Chaibub Neto Elias, Ferrara Christine T, Attie Alan D, Yandell Brian S
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA.
Genetics. 2008 Jun;179(2):1089-100. doi: 10.1534/genetics.107.085167. Epub 2008 May 27.
A major goal in the study of complex traits is to decipher the causal interrelationships among correlated phenotypes. Current methods mostly yield undirected networks that connect phenotypes without causal orientation. Some of these connections may be spurious due to partial correlation that is not causal. We show how to build causal direction into an undirected network of phenotypes by including causal QTL for each phenotype. We evaluate causal direction for each edge connecting two phenotypes, using a LOD score. This new approach can be applied to many different population structures, including inbred and outbred crosses as well as natural populations, and can accommodate feedback loops. We assess its performance in simulation studies and show that our method recovers network edges and infers causal direction correctly at a high rate. Finally, we illustrate our method with an example involving gene expression and metabolite traits from experimental crosses.
复杂性状研究的一个主要目标是破译相关表型之间的因果相互关系。目前的方法大多产生无向网络，这些网络连接表型但没有因果方向。由于非因果的部分相关性，其中一些连接可能是虚假的。我们展示了如何通过为每个表型纳入因果QTL，将因果方向纳入表型的无向网络中。我们使用LOD分数评估连接两个表型的每条边的因果方向。这种新方法可以应用于许多不同的群体结构，包括近交和远交杂交以及自然群体，并且可以容纳反馈回路。我们在模拟研究中评估了它的性能，结果表明我们的方法能够以很高的准确率恢复网络边并正确推断因果方向。最后，我们用一个涉及实验杂交中基因表达和代谢物性状的例子来说明我们的方法。
