Causal modeling in a multi-omic setting: insights from GAW20

Auerbach, Jonathan; Howey, Richard; Jiang, Lai; Justice, Anne; Li, Liming; Oualkacha, Karim; Sayols, Sergi; Aslibekyan, Stella W.

doi:http://dx.doi.org/10.1186/s12863-018-0645-4

Causal modeling in a multi-omic setting: insights from GAW20

License

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

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.