Mapping IRAK4, PUS7L, and ANRIL genetic variation, DNA methylation, and expression in atherosclerotic plaques.

Project ID: AE_171212_GBASATEMUR_ZMALLAT.

Collaborators: Gemma Basatemur, Ziad Mallat.

Background

In the past decade genome-wide association studies (GWAS) have robustly linked common genetic variation to coronary artery disease (CAD), and identified 9p21 as a locus causally involved in CAD risk.

In this project we aim to map genetic variation at 9p21 (rs1333049) to IRAK4 DNA methylation (DNAm), and expression in atherosclerotic plaques.

We associate the known risk loci to plaque-derived DNAm. We will associate overall plaque expression with plaque phenotypes using the bulk RNAseq data.

Methods

General

We will use data from the Athero-Express Biobank Study. We have bulk RNAseq (n = 635 samples), genome-wide methylation (Illumina 450K) in n ± 600, as well as overlapping genetic data for ±2,100 individuals with extensive histological plaque characterisation. We will address the following questions:

• Gene expression correlated to characteristics of plaques?
• Is 9p21 associated to DNAm?
• Are DNA methylation and gene expression correlated?

Analyses

Modeling gene variation to plaque phenotypes

For the genetic analyses we will perform regression analyses adjusted for age, sex (where applicable), hospital and genetic principal components.

model 1: `phenotype ~ age + sex + Hospital + PC1 + PC2 + PC3 + PC4`

Plaque phenotypes (characteristics) are:

• calcification, coded Calc.bin no/minor vs. moderate/heavy staining
• collagen, coded Collagen.bin no/minor vs. moderate/heavy staining
• fat10, coded Fat.bin_10 no/<10% fat vs. >10% fat
• fat40, coded Fat.bin_40 no/<40% fat vs. >40% fat
• intraplaque hemorrhage, coded IPH.bin no vs. yes
• macrophages (CD68), coded macmean0 mean of computer-assisted calculation CD68⁺ region of interest
• smooth muscle cells (alpha-actin), coded smcmean0 mean of computer-assisted calculation SMA⁺ region of interest
• intraplaque vessel density (CD34), coded vessel_density manually counted CD34⁺ cells per 3-4 hotspots
• mast cells, coded Mast_cells_plaque manually counted mast cell tryptase⁺ cells (https://academic.oup.com/eurheartj/article/34/48/3699/484981)
• neutrophils (CD66b), coded neutrophils manually counted CD66b⁺ cells (https://pubmed.ncbi.nlm.nih.gov/20595650/)

Continuous variables were inverse-rank normal transformated, indicated by _rankNorm.

Modeling SNP to plaque gene expression and DNA methylation

For this molecular quantitative trait locus (molQTL) we performed regression analyses adjusted for covariates as follows.

model 1: `phenotype ~ SNP + Age + Sex + Gen. PC 1 + Gen. PC 2 + Gen. PC 3 + Gen. PC 4 + Hospital

Associate gene expression to plaque phenotypes

Associate bulk gene expression with plaque characteristics.

Associate gene expression to methylation

model 1: `phenotype ~ age + sex + Hospital`
model 2: `phenotype ~ age + sex + Hospital + BMI + GFR_MDRD + hypertension + diabetes + lipid-lowering drugs`

Where do I start?

You can load this project in RStudio by opening the file called 'AE_171212_9p21_IRAK4.Rproj'.

Project structure

File	Description	Usage
README.md	Description of project	Human editable
AE_171212_9p21_IRAK4.Rproj	Project file	Loads project
LICENSE	User permissions	Read only
.worcs	WORCS metadata YAML	Read only
renv.lock	Reproducible R environment	Read only
Results	Some results	Read only

Reproducibility

This project uses the Workflow for Open Reproducible Code in Science (WORCS) to ensure transparency and reproducibility. The workflow is designed to meet the principles of Open Science throughout a research project.

To learn how WORCS helps researchers meet the TOP-guidelines and FAIR principles, read the preprint at https://osf.io/zcvbs/

WORCS: Advice for authors

• To get started with worcs, see the setup vignette
• For detailed information about the steps of the WORCS workflow, see the workflow vignette

WORCS: Advice for readers

Please refer to the vignette on reproducing a WORCS project for step by step advice.

Acknowledgements

Dr Sander W. van der Laan is funded through grants from the Netherlands CardioVascular Research Initiative of the Netherlands Heart Foundation (CVON 2011/B019 and CVON 2017-20: Generating the best evidence-based pharmaceutical targets for atherosclerosis [GENIUS I&II]). We are thankful for the support of the ERA-CVD program ‘druggable-MI-targets’ (grant number: 01KL1802), the EU H2020 TO_AITION (grant number: 848146), and the Leducq Fondation ‘PlaqOmics’.

Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.

The framework was based on the WORCS package.

Changes log

_Version:_      v1.0.2</br>
_Last update:_  2021-05-06</br>
_Written by:_   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).

* v1.0.2 Added all relevant files. 
* v1.0.0 Initial version. 

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