A genome-wide association study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham Heart Study Offspring Cohort

Kalsbeek, Anya; Veenstra, Jenna; Westra, Jason; Disselkoen, Craig; Koch, Kristin A.; McKenzie, Katelyn A; O’Bott, Jacob; Woude, Jason Vander; Fischer, Karen M.; Shearer, Greg; Harris, William S.; Tintle, Nathan L.

doi:10.1371/journal.pone.0194882

Cited by 27 publications

(27 citation statements)

References 48 publications

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“…The post-hoc covariate adjustment on both individual fatty acids and PCA for the Omega-3 and Omega-6 fatty acids hit genes that have been found in previous GWAS on fatty acids such as FADS1, ELOVL2, and LPCAT3. 19–21 Using principal components and covariate adjustment we found a novel gene that has not yet been found associated with fatty acids before: PTPRM, and another (AGPAT4) that was only identified with a fatty acid ratio before on this sample. 19 Table 2 displays all SNPs found significant with any individual Omega-3 or Omega-6 fatty acid, or the first, second, or third principal components of either Omega-3 or Omega-6 fatty acids.…”

Section: Resultsmentioning

confidence: 87%

“…Previous genome wide association studies explored associations between SNPs and red blood cell fatty acid (RBC FA) levels indicative of various health measures such as cardiovascular health and inflammation using data from The Framingham Heart Study. 19–21 We applied our method to unrelated individuals in the Generation 3 and Offspring cohorts with a sample size of 1,454 with data on 408,595 SNPs after quality control. We investigated the Omega-3 and Omega-6 fatty acids.…”

Section: Methodsmentioning

confidence: 99%

“…Look to cited studies for more information regarding the results of past fatty acid GWAS and the Framingham cohort. 19–21…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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“…This strong genetic association did not vary significantly even after multivariate association (p = 7.02 × 10 −15 in the unadjusted model; p = 3.35 × 10 −14 in the model adjusted for sex, age, and diabetes and p = 8.90 × 10 −14 , in the model adjusted for sex, age, diabetes, BMI, medications (including antihypertensive drugs, lipid lowering drugs, and insulin and oral antidiabetic medication), smoking, physical activity, and adherence to the Mediterranean diet. A previous GWAS in the Framingham study also showed a slight effect of the dietary adjustments in the SNP-PUFA associations [122]. Additional adjustment of the model containing all of these variables for more medications (including drug for pain and fever, anti-platelet drugs, tranquilizers-sedative-hypnotics, and vitamins/minerals), did not substantially change the statistical significance of the association between the FADS1-rs174547 polymorphism and serum omega-3 concentrations (p = 2.90 × 10 −13 ).…”

Section: Gwas For Serum Omega-3 Pufa (%) In This Populationmentioning

confidence: 74%

“…Some GWAS have considered dietary intake in adjustments carried out in the statistical analysis, indicating a modest influence [122]. However, to better understand dietary modulation, a gene*diet interaction analysis should be carried out.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study for Serum Omega-3 and Omega-6 Polyunsaturated Fatty Acids: Exploratory Analysis of the Sex-Specific Effects and Dietary Modulation in Mediterranean Subjects with Metabolic Syndrome

et al. 2020

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Many early studies presented beneficial effects of polyunsaturated fatty acids (PUFA) on cardiovascular risk factors and disease. However, results from recent meta-analyses indicate that this effect would be very low or nil. One of the factors that may contribute to the inconsistency of the results is that, in most studies, genetic factors have not been taken into consideration. It is known that fatty acid desaturase (FADS) gene cluster in chromosome 11 is a very important determinant of plasma PUFA, and that the prevalence of the single nucleotide polymorphisms (SNPs) varies greatly between populations and may constitute a bias in meta-analyses. Previous genome-wide association studies (GWAS) have been carried out in other populations and none of them have investigated sex and Mediterranean dietary pattern interactions at the genome-wide level. Our aims were to undertake a GWAS to discover the genes most associated with serum PUFA concentrations (omega-3, omega-6, and some fatty acids) in a scarcely studied Mediterranean population with metabolic syndrome, and to explore sex and adherence to Mediterranean diet (MedDiet) interactions at the genome-wide level. Serum PUFA were determined by NMR spectroscopy. We found strong robust associations between various SNPs in the FADS cluster and omega-3 concentrations (top-ranked in the adjusted model: FADS1-rs174547, p = 3.34 × 10−14; FADS1-rs174550, p = 5.35 × 10−14; FADS2-rs1535, p = 5.85 × 10−14; FADS1-rs174546, p = 6.72 × 10−14; FADS2-rs174546, p = 9.75 × 10−14; FADS2-rs174576, p = 1.17 × 10−13; FADS2-rs174577, p = 1.12 × 10−12, among others). We also detected a genome-wide significant association with other genes in chromosome 11: MYRF (myelin regulatory factor)-rs174535, p = 1.49 × 10−12; TMEM258 (transmembrane protein 258)-rs102275, p = 2.43 × 10−12; FEN1 (flap structure-specific endonuclease 1)-rs174538, p = 1.96 × 10−11). Similar genome-wide statistically significant results were found for docosahexaenoic fatty acid (DHA). However, no such associations were detected for omega-6 PUFAs or linoleic acid (LA). For total PUFA, we observed a consistent gene*sex interaction with the DNTTIP2 (deoxynucleotidyl transferase terminal interacting protein 2)-rs3747965 p = 1.36 × 10−8. For adherence to MedDiet, we obtained a relevant interaction with the ME1 (malic enzyme 1) gene (a gene strongly regulated by fat) in determining serum omega-3. The top-ranked SNP for this interaction was ME1-rs3798890 (p = 2.15 × 10−7). In the regional-wide association study, specifically focused on the FADS1/FASD2/FADS3 and ELOVL (fatty acid elongase) 2/ELOVL 5 regions, we detected several statistically significant associations at p < 0.05. In conclusion, our results confirm a robust role of the FADS cluster on serum PUFA in this population, but the associations vary depending on the PUFA. Moreover, the detection of some sex and diet interactions underlines the need for these associations/interactions to be studied in all specific populations so as to better understand the complex metabolism of PUFA.

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Transcriptome Analysis of Myocardial Ischemic‐Hypoxic Injury in Rats and Hypoxic H9C2 Cells

Niu,

Miao,

Ren

2024

ESC Heart Failure

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AimsThis study aimed to address inconsistencies in results between the H9C2 myocardial hypoxia (MH) cell line and myocardial infarction (MI) rat models used in MI research. We identified differentially expressed genes (DEGs) and underlying molecular mechanisms using RNA sequencing technology.MethodsRNA sequencing was used to analyse DEGs in MI rat tissues and H9C2 cells exposed to hypoxia for 24 h. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify key biological processes and pathways. Weighted correlation network analysis [weighted gene co‐expression network analysis (WGCNA)] was used to construct gene co‐expression networks, and hub genes were compared with published MI datasets [Gene Expression Omnibus (GEO)] for target identification.ResultsGO analysis revealed enrichment of immune inflammation and mitochondrial respiration processes among 5139 DEGs in MI tissues and 2531 in H9C2 cells. KEGG analysis identified 537 overlapping genes associated with metabolism and oxidative stress pathways. Cross‐analyses using the published GSE35088 and GSE47495 datasets identified 40 and 16 overlapping genes, respectively, with nine genes overlapping across all datasets and our models. WGCNA identified a key module in the MI model enriched for mRNA processing and protein binding. GO analysis revealed enrichment of mRNA processing, protein binding and mitochondrial respiratory chain complex I assembly in MI and H9C2 MH models. Five relevant hub genes were identified via a cross‐analysis between the 92 hub genes that showed a common expression trend in both models.ConclusionsThis study reveals both shared and distinct transcriptomic responses in the MI and H9C2 models, highlighting the importance of model selection for studying myocardial ischaemia and hypoxia.

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A genome-wide association study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham Heart Study Offspring Cohort

Cited by 27 publications

References 48 publications

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

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

Genome-Wide Association Study for Serum Omega-3 and Omega-6 Polyunsaturated Fatty Acids: Exploratory Analysis of the Sex-Specific Effects and Dietary Modulation in Mediterranean Subjects with Metabolic Syndrome

Transcriptome Analysis of Myocardial Ischemic‐Hypoxic Injury in Rats and Hypoxic H9C2 Cells

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