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

Jonathan Auerbach, Richard Howey, Lai Jiang, Anne Justice, Liming Li, Karim Oualkacha, Sergi Sayols-Baixeras, Stella W. Aslibekyan
2018 BMC Genetics  
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.
doi:10.1186/s12863-018-0645-4 pmid:30255779 pmcid:PMC6157026 fatcat:ppow5xxwmvft3bfatkotkcrj74