TY - JOUR T1 - What Can Causal Networks Tell Us about Metabolic Pathways? A1 - Blair, Rachael Hageman A1 - Kliebenstein, Daniel J. A1 - Churchill, Gary A. Y1 - 2012/04/05 N2 - Author Summary High-throughput profiling data are pervasive in modern genetic studies. The large-scale nature of the data can make interpretation challenging. Methods that estimate networks or graphs have become popular tools for proposing causal relationships among traits. However, it is not obvious that these methods are able to capture causal biological mechanisms. Here we address the power and limitations of causal inference methods in biological systems. We examine metabolic data from simulation and from a well-characterized metabolic pathway in plants. We show that variation has to propagate through the pathway for reliable network inference. While it is possible for causal inference methods to recover the ordering of the biological pathway, it should not be expected. Causal relationships create subtle patterns in correlation, which may be dominated by other biological factors that do not reflect the ordering of the underlying pathway. Our results shape expectations about these methods and explain some of the successes and failures of causal graphical models for network inference. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 4 UR - https://doi.org/10.1371/journal.pcbi.1002458 SP - e1002458 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002458 ER -