Causal Inference Methods to Integrate Omics and Complex Traits.

Details

Serval ID
serval:BIB_FB7006C4092B
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Causal Inference Methods to Integrate Omics and Complex Traits.
Journal
Cold Spring Harbor perspectives in medicine
Author(s)
Porcu E., Sjaarda J., Lepik K., Carmeli C., Darrous L., Sulc J., Mounier N., Kutalik Z.
ISSN
2157-1422 (Electronic)
ISSN-L
2157-1422
Publication state
Published
Issued date
17/08/2020
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
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.
Pubmed
Create date
31/08/2020 16:36
Last modification date
20/01/2021 7:26
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