Most complex disorders are influenced by a mix genetic and environmental factors. Environmental factors can cause changes to gene expression in the body through epigenetic mechanisms that control gene activity. The Gregg lab has discovered novel epigenetic effects in the brain and in other tissues of the body that differentially impact the expression of maternally versus paternally inherited gene copies. We have defined three major types of these effects. Canonical genomic imprinting involves complete silencing of the maternal or paternal allele. Noncanonical imprinting involves partial or cell-type specific silencing of one parent’s allele. These two mechanisms constitute heritable maternal and paternal influences on gene expression in offspring. The third type of epigenetic allelic effect we uncovered is referred to different allele expression and involves differences in the expression of the two alleles, but not consistent silencing of one parent’s allele over the other.

Since most genetic mutations are heterozygous, meaning only one gene copy is impacted, a potential implication of our findings is that epigenetic mechanisms may silence one gene copy such that some cells in the body only express a mutated copy and the healthy backup copy is silent. We are testing the hypothesis that human brain disorders and other diseases arise, in part, because of the existence of cells that preferentially express a mutated maternal or paternal gene copy. Further, we are investigating the normal biological function of the these epigenetic effects and applications for disease diagnostics and therapy.

Computational Ethology: Deconstructing Complex Patterns of Behavior

The analysis of behavior in mammalian model organisms, such as mice, is fundamental to understanding the molecular and neural mechanisms that underlie brain function.  These assays are also used in pre-clinical studies to test the efficacy of drug treatments.  However, current behavioral paradigms are limited in that the investigator must have a predetermined hypothesis regarding the phenotypes that they are interested in evaluating in their studies.  In addition, for several paradigms the ecological relevance and conservation of the assayed phenotype is unclear.  Other fields, such as the genomics field, have developed methods that allow for unbiased screens to facilitate new discoveries.  Here, we are developing novel methods for unbiased behavioral phenotyping that involve automated video-tracking and machine learning based methods to deconstruct complex patterns of ecologically relevant and highly conserved behaviors. Currently, we have developed new methods to screen hundreds of features of behavior during foraging and applied this method to study parental and genetic mechanisms regulating behavioral development. This work is opening a new field that we refer to as computational ethology, and it is expected to improve our ability to study the mechanisms that shape behavioral phenotypes and discover new disease mechanisms and therapeutics.

Big Data Analysis To Uncover Mechanisms Regulating Behavioral Development and Disease Susceptibility

Breakthroughs in the biological and biomedical sciences arise in large part due to the emergence of new technologies.  One of the most transformative aspects of modern science is the ability to gather, store and analyze massive amounts of data.  The Gregg lab is actively developing novel software and other technologies to analyze and visualize large scale datasets.  One of our major interests is in the development of new approaches to study complex datasets associated with gene expression data, genome data and animal behavior data.  In addition to the computer scientists working in the Gregg Lab on these projects, we collaborate with exceptional scientists at the Scientific Computing and Imaging Institute (SCI) and Center for High Performance Computing (CHPC) at the University of Utah.  These projects have lead to novel methods to uncover genomic programs that regulate behavioral development, as well as disease resistance to cancer, stroke, diabetes, autism and other disorders.





Gregg C, Zhang J, Weissbourd B, Luo S, Schroth GP, Haig D, Dulac C (2010). High-resolution analysis of parent-of- origin allelic expression in the mouse brain. Science, 329(5992), 643-8.

Gregg C, Zhang J, Butler JE, Haig D, Dulac C (2010). Sex-specific parent-of-origin allelic expression in the mouse brain. Science, 329(5992), 682-5.

Gregg C.  (2010) Eppendorf winner. Parental Control Over The Brain. Science. 330(6005):770-1.

Bonthuis P, Huang WC, Statcher Horndli C, Ferris E, Cheng T, Gregg C (2015). Noncanonical genomic imprinting effects in offspring. Cell Reports, 12(6), 979-91.

Huang WC., Ferris E., Cheng T, Stacher Horndli C., Tamminga C., Wagner J.D., Christian J., Gregg C. Developmentally-regulated differential allele expression effects shape genetic architecture at the cellular level in the mouse and primate brain. (under revision)


Stacher Horndli CN, Wong E, Rhodes AN, Ferris E, Fletcher PT, Gregg C. An Ethomics Approach to Study Behavioral Development and Foraging Reveals Age-Dependent Parental and Genetic Effects. (under review)

Stacher Horndli CN, Wong E, Palande S, Fletcher T, Wang B, Gregg C. Parental and Genetic Influences on Offspring Personality Architecture and Economic Strategies Revealed by a Computational Approach to Ethology. (In Preparation)


McKenna S, Meyer M, Gregg C, Gerber S (2015). s-CorrPlot: An Interactive Scatterplot for Exploring Correlation. J Comput Graph Stat, 25(2,2016).

...four other papers in preparation that will be posted here soon!