Causal Inference Methods to Integrate Omics and Complex Traits

Abstract

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Figure 1.

Multivariable Mendelian randomization (MR) applied to expression traits. (eQTL) expression quantitative trait loci.

Figure 2.

A key violation of the InSIDE assumption of Mendelian randomization. An unmeasured heritable confounding factor V and separate direct (π_x) and indirect (q_xπ_v) genetic effect for X for a set of single-nucleotide polymorphisms (SNPs) G_x and G_v. The causal effects of V on X, V on Y, and X on Y are denoted by q_x, q_y, and α, respectively.

Figure 3.

QQ plot of the correlation P-values. Observational correlations were calculated between low-density lipoprotein (LDL) levels and whole blood expression levels of causal genes for LDL (by transcriptome-wide Mendelian randomization [TWMR]) in the EGCUT study (in red). Correlations were also calculated between LDL levels and a set of random genes (in blue).