Download Genome-Wide Interaction Study of Red

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.
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•
•
•
•
•
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