Causal Inference Methods to Integrate Omics and Complex Traits
- Eleonora Porcu1,2,3,6,
- Jennifer Sjaarda2,3,6,
- Kaido Lepik3,4,
- Cristian Carmeli3,
- Liza Darrous2,3,
- Jonathan Sulc2,3,
- Ninon Mounier2,3 and
- Zoltán Kutalik2,3,5
- 1Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland
- 2Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- 3University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
- 4Institute of Computer Science, University of Tartu, Tartu 50409, Estonia
- 5Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX2 5AX, United Kingdom
- Correspondence: zoltan.kutalik{at}unil.ch
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.
