PLoS Genet. 2014 Feb 6;10(2):e1004122. doi: 10.1371/journal.pgen.1004122. eCollection 2014 Feb.

Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.

Karczewski KJ¹, Snyder M², Altman RB³, Tatonetti NP⁴.

Author information

1: Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, California, United States of America ; Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
2: Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
3: Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America ; Department of Bioengineering, Stanford University School of Medicine, Stanford, California, United States of America.
4: Department of Biomedical Informatics, Department of Systems Biology, and Department of Medicine, Columbia University, New York, New York, United States of America.

Abstract

Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.