Causal modeling in a multi-omic setting: insights from GAW20
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- dc.contributor.author Auerbach, Jonathan
- dc.contributor.author Howey, Richard
- dc.contributor.author Jiang, Lai
- dc.contributor.author Justice, Anne
- dc.contributor.author Li, Liming
- dc.contributor.author Oualkacha, Karim
- dc.contributor.author Sayols, Sergi
- dc.contributor.author Aslibekyan, Stella W.
- dc.date.accessioned 2019-03-12T08:40:23Z
- dc.date.available 2019-03-12T08:40:23Z
- dc.date.issued 2018
- dc.description.abstract BACKGROUND: Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies. RESULTS: Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R2 measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations. CONCLUSIONS: The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes.
- dc.format.mimetype application/pdf
- dc.identifier.citation Auerbach J, Howey R, Jiang L, Justice A, Li L, Oualkacha K. et al. Causal modeling in a multi-omic setting: insights from GAW20. BMC Genet. 2018 Sep 17;19(Suppl 1):74. DOI: 10.1186/s12863-018-0645-4
- dc.identifier.doi http://dx.doi.org/10.1186/s12863-018-0645-4
- dc.identifier.issn 1471-2156
- dc.identifier.uri http://hdl.handle.net/10230/36797
- dc.language.iso eng
- dc.publisher BioMed Central
- dc.relation.ispartof BMC Genomics. 2018 Sep 17;19(Suppl 1):74
- dc.rights © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0. International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Bayesian networks
- dc.subject.keyword Causal inference
- dc.subject.keyword Epigenomics
- dc.subject.keyword Genomics
- dc.subject.keyword Mendelian randomization
- dc.subject.keyword Outliers
- dc.subject.keyword Structural equation modeling
- dc.subject.keyword Variable selection methods
- dc.title Causal modeling in a multi-omic setting: insights from GAW20
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion