Mendelian randomization under the omnigenic architecture.
Lu WangBoran GaoYue FanFuzhong XueXiang ZhouPublished in: Briefings in bioinformatics (2022)
Mendelian randomization (MR) is a common analytic tool for exploring the causal relationship among complex traits. Existing MR methods require selecting a small set of single nucleotide polymorphisms (SNPs) to serve as instrument variables. However, selecting a small set of SNPs may not be ideal, as most complex traits have a polygenic or omnigenic architecture and are each influenced by thousands of SNPs. Here, motivated by the recent omnigenic hypothesis, we present an MR method that uses all genome-wide SNPs for causal inference. Our method uses summary statistics from genome-wide association studies as input, accommodates the commonly encountered horizontal pleiotropy effects and relies on a composite likelihood framework for scalable computation. We refer to our method as the omnigenic Mendelian randomization, or OMR. We examine the power and robustness of OMR through extensive simulations including those under various modeling misspecifications. We apply OMR to several real data applications, where we identify multiple complex traits that potentially causally influence coronary artery disease (CAD) and asthma. The identified new associations reveal important roles of blood lipids, blood pressure and immunity underlying CAD as well as important roles of immunity and obesity underlying asthma.
Keyphrases
- genome wide
- coronary artery disease
- dna methylation
- genome wide association
- blood pressure
- copy number
- chronic obstructive pulmonary disease
- contrast enhanced
- magnetic resonance
- lung function
- cardiovascular events
- type diabetes
- percutaneous coronary intervention
- weight loss
- gene expression
- insulin resistance
- allergic rhinitis
- coronary artery bypass grafting
- weight gain
- big data
- adipose tissue
- single cell
- magnetic resonance imaging
- physical activity
- acute coronary syndrome
- monte carlo
- case control
- data analysis