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.

Mendelian Randomization: Concepts and Scope

“…Several of the other papers in this collection elaborate on some of the novel methodological approaches, including multivariable MR and the use of MR for assessing mediation (Sanderson 2021), polygenic MR methods for assessing pleiotropy , as well as withinfamily MR methods (Hwang et al 2020). Other papers describe the application of the approach for extending clinical applicability and uncovering molecular mechanisms (Porcu et al 2021).…”

“…However, in contrast to germline genetic variation, -omic signatures are largely phenotypic, and are therefore subject to the same potential problems of confounding and reverse causation that afflict conventional epidemiology (Relton and Davey Smith 2010). MR is being increasingly applied to elucidate causality for a range of molecular data, including epigenetics, transcriptomics, gene expression, metabolomics, and proteomics (Porcu et al 2021). For this, approaches such as two-step, network, and multivariable MR are of particular utility for determining whether these markers lie on the causal pathway between risk factors and disease (Table 4).…”

Causal Inference with Genetic Data: Past, Present, and Future

“…Note that we define phenotypes broadly as including any individual characteristic other than genotypes, which includes all omics other than genomics. Methods covered in Porcu et al (2021) aim to query the potential of genetically informed methods in elucidating the role of metabolomics as modifiable risk factors for diseases. Such research questions build on largescale and growing data sets and cover much-needed computational tools for causal inference.…”

Quantifying mediation between omics layers and complex traits

“…Mapping genetic variants identified in GWAS analyses to biological processes is notoriously difficult [2]. However, systems genetics approaches that integrate multiple omics datasets as a way of leveraging GWAS summary data have proven successful in providing a more complete picture of the path from genotype to phenotype [82]. Here, we demonstrated that our multi-omics framework was able to attribute GWAS signals to biological pathways in loci harbouring multiple genes (e.g.…”