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

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

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 comp...

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Bibliographic Details
Main Authors: Jonathan Auerbach, Richard Howey, Lai Jiang, Anne Justice, Liming Li, Karim Oualkacha, Sergi Sayols-Baixeras, Stella W. Aslibekyan
Format: Article
Language:English
Published: BMC 2018-09-01
Series:BMC Genetics
Subjects:
Genomics
Epigenomics
Causal inference
Mendelian randomization
Bayesian networks
Structural equation modeling
Online Access:http://link.springer.com/article/10.1186/s12863-018-0645-4

Internet

http://link.springer.com/article/10.1186/s12863-018-0645-4

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