Dataset Information

Healthy diet is associated with gene expression in blood: the Framingham Heart Study.

ABSTRACT:

Background

Genes in metabolic and nutrient signaling pathways play important roles in lifespan in model organisms and human longevity.

Objective

The aim of this study was to examine the relation of a quantitative measure of healthy diet to gene expression in a community-based cohort.

Methods

We used the 2015 Dietary Guidelines for Americans Adherence Index (DGAI) score to quantify key dietary recommendations of an overall healthy diet. Our current analyses included 2220 Offspring participants (mean age 66 ± 9 y, 55.4% women) and 2941 Third-Generation participants (mean age 46 ± 9 y, 54.5% women) from the Framingham Heart Study. Gene expression was profiled in blood through the use of the Affymetrix Human Exon 1.0 ST Array. We conducted a transcriptome-wide association study of DGAI adjusting for age, sex, smoking, cell counts, and technical covariates. We also constructed a combined gene score from genes significantly associated with DGAI.

Results

The DGAI was significantly associated with the expression of 19 genes (false discovery rate <0.05). The most significant gene, ARRDC3, is a member of the arrestin family of proteins, and evidence in animal models and human data suggests that this gene is a regulator of obesity and energy expenditure. The DGAI gene score was associated with body mass index (P = 1.4 × 10-50), fasting glucose concentration (P = 2.5 × 10-11), type 2 diabetes (P = 1.1 × 10-5), and metabolic syndrome (P = 1.8 × 10-32).

Conclusions

Healthier diet was associated with genes involved in metabolic function. Further work is needed to replicate our findings and investigate the relation of a healthy diet to altered gene regulation.

SUBMITTER: Lin H

PROVIDER: S-EPMC6736078 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach to map genetic loci related to these heritable phenotypes.In 1087 Framingham Heart Study Offspring cohort participants (mean age 47 years, 52% women), we conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a approximately 30 year span were the primary phenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variance components linkage to investigate the relationships between SNPs (on autosomes, with minor allele frequency > or =10%, genotypic call rate > or =80%, and Hardy-Weinberg equilibrium p > or = 0.001) and multivariable-adjusted residuals. We pursued a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II (n approximately 1450 individuals) and 40 SNPs (P < 0.001 in joint analysis of Stages I and II) tested in Stage III (n approximately 6650 individuals).Long-term averages of LDL-C, HDL-C, and TG were highly heritable (h2 = 0.66, 0.69, 0.58, respectively; each P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10(-5) in GEE results for LDL-C, four for HDL-C, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10(-4) ranged from 13 to 18 and with p < 10(-3), from 94 to 149. Some results confirmed previously reported associations with candidate genes including variation in the lipoprotein lipase gene (LPL) and HDL-C and TG (rs7007797; P = 0.0005 for HDL-C and 0.002 for TG). The full set of GEE, FBAT and linkage results are posted at the database of Genotype and Phenotype (dbGaP). After three stages of replication, there was no convincing statistical evidence for association (i.e., combined P < 10(-5) across all three stages) between any of the tested SNPs and lipid phenotypes.Using a 100K genome-wide scan, we have generated a set of putative associations for common sequence variants and lipid phenotypes. Validation of selected hypotheses in additional samples did not identify any new loci underlying variability in blood lipids. Lack of replication may be due to inadequate statistical power to detect modest quantitative trait locus effects (i.e., <1% of trait variance explained) or reduced genomic coverage of the 100K array. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids.