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GENOME-WIDE INTERACTION STUDY OF RED-BLOOD CELL FATTY ACIDS ON INFLAMMATORY BIOMARKERS IN THE FRAMINGHAM HEART STUDY JENNA VEENSTRA, ANYA KALSBEEK DEPARTMENT OF STATISTICS, DORDT COLLEGE, SIOUX CENTER, IOWA INTRODUCTION • Researched the relationship between genetics and environment to predict inflammatory biomarkers • Looked at four fatty acids, eight biomarkers, and 14.1 million single nucleotide polymorphisms (SNPs) • Focused on the interaction between genetics (SNPs) and environment (fatty acid levels) BACKGROUND • Framingham Heart Study (FHS) • Longitudinal Study from Framingham, Massachusetts. • Dedicated to identifying the common factors or characteristics that contribute to cardiovascular disease (CVD). • Our data looked at the Offspring Cohort Exam 8. • The sample size was 2164 individuals. BACKGROUND • Eight different biomarkers that indicate risk for CVD were studied. • • • • • • • • C-reactive protein (CRP) P-selectin Osteoprotegrin (OPG) Tumor necrosis factor (TNF) Interleukin-6 (IL-6) Monocyte Chemotactic Protein-1 (MCP-1) Cell Adhesion Molecule (CAM) Intercellular Cell Adhesion Molecule (I-CAM) BACKGROUND • Biomarker levels were measured by immunoassay1 • Genetic information was generated using the Affymetrix GeneChip Human Mapping 500K Array2 • University of Michigan imputation server was used to impute the genomes through the use of 1000 Genomes phase 1 panel3 • 14.1 million SNPs were imputed and analyzed4 • Fatty acid (FA) data was generated by gas chromatography and flame ionization • Composition was then computed as a proportion of the total identified FA concentration5 BACKGROUND • Existing data • How FA and SNPs relate • How biomarkers and SNPs relate • Non-existing data • How FA levels interact with genetics to predict chronic disease • Looking at the interaction gives an approach that integrates environment and genetics • This may result in more robust predictions of risk for CVD METHODS • Transformed the FA data to a logarithmic scale • Ran linear regression models for each biomarker as predicted by one of four FAs, a SNP, and their interaction • Each model included age and sex as covariates • All models were run through a GWAS pipeline • A p-value threshold of 5.0x10-8 was used to filter the results • Interaction plots were created • Biological interpretation of the results RESULTS • The interaction test generated novel loci when compared to the models that had no interaction variable • 33 significant interactions between FA and SNP were found to predict the biomarker levels • Of the eight biomarkers, seven of them corresponded with a significant interaction • Two examples of significant interactions are shown BM = 𝛽𝐹𝐴 log 𝐹𝐴 + 𝛽𝑆𝑁𝑃 𝑆𝑁𝑃 + 𝛽𝐼𝑁𝑇 (log 𝐹𝐴 ∗ 𝑆𝑁𝑃) BM = Biomarker Level 𝛽FA = Beta value for FA 𝛽SNP = Beta value for SNP 𝛽INT = Beta value for Interaction RESULTS Chromosome Biomarker # sig SNPs Genes Containing SNPs Previous Evidence10 Smallest SNP p-value (rs#:Fatty Acid) 14 CAM 7 CTD Cardiovascular disease risk factors 3.07x10-8 (rs4360849:Oleic Acid) 11 I-CAM 2 CTTN Urate levels in obese individuals 3.51x10-8 (rs117970650:Oleic Acid) 3 IL-6 3 FHIT Conotruncal Heart Defects 4.29x10-8 (rs6778312:Oleic Acid) 