Correct spatial and temporal gene expression are essential for many developmental and physiological processes, and gene expression phenotypes are often under stabilizing selection. At the same time, there is extensive variation in DNA regulatory elements in natural populations. In humans, this variation has been associated with a range of diseases as well as individual variation in drug toxicity and efficacy. Predicting the functional consequences of variation in DNA regulatory elements is critical for biomedical research, but complicated. Gene expression is the result of complex interactions between transcription factors, DNA regulatory elements, and components of the basal transcriptional machinery. As a result, the functional consequence of any one change in a regulatory element can depend on what changes are found elsewhere within the same regulatory element. The goal of the proposed research is to understand the impacts that naturally occurring variation in DNA regulatory elements has on transcription factor binding and gene expression. In particular, this project focuses on quantifying the functional consequences of interactions between variants within regulatory elements to understand, in part, the extent to which these interactions may violate the assumptions of additivity that underlie many attempts to link phenotypic and genotypic variation (e.g. Genome-Wide Association Studies). The proposed research makes use of extensive information about combinatorial transcriptional regulation at a set of ~8,000 regulatory elements underlying the expression of mesodermal genes in Drosophila. I will take three complementary approaches to addressing the project goal. First, I will use of ChIP-Seq to quantify the contribution of natural genetic variation to transcription factor binding variation for a set of core mesodermal regulators Drosophila strains for which we have full genomic sequences and extensive expression data (Aim 1). Next, I will conduct population genetic analyses to understand how selection operates on functional regulatory variants and to identify potential interactions among variants within regulatory elements (Aim 2). Finally, I will use the results of Aim 1 and 2 to select candidate regulatory elements in which I will quantify the contributions of variants, and interactions between them, to gene expression variation using in vivo reporter assays. The results of my research will provide essential insights into the relationship between the sequence of regulatory elements and gene expression, improving our ability to predict the consequences of regulatory variation and our understanding of how regulatory DNA evolves within and between populations.

Public Health Relevance

Interactions between variants (mutations) within the regions of DNA controlling gene expression are often non-linear, confounding methods used by biomedical researchers for mapping changes in regulatory DNA to gene expression changes, as these methods generally ignore non-linear interactions. By investigating the functional consequences of natural variants, and interactions among them, in a well-studied set of DNA regulatory elements in the fruit fly Drosophila, I will gain important insights into the mechanisms that generate differences between individuals in the expression of critical genes that contribute to human disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32GM100635-01X1
Application #
8413141
Study Section
Special Emphasis Panel (ZRG1-F08-E (20))
Program Officer
Janes, Daniel E
Project Start
2012-09-05
Project End
2015-08-04
Budget Start
2012-09-05
Budget End
2013-09-04
Support Year
1
Fiscal Year
2012
Total Cost
$7,850
Indirect Cost
Name
European Molecular Biology Laboratory
Department
Type
DUNS #
321691735
City
Heidelberg
State
Country
Germany
Zip Code
69117
Zichner, Thomas; Garfield, David A; Rausch, Tobias et al. (2013) Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res 23:568-79