Originally published as Genetics Published Articles Ahead of Print on May 27, 2008.

Genetics, Vol. 179, 1089-1100, June 2008, Copyright © 2008
doi:10.1534/genetics.107.085167

Inferring Causal Phenotype Networks From Segregating Populations

Elias Chaibub Neto^*, Christine T. Ferrara^,, Alan D. Attie and Brian S. Yandell^*,,1

^* Department of Statistics, Biochemistry Department and Department of Horticulture, University of Wisconsin, Madison, Wisconsin 53706 and Sarah W. Stedman Nutrition and Metabolism Center and Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina 27704

¹ Corresponding author: Department of Statistics, University of Wisconsin, 1300 University Ave., MSC-1239, Madison, WI 53706.
E-mail: byandell{at}wisc.edu

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.

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Genetics 2008 179: NP. [Full Text]  

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