins.

Methods

We conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10-8).

Results

The SNP rs143070873 was associated with (p < 5×10-8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis.

Conclusion

Genetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks

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.