14 IL-6 1 TMEM179 None Found 2.13x10-8 (rs75442513:Oleic Acid) 16 MCP-1 1 HYDIN COPD-Related Biomarkers 2.49x10-8 (rs182312570:Oleic Acid) 2 OPG 1 CDCA7 Blood Pressure 4.26x10-8 (rs144194198: Arachidonic Acid) 4 p-Selectin 1 IRF2 Lewy Body Disease 2.1x10-9 (rs74960894: Eicosapentaenoic Acid) 16 p-Selectin 1 RP11 Bilirubin Levels 2.29x10-8 (rs138588207:Oleic Acid) 3 TNF 1 ULK4 Blood Pressure 1.17x10-8 (rs62256958:Oleic Acid) 19 TNF 6 PEX11G, ARHGEF18 Urate Levels 8.44x10-9 (rs10419652:Oleic Acid) Homozygous Dominant TNF PREDICTED BY OLEIC ACID AND SNP 3 Heterozygous 2.5 Homozygous Recessive 2 • Significance: 1.17x10-8 • TNF6 • Cytokine • Used to indicate risk for CVD • Gene: ULK47 RELATIVE TNF LEVEL • SNP: rs62256958 1.5 -2.4 1 0.5 0 -2.2 -2 -1.8 -1.6 -0.5 • Found on chromosome 3 • Known to be associated with various -1 levels of blood pressure • Little is known about this gene or how its variation affects blood pressure -1.4 -1.5 FATTY ACID PERCENT COMPOSITION (LOG SCALE) OPG PREDICTED BY ARACHIDONIC ACID AND SNP 2 1.5 • • • RELATIVE OPG LEVEL 1 0.5 0 -2.4 -2.2 -2 -1.8 -1.6 -1.4 Heterozygous Homozygous Recessive FATTY ACID PERCENT COMPOSITION (LOG SCALE) -1 -1.5 -2 SNP: rs144194198 OPG8 • Found in plaque buildup in arteries -0.5 Homozygous Dominant Significance: 4.26x10-8 • Used as a predictor for CVD • Gene: CDCA79 • Found on chromosome 4 • Also known to be associated with various levels of blood pressure NEXT STEPS • Run through the GWAS pipeline with the other 18 RBC FAs • Analyze data with more covariates • Environmental interactions • Demographic interactions • Apply this analysis to other studies like Women’s Health Initiative • Biological follow up of these genes via a wet lab REFERENCES 1. Human Soluble ICAM-1 Immunoassay. (2002). Retrieved from Framingham Heart Study: http://www.framinghamheartstudy.org/share/protocols/icam1_7s_protocol.pdf 2. Genetic Data. (n.d.). Retrieved from Framingham Heart Study: https://www.framinghamheartstudy.org/researchers/description-data/geneticdata.php 3. Michigan Imputation Server. (n.d.). Retrieved from University of Michigan: https://imputationserver.sph.umich.edu/index.html 4. Auton, A., Abecasis, G., et al. (2015, October 1). A global reference for human genetic variation. Nature, 526, 68-74. 5. Tintle NL, e. a. (2014). A genome-wide association study of saturated, mono- and polyunsaturated red blood cell fatty acids in the Framingham Heart Offspring Study. PLEFA, 94, 65-72. 6. Popa, C., et al. (2007, January 2). The role of TNF-α in chronic inflammatory conditions, intermediary metabolism, and cardiovascular risk. Journal of Lipid Research, 48, 751-762. 7. Levy, D., et al. (2009, June). Genome-wide association study of blood pressure and hypertension. Nat Genet, 41, 677-687. 8. Bjerre, M. (2013). Osteoprotegerin (OPG) as a Biomarker for Diabetic Cardiovascular Complications. SpringerPlus, 658. 9. He, J et. al. (2013). Genome-Wide Association Study Identifies 8 Novel Loci Associated with Blood Pressure Responses to Interventions in Han Chinese. Circ Cardiovasc Genet., 598-607. 10. GWAS Catalog. (2015). Retrieved from EMBL-EBI: https://www.ebi.ac.uk/gwas/ ACKNOWLEDGMENTS • Dr. Nathan Tintle • Dr. Caren Smith • Craig Disselkoen and Jason Westra